capillary electrophoresis instrumentation and applications ... · capillary electrophoresis...

CAPILLARY ELECTROPHORESIS INSTRUMENTATION AND APPLICATIONS

FOR THE ANALYSIS OF NEURAL SYSTEMS

BY

CHRISTOPHER A. DAILEY

DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Chemistry

in the Graduate College of the

University of Illinois at Urbana-Champaign, 2012

Urbana, Illinois

Doctoral Committee:

Professor Jonathan V. Sweedler, Chair

Professor Alexander Scheeline

Assistant Professor Richard H. Perry

Professor Robert B. Gennis

ii

Abstract

Capillary electrophoresis (CE) separations for the analysis of the chemically-

complex environment of the nervous system have been demonstrated to be an effective

tool in neuroscience. The inherent scaling laws of CE allow for the analysis of low-

volume samples at high separation efficiencies which provide a platform for the study of

neural systems. Here, CE systems using laser-induced, native-fluorescence (LINF)

detection are described along with its applications for neural analysis across a variety of

metazoan life. Selective detection of analytes after separation allows for the expedited

analysis of complex biological systems by substantially decreasing the electropherogram

complexity and reducing background interference. Many tryptophan and tyrosine

metabolites present in biological systems are amenable to LINF detection.

An enhanced wavelength-resolved CE-LINF instrument was developed in an

effort to take advantage of improvements in technology and electronics to increase

sensitivity and versatility over prior, similar instruments our lab has developed. The

enhanced instrument uses a frequency-doubled, argon-ion laser operating in the deep

ultraviolet (UV) region (either 264 or 229 nm) for efficient excitation of indolamines and

catecholamines as well as an UV enhanced charge-coupled device for wavelength-

resolved detection. Using 264 nm excitation detection limits were 1 nM for serotonin

and 36 nM for dopamine, approximately 5-fold improvement for both as compared to

previous systems. Additionally, the use of 229 nm excitation improved limits of

detection for tyrosine metabolites by a factor of 2-3 over excitation at 264 nm.

Our enhanced, wavelength-resolved CE-LINF system has been used to investigate

a variety of model systems. Using the mouse as a mammalian model, we have

iii

demonstrated a significant, non-neuronal source of serotonin to the hippocampus from

nearby mast cells, a major component of the immune system. Also, using a genetically

modified strain of Drosophila melanogaster, we have developed a single-neuron

sampling method which stabilizes the neuron by adding a temperature-gated arrest of

neurosecretory activity. This strain resulted in improved reliability for the detection of

serotonin in well-characterized neuron as well as the detection of tryptamine which had

not been previously detected. Additionally, we have investigated the absence of

serotonin and many other classical neurotransmitters in the basal phyla Ctenophore which

compliments genomic studies by our collaborators indicating a lack an enzymatic

pathway. While serotonin is an important and well-studied tryptophan metabolite, we

have begun expanding our system to detect metabolites in the kynurenine pathway. In

fact, this pathway is responsible for the majority of tryptophan metabolism in mammalian

physiology and results in the neuroactive compounds kynurenine and kynurenic acid

which are natively fluorescent.

An automated version of a CE-LINF instrument was optimized and evaluated for

the quantitation of both tryptophan and tyrosine metabolites. This system uses a metal-

vapor, He-Ag laser with 224 nm output for excitation and a tunable emission detection

system using a spectrometer and photomultiplier tube, all relatively low-cost components.

The system demonstrated excellent linearity over several orders of magnitude of

concentration and intraday precision of 1–11% relative standard deviation. Limits of

detection ranged from 4 to 30 nmol/L for serotonin and tyrosine, respectively. Extracts

from mammalian brain stem were analyzed for serotonin and tryptophan with an intraday

precision of less than 10%.

iv

Acknowledgements

My graduate school experience has been influenced by numerous people in many

different ways. These acknowledgements are in no way assumed to be completely

inclusive as there is not enough space. This accomplishment would not have been

possible without my research advisor, Prof. Jonathan Sweedler, whose support, advise,

and guidance has shaped both my research pursuits and personal growth to become a

more independent scientist. I would also like to thank my committee members, Prof.

Alexander Scheeline, Prof. Richard Perry, and Prof. Robert Gennis whose advise, input

and general advise have been helpful in completing this work.

I would like to thank some specific group members both past and present (in no

particular order) for their input and support throughout my time in the Sweedler Group:

Nobutoshi Ota, Callie Croushore, Kasia Cudzilo, Ann Knolhoff, Bo Young Kim,

Stanislav Rubakhin, Elana Romanova, Eric Lanni, Nobitoshi Ota, Jordan Aerts, and

Suzanne Neupert. Thank you all for providing a fun and accepting group to work with

over the years, it would not have been the same without you.

A special thanks to Julie Sides who does far more than just make the Analytical

Area run smoothly. She has been an invaluable giver of advice and encouragement.

Without a doubt Julie is one of the most selfless individuals I have ever had the pleasure

of knowing and am a better person for it. Without the help of Stephanie Baker the

Sweedler group would not be able to produce the amazing publications that it does. Her

attention to detail is astounding and brings a level of refinement that has come to be

known and expected from the Sweedler group. In addition to these outstanding

v

professional contributions, both Julie and Stephanie have been great friends and sounding

boards over the years and I owe them both a debt of gratitude.

My family and friends have provided great support these last 5 years. To my

parents, I would like to thank you for always encouraging and believing in me. Without

the independence, perseverance, and strength of character they taught me I wouldn’t have

been able to do any of this. To Derek Moore, you taught me a lot about computers and

programming that I put to good use during my graduate research. More importantly, your

support, patience, and love has made my life better in more ways than I can count and

you have brought more balance than I ever thought possible. A special thanks to Ravage

and Max who always gave me a smile when it was needed.

vi

Table of Contents

Chapter 1 - Introduction ......................................................................................................... 1

1.1 Research Motivation ................................................................................................. 1

1.2 Thesis Overview ........................................................................................................ 2

1.3 Instrumentation ........................................................................................................ 3

1.3.1 Capillary Electrophoresis ............................................................................... 3

1.3.2 Native Fluorescence Detection ...................................................................... 5

1.4 Tryptophan Metabolism in the Neural Systems ....................................................... 7

1.5 Biological Models ...................................................................................................... 9

1.5.1 Mouse ............................................................................................................ 9

1.5.2 Drosophila .................................................................................................... 10

1.5.3 Ctenophore .................................................................................................. 11

1.6 Figure ...................................................................................................................... 12

1.7 References............................................................................................................... 13

Chapter 2 - Improved Capillary Electrophoresis Instrumentation using Wavelength-Resolved

Laser-Induced Native Fluorescence Detection .................................................... 21

2.1 Introduction ............................................................................................................ 21

2.2 Experimental ........................................................................................................... 25

2.2.1 Chemicals and Solutions .............................................................................. 25

2.2.2 Instrument Design ....................................................................................... 26

2.2.3 Instrument Control and Data Processing .................................................... 27

2.2.4 Electrophoresis ............................................................................................ 28

2.2.5 Data Analysis ................................................................................................ 29

2.3 Tissue Extraction ..................................................................................................... 29

2.4 Results and Discussion ............................................................................................ 29

2.4.1 Instrument Design ....................................................................................... 30

2.4.2 Performance ................................................................................................ 31

2.5 Conclusions ............................................................................................................. 33

2.6 Future Directions..................................................................................................... 34

2.7 Tables and Figures ................................................................................................... 36

2.8 References............................................................................................................... 40

vii

Chapter 3 - Automated Method for Analysis of Tryptophan and Tyrosine Metabolites Using

Capillary Electrophoresis with Native Fluorescence Detection ............................ 46

3.1 Introduction ............................................................................................................ 46

3.2 Experimental ........................................................................................................... 49

3.2.1 Chemicals, Solutions, and Materials ............................................................ 49

3.2.2 Preparation of Standards ............................................................................. 50

3.2.3 Tissue Extraction and Quantitation ............................................................. 50

3.2.4 CE Separation Conditions ............................................................................ 51

3.2.5 Automated CE-LINF System ......................................................................... 51

3.2.6 CE-WR-LINF System ..................................................................................... 52

3.2.7 Automated System Optimization ................................................................ 53

3.2.8 Data Processing and Analysis ...................................................................... 54

3.2.9 Evaluation of Analytical Performance ......................................................... 54


3.3.1 Design and Application ................................................................................ 55

3.3.2 System Performance.................................................................................... 57

3.3.3 Analysis of Mammalian CNS Tissue Extracts ............................................... 58

3.4 Conclusions ............................................................................................................. 60

3.5 Tables and Figures .................................................................................................. 61

3.6 References............................................................................................................... 69

Chapter 4 - Serotonin of Mast Cell Origin Contributes to Hippocampal Function ................... 73

4.1 Introduction ............................................................................................................ 73

4.2 Experimental ........................................................................................................... 77

4.2.1 Mast Cell Deficient Mouse Model ............................................................... 77

4.2.2 Chemicals ..................................................................................................... 77

4.2.3 Standards and Solutions .............................................................................. 77

4.2.4 Hippocampal Tissue Preparation ................................................................. 78

4.2.5 CE-LINF Analysis of Hippocampal Tissue ..................................................... 78

4.2.6 Data Analysis ................................................................................................ 79


4.3.1 Serotonin Results ......................................................................................... 80

4.3.2 Tyrosine Results ........................................................................................... 82

4.3.3 Chemical Analysis in Support of Behavioral and Physiological Results ....... 82

viii

4.4 Conclusions ............................................................................................................. 83

4.5 Future Directions..................................................................................................... 84

4.6 Figures ..................................................................................................................... 85

4.7 References............................................................................................................... 89

Chapter 5 – Detection of Kynurenine and Kynurenic Acid Using Wavelength-Resolved Capillary

Electrophoresis ................................................................................................. 93

5.1 Introduction ............................................................................................................ 93

5.2 Experimental ........................................................................................................... 96

5.2.1 Chemicals, Solutions, and Materials ............................................................ 96

5.2.2 Absorption Spectra ...................................................................................... 96

5.2.3 Capillary Electrophoresis Analysis ............................................................... 97

5.2.4 Modifications for Non-Aqueous Solvents ................................................... 97


5.4 Conclusions ........................................................................................................... 100

5.5 Future Directions................................................................................................... 100

5.6 Tables and Figures ................................................................................................ 102

5.7 References............................................................................................................. 107

Chapter 6 - Investigation of Basal Phyla for Presence of Classical Signaling Molecules ......... 112

6.1 Introduction .......................................................................................................... 112

6.2 Experimental ........................................................................................................ 115

6.2.1 Chemicals and Materials............................................................................ 115

6.2.2 Standards and Solutions ............................................................................ 116

6.2.3 Animal Collection and Species ................................................................... 116

6.2.4 Sample Preparation ................................................................................... 117

6.2.4.1 Dissection .................................................................................... 117

6.2.4.2 Tissue Extraction ......................................................................... 117

6.2.5 Analysis Methods ....................................................................................... 118

6.2.5.1 Capillary Electrophoresis – Laser-Induced Native Fluorescence 118

6.2.5.2 Capillary Electrophoresis – Mass Spectrometry ......................... 118

6.3 Results and Discussion .......................................................................................... 119

6.4 Conclusions ........................................................................................................... 120


6.6 Tables and Figures ................................................................................................. 122

ix

6.7 References............................................................................................................. 128

Chapter 7 - Single Neuron Analysis from Drosophila Melanogaster ..................................... 131

7.1 Introduction .......................................................................................................... 131

7.2. Experimental ......................................................................................................... 134

7.2.1 Chemicals and Solutions ............................................................................ 134

7.2.2 Capillary Electrophoresis – Laser-Induced Native Fluorescence Analysis . 135

7.2.3 Fly Stocks and Neuron Isolation ................................................................ 135

7.3 Results and Discussion .......................................................................................... 136

7.3.1 GFP Expressing Mutant .............................................................................. 137

7.3.2 Shibire Mutant ........................................................................................... 137

7.4 Conclusions ........................................................................................................... 138


7.6 Tables and Figures ................................................................................................. 141

7.7 References............................................................................................................. 147

1

Chapter 1

Introduction

1.1 Research Motivation

The brain is responsible for an astonishingly wide range of activities and

behaviors from basic, autonomic functions, to our individual personalities. Its

complexity, with billions of neurons and trillions of synapses in an ever-changing

configuration, makes the brain one of the most difficult mysteries to unravel, even within

simple organisms. Investigations of the electrical and chemical signaling pathways of the

nervous system are important for our understanding of both normal functioning and

pathologic states. Tissue and cell specific levels of signaling molecules, their rates of

synthesis, and dietary availability of precursors have been found to influence neural

functioning and are often correlated with behavior.1-4

Complex sample matrices, volume

and mass limited sampling methods, and dynamic distribution create interesting

analytical challenges for chemical analysis. It is therefore critical to develop and further

refine techniques for the interrogation of the vast number of chemical species involved in

signaling, maintenance, and development of the central nervous system (CNS).

The Sweedler research group has a long history of developing instrumentation

and methods for analyzing the chemical environment of neural systems at increasingly

higher spatial, chemical, and temporal sensitivities using a variety of technologies. These

methods have included microcoil NMR,5 techniques for single-neuron analysis,

6-10 and,

the focus of this dissertation, capillary electrophoresis (CE) using laser-induced native

fluorescence detection (LINF).7, 11-16

These CE-LINF systems have been used to detect

and identify new serotonin catabolism pathways,17-18

assay single neurons from Aplysia

2

californica,7 and measure serotonin from mast cells in the hippocampus.

19 Progress in

electronics and technology provides the potential to build updated and enhanced CE-

LINF instrumentation with improved performance including lower detection limits and

higher throughput.

The overarching goal of this dissertation has been to create an enhanced capillary

electrophoresis instrument with wavelength-resolved, laser-induced native fluorescence

detection and apply it to the investigation of model neural systems. These investigations

include the analysis of well-characterized invertebrate neurons, studying the role of mast

cells in serotonin signaling within the mammalian hippocampus, and unusual tryptophan

metabolic pathways.

1.2 Thesis Overview

The remaining portion of Chapter 1 provides short overviews/reviews of the

major topics presented, ranging from the fundamentals of CE instrumentation, to the

specific pathways in the nervous system, to the biological models used during our

investigations of neural systems. This background allows the specific development

efforts and investigations to be placed into a broader context. Chapter 2 describes the

design, development, and of the characterization of the wavelength-resolved CE-LINF

instrumentation with 229 and 264 nm excitation wavelengths. Chapter 3 covers the use

of native fluorescence detection with an automated capillary electrophoresis system for

the detection and quantitation of small-molecule metabolites of tryptophan and tyrosine

in collaboration with Beckman Coulter Inc., and will hopefully result in a commercially

available instrument that may popularize native fluorescence detection in capillary

3

electrophoresis. Chapter 4 describes new investigations of a non-neuronal source of

serotonin in the mammalian brain which appears to contribute significant amounts of

serotonin to the hippocampus. Chapter 5 describes a new application of CE-LINF

instrumentation; more specifically, the quantitation of kynurenine-related metabolites is

highlighted. Chapter 6 describes the analysis to determine if serotonin or other classical

neural signaling molecules are present in the evolutionarily primitive ctenophore,

Pleurobrachia. This model represents one of the simplest animal groups with a nervous

system and aids in our understanding of neuronal cell-to-cell signaling through evolution.

Chapter 7 details the use of genetically modified Drosophila melanogaster with a

blocked neurosecretory system to interrogate single neurons from the well-characterized

genetic model. Each chapter contains a “Future Directions” section that includes

suggestions for the future of the research and may act as a guide for additional follow-up

studies.

1.3 Instrumentation

1.3.1 Capillary Electrophoresis

Electrophoresis separates charged species based on differential migration rates

within an electric field. Electrophoretic separations date to the pioneering work of

Tiselius in the 1930s, for which he was awarded a Nobel Prize in 1948.20

In 1981

Jorgenson and Lukacs reported an electrophoretic separation technique within open,

fused-silica capillaries rather than the typical application of electrophoresis to protein

separation within gel slabs; a remarkable aspect of their work was the outstanding

separation efficiency and fast separation times.21-24

The invention and development of

4

the open-tublar, fused-silica capillary made robust and flexible by external coatings has

led to improved separations in gas chromatography, capillary liquid chromatography, and

the development of practical CE systems over the past three decades. Separation

efficiencies in CE are directly proportional to the applied voltage (typically 20-30 kV),

equation 1.1 where µ is the mobility of a species in an electric field, V is the applied

voltage, and D is the diffusion coefficient.

Equation 1.1

The flow profile in CE is plug-like rather than parabolic as in traditional chromatography

which uses pressure-driven flow. This profile substantially reduces peak broadening and

is one of the major reasons separation efficiencies can exceed 106 theoretical plates. On-

line sample concentration techniques are another advantage in CE by improving the

already impressive separation efficiencies by decreasing the length of analyte bands

within the capillary.25

One technique, field-amplified stacking, occurs when the injected

sample is less conductive than the background electrolyte, creating high, local electric

fields concentrating the charged analytes into narrow bands at the boundaries of the

injected sample plug.26

This allows for relatively large sample volumes (increased

analyte abundance) to be injected while maintaining the high resolution with narrow

analyte bands. The methods described throughout this work take advantage of field-

amplified stacking to improve detection limits and increase separation efficiencies.

The application of CE separations to neuroscience has been particularly useful

due to the inherently high separation efficiencies and low-volume sample requirements

which have been recently reviewed by Lapainis et al.27-28

With sampling techniques

requiring volumes in the nanoliter regime, varying levels of cellular and chemical

5

organization can be investigated within the CNS, ranging from entire brain regions, to

single cells, and even to subcellular organelles.

A number of different detection methods can be coupled to CE separations for

analysis — electrochemical (EC),29

UV absorbance,23

laser-induced fluorescence

(LIF),30-31

and mass spectrometry32

depending on the specific question to be investigated

and the chemical species of interest. The majority of the work reported within this

dissertation concerns LINF; this detection mode is discussed in the next section of this

chapter.

1.3.2 Native Fluorescence Detection

A subset of biomolecules derived from the aromatic amino acids tryptophan and

tyrosine exhibit intrinsic fluorescence upon excitation in the deep UV (200-300 nm),

accessing S0 S1 or S0 S2 transitions.33

Tryptophan, tyrosine, their metabolites, and

the peptides and proteins containing these residues have fluorescence quantum yields

ranging from 0.13 - 0.28.34-35

The typical fluorescence emission for these species is in the

range of 300-500 nm. With both excitation and emission wavelengths primarily in the

UV, significant constraints are placed on excitation and detection equipment and is the

topic for the remainder of this section. For most recent review of the state of the art of

LINF techniques see Gooijer et al.36

Early CE-LINF instrumentation used a frequency-doubled, argon-ion laser as the

excitation source (257, 305, 275 nm).11, 37-38

Currently, the major detraction of LINF as a

more common detection method is the limited availability and high price of deep UV

excitation sources that are both space and economically efficient. Traditionally, these

6

have been limited to large-frame, frequency-doubled, gas-ion lasers and frequency-

quadrupled Nd:YAG lasers; both are large, high-cost lasers. In recent years, the

advancements in light emitting diode (LED) technology to access higher energy

wavelengths make them a viable deep UV excitation source.39

With relatively low

purchase and operating costs, LEDs are poised to develop cost-effective instrumentation

that could make the technique more widely accessible. In addition to LEDs, hollow-

cathode, metal-vapor lasers have been used in several LINF detection systems which

access wavelengths in the deep UV affordably. Currently, the two reported

configurations for these lasers are He-Ag16

and Ne-Cu40

with outputs at 224.3 and 248.6

nm respectively. Both configurations have excellent power and beam profiles for LINF

applications, but are pulsed with durations of hundreds of microseconds and a typical

maximum repetition rate of 5 Hz. These lasers have been effective, but higher duty

cycles would improve their performance.

While many instruments have been designed with single-channel detection, LINF

can be used as an information-rich method when using multichannel detection schemes.

The use of spectral information for the identification of analytes based on fluorescence

emission in addition to migration time increases the confidence of peak assignments.

The earliest report of multichannel detection used in CE was by Kobayashi et al. using a

photodiode array41

and was shortly followed by the development of instruments using

charge coupled devices (CCDs).42-43

Multichannel detection has been used for DNA

sequencing,44

single-neuron analysis,7 environmental monitoring,

45 and discovery of new

serotonin metabolites,46

demonstrating the value and versatility of the detection method

7

even though it only accesses a small subset of molecules that are fluorescent using deep

UV excitation.

1.4 Tryptophan Metabolism in Neural Systems

Early life used the indole ring of tryptophan to capture photons during the

photosynthesis process.47

While tryptophan is routinely synthesized in plants, animals

must consume it in their diet, resulting in a limited supply. This limited source has likely

led to it being highly regulated throughout animal physiology. In addition to its use in

protein and peptide synthesis, a number of major neurotransmitters and neuromodulators

are derived from tryptophan metabolism. The three major metabolic pathways of

tryptophan are outlined in Fig. 1-1: serotonin, kynurenine, and tryptamine.

The metabolism of tryptophan into serotonin is a well-conserved pathway

originating in unicellular organisms around 3 billion years ago.48

Interestingly, serotonin

is present at higher levels in plants than animals.49

The early appearance of serotonin in

the evolution of life has led to its incorporation into many critical processes. Since the

identification of serotonin in 1952 it has become the target of extensive study and a

multi-billion dollar pharmaceutical target.50

Although serotonin is well-known for its

role in the CNS, its original isolation was from enterochromaffin cells in the enteric

nervous system where it is responsible for smooth muscle contractions.51

In fact,

serotonin plays a large role in the enteric nervous system which contains an estimated

95% of the human bodies total serotonin content with only ~5% present in CNS.52

Serotonin signaling is critical to homeostasis, acting as both a neurotransmitter and

trophic factor. Serotonin signaling regulates neurogenesis,53-54

mood,55

body

8

temperature,56

appetite,3 memory, and learning.

57 Metabolism of serotonin into

melatonin within the pituitary is implicated in regulating circadian rhythm. Disruption of

serotonin signaling results in numerous disease states.4 Dysfunction of serotonin

signaling is involved in depression,58

anxiety,59

and many neurodegenerative diseases.60-

61 With such critical and diverse roles it is important to continue investigating the

serotonergic system in the CNS as well as other tryptophan metabolites.

Although serotonin is the most well studied tryptophan metabolite it represents

only a small fraction of all tryptophan metabolism, more than 90% of tryptophan goes

through the kynurenine pathway (KP). The metabolism of tryptophan into the KP within

the brain begins with tryptophan 2,3-dioxygenase (TDO) in the liver or indoleamine 2,3-

dioxygenase (IDO) in rest of the body.62-64

Pro-inflammatory cytokines can regulate this

pathway by inducing or suppressing expression of IDO.65

As these cytokines are

regulated by the immune system signaling, IDO expression is largely controlled by the

immune system, creating a link between the biochemical content of the brain and the

activity of the immune system.66-67

The downstream metabolites in the KP include the

neuroactive compounds kynurenine, kynurenic acid, and quinolicic acid. These

metabolites have both neuroprotective and neurotoxic actions on neurons.63

Kynurenic

acid demonstrates neuroprotective properties and is produced from kynurenine by the

enzyme kynurenine aminotransferase (KAT) preferentially localized in glial cells.68

Quinolinic acid is an NMDA receptor agonist and can be toxic to neurons. The

relationship between specific KP metabolites of the CNS are reviewed by Stone.69

KP

metabolite levels are linked to diseases including Huntington’s, Alzheimer’s, and

multiple sclerosis as well as chronic infections such as Lyme disease, malaria, and AIDS.

9

The impact of KP metabolites on the CNS has created interest in developing

pharmaceutical-based therapies creating a demand for measure techniques for these

metabolites.69-71

1.5 Biological Models

The Sweedler group uses a reductionist approach in the investigation of neural

systems by employing several model systems, each with attributes amenable to the

particular study. For example, Aplysia californica are commonly used because of a

relatively simple nervous system (~10,000 CNS neurons) which is physiologically well

defined with large, individual neurons — making it an excellent model for method and

instrument development.72

Investigations included in this dissertation have used mice,

ctenophores, and drosophila as model species; their general characteristics and specific

genetic variants are discussed below.

1.5.1 Mouse

The mouse is a classic model system for mammalian biology and is often used as

a corollary for human physiology.73

Genetic manipulation creating both transgenic and

knockout strains allow for investigations of specific genes, and in some cases replication

of human diseases. Chapter 4 uses a strain, C57BL/6-KitW-sh/W-sh

(Wsh

/ Wsh

), devoid of

mast cells due to a naturally occurring mutation in the white spotting locus, suppressing

the c-kit receptor expression preventing differentiation of mast cells from their progenitor

cells.74

This strain of mouse has been used extensively as a method to study mast cell

biology in vivo.75

Interestingly, mast cells are known to cross the blood-brain barrier and

10

mature in areas of the brain for several mammalian species including the rat, mouse,

dove, vole, and human, but their exact function in the CNS has undergone limited

investigation.76-79

Behavioral studies show increased levels of anxiety and profound

deficits in hippocampal learning and spatial memory in this mast cell deficient strain.19, 80

1.5.2 Drosophila

The genome of drosophila was one of the first sequenced and with little genetic

redundancy specific genes can be linked to behavior and biochemical production.81-82

Neurotransmitters have been linked to specific behaviors and functions such as

octopamine in aggression,83

dopamine in both arousal84

and olfactory memories,85

and

tyramine for addiction to cocaine.86

Numerous techniques have been developed for the

genetic manipulation of drosophila, and combined with their short reproduction cycle,

allows for the breeding of genetic variants quickly and at low cost, particularly using the

UAS/Gal4 system.87

One difficulty in the analysis of the drosophila nervous system is its

small size. The total brain is only ~5 nL in volume and individual neurons are between

5-10 µm in diameter. The genetic models used in the work described in chapter 7

contained two genetic variants within the same fly to assist in single-cell analysis - GFP

co-expressed with tryptophan hydroxylase, the rate limiting enzyme for serotonin

production,88

and a Shibire mutant which reversibly blocks the neurosecretory system

above a threshold temperature of 30°C. Under a fluorescence microscope the cells

expressing tryptophan hydroxylase are easily visualized allowing for dissection of

serotonergic cells while the blocked neurosecretory activity prevents inadvertent release

of contents during the dissection process.

11

1.5.3 Ctenophore

The complex nervous systems present in higher order animals have their origins

in the earliest metazoan life.89

The use of molecular genomic techniques has revealed

many new relationships among Bilateria,90

but there is poor resolution of placement

among early phyla.91-92

One method in understanding the conservation of signaling

pathways is to study the ancient systems of early species such as the Ctenophore,

Pleurobrachia. Ctenophores diverged prior to the occurrence of Bilateria but still have a

differentiated, yet diffuse, nervous system with synapses that are bidirectional. This

rudimentary nervous system is not centralized and is often described as a neural net.93

However, even without the advanced organization seen in higher species it can still feed,

reproduce, and defend itself.

12

1.6 Figure

Kynurenine Metabolites • Inflammatory regulated pathway • Downstream metabolites are both

neuroprotective and neurotoxic • Implicated in numerous

neurodegenerative disease • Alzheimer's • Parkinson's • Multiple sclerosis • Huntington's

Serotonin Metabolites • Homeostasis

• Temperature regulation • Neurogenesis • Digestion • Circadian rhythm

• Pathologies • Depression • Schizophrenia • Irritable bowel syndrome

Fig. 1-1 Shown here are the major, neuroactive

metabolites of tryptophan.

13

1.7 References

1. Fernstrom, J. D.; Fernstrom, M. H., Tyrosine, phenylalanine, and catecholamine synthesis and function in the brain. Journal of Nutrition 2007, 137 (6), 1539S-1547S.

2. Buhot, M. C., Serotonin receptors in cognitive behaviors. Current Opinion in Neurobiology 1997, 7 (2), 243-254.

3. Leibowitz, S. F.; Alexander, J. T., Hypothalamic serotonin in control of eating behavior, meal size, and body weight. Biological Psychiatry 1998, 44 (9), 851-864.

4. Lucki, I., The spectrum of behaviors influenced by serotonin. Biological Psychiatry 1998, 44 (3), 151-162.

5. Peck, T. L.; Webb, A. G.; Wu, N.; Magin, R. L., et al. In On-line NMR detection in capillary electrophoresis using an RF microcoil, Baltimore, MD, USA, IEEE: Baltimore, MD, USA, 1994; pp 986-987.

6. Jankowski, J. A.; Tracht, S.; Sweedler, J. V., Assaying single cells with capillary electrophoresis. Trac-Trends in Analytical Chemistry 1995, 14 (4), 170-176.

7. Fuller, R. R.; Moroz, L. L.; Gillette, R.; Sweedler, J. V., Single neuron analysis by capillary electrophoresis with fluorescence spectroscopy. Neuron 1998, 20 (2), 173-181.

8. Dahlgren, R. L.; Page, J. S.; Sweedler, J. V., Assaying neurotransmitters in and around single neurons with information-rich detectors. Analytica Chimica Acta 1999, 400 (1-3), 13-26.

9. Monroe, E. B.; Jurchen, J. C.; Rubakhin, S. S.; Sweedler, J. V., Single-cell measurements with mass spectrometry. Xu, X. H. N., Ed. 2007; Vol. 172, pp 269-293.

10. Lapainis, T.; Rubakhin, S. S.; Sweedler, J. V., Capillary electrophoresis with electrospray ionization mass spectrometric detection for single-cell metabolomics. Analytical Chemistry 2009, 81 (14), 5858-5864.

11. Timperman, A. T.; Khatib, K.; Sweedler, J. V., Wavelength-resolved fluorescence detection in capillary electrophoresis. Analytical Chemistry 1995, 67 (1), 139-144.

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14

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24. Jorgenson, J. W., Zone electrophoresis in open-tubular capillaries. Trends in Analytical Chemistry 1984, 3 (2), 51-54.

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37. Lee, T. T.; Yeung, E. S., High-sensitivity laser-induced fluorescence detection of native proteins in capillary electrophoresis. Journal of Chromatography 1992, 595 (1-2), 319-325.

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39. Sluszny, C.; He, Y.; Yeung, E. S., Light-emitting diode-induced fluorescence detection of native proteins in capillary electrophoresis. Electrophoresis 2005, 26 (21), 4197-4203.

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44. Karger, A. E.; Harris, J. M.; Gesteland, R. F., Multiwavelength fluorescence detection for DNA sequencing using capillary electrophoresis. Nucleic Acids Research 1991, 19 (18), 4955-4962.

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46. Squires, L. N.; Jakubowski, J. A.; Stuart, J. N.; Rubakhin, S. S., et al., Serotonin catabolism and the formation and fate of 5-hydroxyindole thiazolidine carboxylic acid. Journal of Biological Chemistry 2006, 281 (19), 13463-13470.

47. Azmitia, E. C., Evolution of serotonin: Sunlight to suicide. In Handbook of Behavioral Neuroscience, Christian, P. M.; Barry, L. J., Eds. Elsevier: 2010; Vol. Volume 21, pp 3-22.

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75. Grimbaldeston, M. A.; Chen, C. C.; Piliponsky, A. M.; Tsai, M., et al., Mast cell-deficient W-sash c-kit mutant kitW-sh/W-sh mice as a model for investigating mast cell biology in vivo. American Journal of Pathology 2005, 167 (3), 835-848.

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21

Chapter 2

Improved Capillary Electrophoresis Instrumentation Using

Wavelength-Resolved, Laser-Induced Native Fluorescence Detection

Notes and Acknowledgements

We would like to acknowledge the skills and assistance of the School of

Chemical Sciences Machine Shop in manufacturing and modifying equipment during the

design and construction of the instrumentation described within this chapter. Funding for

a new laser excitation source was secured by a supplement to the National Institute of

Neurological Disease and Stroke’s grant NS031609, with the rest of the equipment

coming from both NS031609 and DE018866. This work builds on and updates the prior

CE-LINF systems created and used by Dr. Aaron Timperman, Dr. Kurt Oldenburg, Dr.

Robert Fuller, Dr. Jeffrey Stuart, Dr. Xin Zhang and Dr. Leah Squires as cited herein.

2.1 Introduction

The chemical analysis of neural systems oftentimes requires highly sensitive

bioanalytical techniques compatible with volume- and mass-limited sampling methods to

investigate the chemical content of the billions of neurons and trillion of synapses within

the brain. With the complex cellular organization of neural systems, analysis at vastly

varying levels from whole brain, to individual regions and structures, to single neurons,

and even subcellular domains provides valuable information about chemical

distribution.1-3

While there are many metabolic pathways integral to the central nervous system

(CNS), those related to indoles and catechols are highlighted here. Tryptophan and

22

tyrosine metabolism produces neurotransmitters, trophic factors, enzyme cofactors, and

neuromodulators critical to the functioning of neural systems that are related to functions

and behaviors including aggression,4 anxiety,

5 depression,

6-7 and feeding

8 as well as

numerous pathologies. Even though many of these neuroactive compounds have been

well researched, the underlying mechanisms are still not well understood, particularly in

complex interactions between pathways. As an example, reward and addiction pathways

are known to be dopaminergic,9-10

but serotonin signaling has been shown to be

inhibitory to dopaminergic systems within the brain as reviewed by Daw et al.,

complicating model addiction paradigms.11

Investigations into the function of these

compounds will be aided by both the development of new and the improvement of

existing selective, sensitive, and quantitative methods, particularly systems capable of

multi-analyte analysis.

Capillary electrophoresis (CE) has been an effective tool in neuroscience

investigations as reviewed by Lapainis et al.12

and Powell et al.13

High separation

efficiencies, on-line sample concentration, and, most importantly, low sample volume

requirements make CE well-suited for the analysis of highly complex, mass- and volume-

limited samples. These attributes enable analysis at high spatial resolution, down to the

single neuron and subcellular levels. Additionally, compatibility of CE with a variety of

detection methods allows for the analysis of analytes with a wide range of

physiochemical properties. Common methods include electrochemical,14

UV

absorption,15

mass spectrometry,16

conductance,17-18

and fluorescence19

detection

schemes. Applications in neuroscience have relied primarily on electrochemical and

fluorescence systems given their high sensitivities.

23

Fluorescence has been an attractive detection method for CE since its original

description by Jorgenson and Lukacs in 1981 which used a mercury arc lamp for

excitation.20

The advent of lasers as an excitation source have made fluorescence even

more applicable and competitive among the available detection schemes.21

Fluorescence

can be achieved either by derivatization of target molecules with a fluorophore or by

taking advantage of the intrinsic fluorescence exhibited by a subset of molecules.

Numerous derivatization reactions for fluorophore addition to analytes prior to detection

have been developed and have often yielded exceedingly low levels of sensitivity,22-23

but

these reactions are often complex, incomplete, unstable, or non-specific, drawbacks that

are particularly problematic with samples of limited volume.24-26

Taking advantage of

the intrinsic fluorescence of analytes for unlabeled detection avoids the pre-analysis

reactions and reduces interfering signals from co-migrating peaks. Biological systems

contain a number of natively fluorescent compounds. The amino acids tryptophan and

tyrosine have absorbance cross sections and sufficient fluorescence quantum yields that

allow for detection by native fluorescence at trace levels.27-28

As a result, many

metabolites of tryptophan and tyrosine along with peptides and proteins containing these

residues have excitation and emission spectra in the deep UV, 200-300 nm and 300-400

nm respectively. The early work developing CE-LINF used the 275.4 nm line from an

argon-ion laser to excite tryptophan and tyrosine containing peptides29

with later

applications targeting small-molecule metabolites.30-33

Native fluorescence has also been

used to visualize dynamic serotonin release from living cells.34

Wavelength-resolved fluorescence detection in CE is an effective, information-

rich method for identification and quantitation of fluorescent analytes when combined

24

with LINF excitation. Since the initial description of wavelength-resolved detection

systems for CE using a photodiode array35

and later using an optimized CCD system,36

several systems have been developed for a wide range of applications including

quantitative analysis of peptides37

and proteins,38

characterization of protein folding,39

analysis of environmental contaminants,40

DNA sequencing,41

and neurotransmitter

analysis.42-44

These instruments have used photodiode arrays, multiple PMTs,45

and

charge-coupled devices (CCD)46

as high information, multichannel detection systems.

Our group has constructed several versions of CE-LINF instruments, some equipped with

wavelength-resolved detection.45-51

These systems have been used to detect and identify

new serotonin catabolism pathways,42, 44

assay single neurons from Aplysia californica,49

and measure serotonin from mast cells in the hippocampus.52

The instrumentation described here is an update and redesign to our previous

systems intended to improve performance, versatility, and throughput. With this

redesign, the sensitivity was improved for the tested analytes primarily due to a UV-

enhanced CCD that is well-suited measuring fluorescence from 250-700 nm with

quantum efficiencies (QE) of 60-70% in the 300-400 nm range. Additionally, multiple

readout rates and gain settings for the CCD allow for optimization for analysis at high

sensitivities or quantitation of intense signals. Longer exposure times are possible given

the increase in readout speed, improving signal-to-noise ratios. The control software was

designed for spectroscopic measurements and substantially improves practical use and

throughput. An argon-ion laser operating in the deep UV was used with flexibility of

excitation at either 264 or 229 nm. Improvements in detection limits were approximately

5-fold for serotonin and dopamine and 2-3 fold for epinephrine and octopamine. With

25

sensitivities competitive with the current state of the art and the additional information-

rich, wavelength-resolved detection, the system is capable of providing sophisticated

analyses from a range of neural systems.

2.2 Experimental

2.2.1 Chemicals and Solutions

Standards and chemicals were obtained from Sigma-Aldrich (St. Louis, MO,

USA) at the highest purity possible unless otherwise noted. Serotonin was purchased

from Alfa Aeser (Ward Hill, MA, USA). Water for solutions and standards was obtained

from an Elga PureLab Prima water filtration system (Woodridge, IL, USA) at purity of

18.2 MΩ.

Extraction media consisted of 49.5/49.5/1, methanol (LC-MS grade)/water/glacial

acetic acid (99%) by volume. Standards were weighed (1-2 mg) on a microbalance

(Mettler Toledo; Columbus, OH, USA) and dissolved in extraction matrix for high

concentration stock solutions. These stocks were stored at -80°C until use when diluted

to working concentrations. Citric acid (25 mM, pH 2.5) used for sheath-flow liquid was

prepared by dissolving 5.25 g citric acid in 1.0 L ultrapure water. Borate buffer (50

mM, pH 9.25) for separations was prepared by dissolving 9.2 g of sodium borate with 3.0

grams of boric acid in 1 L of ultrapure water. Both the citric acid and borate buffers were

filtered using a 0.22 µm vacuum filter and degassed by maintaining the vacuum for 20

minutes while stirring.

26

2.2.2 Instrument Design

The major design considerations for the instrument were excitation source,

separation and detection interface, and fluorescence detection. A diagram along with

photographs of the primary components of the laboratory-built CE-LINF instrument are

presented in Fig. 2-1. The excitation source was an Innova 90c argon-ion, frequency-

doubled (FreD) laser (Coherent, Inc.; Santa Clara, CA, USA). Accessible deep UV

wavelengths were 229 and 264 nm by use of 2 different beta barium borate (BBO),

frequency-doubling crystals. Excitation occurs post-capillary within a 3x3x45 mm UV-

grade, fused-silica cuvette which was open at both top and bottom (Starna Cells, Inc.;

Atascadero, CA, USA). The laser was focused approximately 1.0 mm below the outlet

of the capillary. Fluorescence was collected orthogonal to the excitation path using a

MgF2 coated all-reflective collection objective (12596; Newport; Irvine, CA, USA). The

collected fluorescence emission was imaged onto the CCD array by a flat-field corrected

grating (CP140-103; Horiba Scientific; Edison, NJ, USA). The CCD detector (Spec-10;

2KBUV/LN; Princeton Instruments; Trenton, NJ, USA) was back-illuminated with an

antireflection UV coating, yielding quantum efficiencies of 60-70% in the deep-UV

(300-400 nm). The active area of the array is 512x2048 pixels with each measuring 13.5

x 13.5 µm. The CCD is equipped with a dual-speed digitizer (100 kHz and 1 MHz) as

well as low-noise and high-capacity output modes which allowed the system to be

versatile for trace detection or quantitation of intense signals. The spectral range

recorded was 275 – 700 nm.

27

2.2.3 Instrument Control and Data Processing

Individual fluorescence spectra were obtained by binning every 2 columns on-chip

producing a spectrum of 1024 data points for each exposure. The exposure of the CCD

array started with a +5 volt TTL pulse and lasted for as long was set within the CCD

control software. Typically, data was collected at 2 Hz with 440 ms of active collection,

50 ms to read the data from the array, and a 10 ms gap before the arrival of the next TTL

pulse. The TTL pulses were generated using computer controlled, USB digital I/O

device from National Instruments (Austin, TX, USA) with the frequency and duration of

pulses controlled by a laboratory-written LabView (National Instruments; Austin, TX,

USA) program. Emission spectrum from a Hg(Ar) pen lamp (Oriel Instruments;

Stratford, CT, USA) was used for wavelength calibration of the CCD.

The raw data was imported into MatLab (The MathWorks Inc.; Natic, MA, USA)

for processing, smoothing, visualization, and quantitation. The background was

subtracted by averaging the first 50 fluorescence spectra, prior to analyte elution from the

capillary, and subtracted from each individual spectrum within the data matrix. The

active array of the CCD is sensitive to charged particles generated from the impact of

cosmic rays with gaseous nuclei in the upper atmosphere. These secondary cosmic rays

degrade into mostly muons which pass through the active area on the CCD array and

result in signal spikes. These are identified by the relatively narrow peak characteristics

both in the time and fluorescence domains of the data. The spikes are less than 3 adjacent

pixels in width and are confined to an individual exposure making them readily

identifiable in comparison to typical, broader features produced by analytes in the

electropherogram.

28

The most significant sources of high-frequency noise present within the system are

from optical shot noise and electronic noise from the CCD system hardware and signal

transduction. Several smoothing methods were evaluated – boxcar averaging, Savitzky–

Golay, and a two-dimensional Gaussian filter. The filtering method that produced the

best signal-to-noise ratio was a two-dimensional Gaussian filter optimized for both

wavelength and time domains.47

Size and power of the filter was optimized based on a

collection rate of 2 Hz and the fluorescence spectra obtained from multi-analyte standard.

2.2.4 Electrophoresis

A 50/150 µm ID/OD fused silica capillary (Polymicro Technologies; Phoenix,

AZ, USA) was used as the separation column. Capillaries were conditioned prior to use

by flushing with 0.1 N sodium hydroxide for 5 minutes and then flushed with separation

buffer for a minimum of 5 min. Sample injections were hydrodynamic by lowering the

waste outlet and siphoning 10 nL of sample into the capillary from the custom, stainless

steel nanovials. Separations were performed using 25 mM citric acid for dopamine,

epinephrine, and norepinephrine and 50 mM borate separation buffer for all other

compounds. 25 mM citric acid was used as the sheath-flow liquid for all separations.

Voltage was applied using a variable, up to +30 kV, high voltage power supply

(Spellman; Hauppauge, NT, USA) in conjunction with a laboratory-built control box.

Applied voltages were +21 kV for separations using 50 mM borate as the separation

buffer and +30 kV for those using 25 mM citric acid.

29

2.2.5 Data Analysis

Limits of detection were defined to be the concentration of analyte that produces a

signal-to-noise ratio of 3 with the signal being the peak fluorescence intensity and the

noise being the standard deviation of the background directly preceding the peak. Each

analyte was analyzed individually at a concentration of 2 µM and the data processed as

described in section 2.2.3 “Instrument Control and Data Processing”.

2.3 Tissue Extraction

Brain stem tissues were dissected from Sprague Dawley rats purchased from

Harlan, Inc. (Indianapolis, IN, USA). Animal euthanasia was performed in accordance

with an animal use protocol approved by the University of Illinois’ Institutional animal

care and use committee, local, and federal regulations. Brainstem was surgically

removed immediate after animal decapitation by sharp guillotine. The excised brain

structures were flash frozen in dry ice cooled isopentane and transferred into individual

capped plastic tubes and stored at -80°C until extraction. Extraction media was added to

the weighed tissue at a ratio of 5 µL/mg of wet weight tissue. The tissue was manually

homogenized with the extraction media and allowed to extract at for 90 min, centrifuged

at 16,000 g for 15 min, and then the supernatant was filtered using an Amicon 10 kDa

MWCO filter (Millipore; Billerica, MA, USA) at 4°C and stored at -80°C until analysis.

2.4 Results and Discussion

Wavelength-resolved detection when using native fluorescence provides an

information-rich detection method where analytes can be interrogated not only by

30

migration time, but spectral characteristics as well. Our goal here was to build an

updated and improved instrument based on our previous successes using CE-LINF

methods over the previous 15 years. Improvements in technology allows for a more

sensitive, efficient, and versatile instrument to be built for the analysis of tyrosine and

tryptophan metabolites within neural systems, as well as correct for issues related to

failing components in the original system.

2.4.1 Instrument Design

The instrument setup along with photographs of the key components and

interfaces are given in Fig. 2-1. There were two major design considerations - excitation

source and detection system. Excitation sources in the deep UV have traditionally been

gas-ion lasers or frequency-quadrupled Nd:YAG lasers, but more recently laser emitting

diodes (LEDs),53

diode-pumped, solid-state lasers,54

and hollow-cathode, metal-vapor

lasers45, 50

have been developed which access wavelengths in the deep UV and have been

used as excitation sources for LINF instrumentation. Here, an argon-ion laser was

selected for its stability and access to several different wavelengths in the DUV,

specifically 229 and 264 nm for this system using two different beta barium borate

crystals. Indoles are efficiently excited at 264 nm, however, catecholamines have a

larger absorbance cross section at 229 nm which accesses the S0-S2 transition.55

The 229

nm option of the instrument is accessible, but the available output power is much lower

than 264 nm, ~0.5 mW and 15 mW respectively.

As the fluorescence emission for the analytes of interest is in the 300-500 nm

range, a specialized UV sensitive CCD is required. Typical CCD arrays are not

31

responsive to photons in the UV region, but detection can be accomplished using a

phosphorus coating to red shift incident photons to a wavelength where the CCD has a

higher quantum efficiency56-57

or by design changes for thinned, back-illuminated

devices with anti-reflection coatings which increase the quantum efficiency in the UV.58

The back-illuminated CCD array used here was designed to enhance the quantum yield in

the UV region using anti-reflection coatings instead of the photon conversion as has been

used on our previous instruments. The result is an impressive quantum yield of 60-70%

in the 300-400 nm range. Readout rates for this CCD are also substantially improved and

variable to facilitate either low noise or fast acquisition by built-in, dual-speed digitizers.

With LODs and separation efficiencies significantly affected by signal processing,

several methods were evaluated to remove high-frequency noise. Parameters were

optimized for box car averaging, Savitzky-Golay, and Gaussian filtering. The

application of a Gaussian filter improved the signal-to-noise ratio while still retaining

peak height and width resulting in the lowest LODs. This filter was two-dimensional and

both the wavelength and time domains were optimized for the typical feature size for

each. Application of this filter resulted in a 5-10 fold decrease in LODs for most analytes

over unprocessed data.

2.4.2 Performance

One of the goals of building this instrumentation was to improve upon our

previous instruments sensitivities for a wide range of analytes by using a more efficient

CCD and access to excitation wavelengths optimal for two classes of compounds –

indoleamines (264 nm) and catacholamines (229 nm). With the gain, exposure, and

32

readout set for high sensitivity, excellent LODs at both 229 and 264 nm were obtained. A

summary of LODs are provided in Table 2-1 along with reported detection limits from

our previous instrument.59

LODs were improved for all tested analytes. Significantly, an

improvement from 5 nM to 1 nM for serotonin and 120 to 23 nM for dopamine.

Dopamine, epinephrine, and norepinephrine were separated in 25 mM citric acid rather

than the 50 mM borate buffer used for the other analytes as the fluorescence signal is

significantly better, likely due to the complexation of borate with compounds like

dopamine. Even though parameters were optimized for sensitivity, the dynamic range

exceeded a minimum of 2 orders of magnitude (10-1000 nM). Here, sample injection

was accomplished using hydrodynamic injection rather than the previously used

electrokinetic method. It is important to note that the reported detection limits in Table

2-1 are routinely achieved without extraordinary alignment or preparation procedures; in

the past, the instrument had to be optimized daily to achieve the reported LODs and so

another major enhancement is the robust operation of the system.

The performance here is comparative to other recent methods for neurotransmitter

analysis as reviewed by Perry et al.60

Within CE separations laser-induced fluorescence

and electrochemical detection have typically been demonstrated to be the most sensitive.

For example, Benturquia et al. describes a CE-LINF instrument using a single-channel

detector and a pH-mediated, on-column concentration technique that achieves an

impressive 0.25 nM detection limit for serotonin.54

While indoleamines such as

serotonin are highly fluorescent, catecholamines are less fluorescent, but have a stronger

electroactive response and a number of CE-EC methods have been developed for

detection in the low nM regime.60

33

The instrument described here can be used to identify analytes based on spectral

characteristics as well as migration times as demonstrated in Fig. 2-2. This information

is particular useful for analytes that have similar migration times for a particular

separation method. Often times, in preparations of samples derived from biological

sources the matrix contains salts which change the local electric conditions, as well as

proteins and peptides that can coat the capillary walls, both significantly alter migration

times making peak identification difficult. Identification based on spectral

characterization significantly improves the ability to identify peaks even with the

degraded migration reproducibility from biological sample matrices. Baseline resolution

and quantitation can be achieved for co-migrating analytes by using specific wavelengths

for analysis of compounds with differing fluorescence spectra.

Applications to the analysis of biological extracts from neural tissues was

demonstrated using the brainstem of a rat. Analysis at both 264 and 229 nm under the

same separation conditions, Fig. 2-3, compares the two excitation conditions. Signal

from serotonin, tryptophan, tyrosine, and HIAA were readily detectable at both

wavelengths. Generally, the electropherogram using 264 nm excitation contains more

detectable analytes than 229 nm excitation, likely due to the accessible power at 229 is

lower than at 264 nm.

2.5 Conclusions

A CE system equipped with a wavelength-resolved detector provides sensitive

detection and analyte identification for metabolites of the amino acids tryptophan and

tyrosine. The LODs for these analytes have been shown to be well within physiological

34

levels of neural systems (low nanomolar) and competitive with the current state of the art

in the field of trace analysis from neural systems in addition to yielding information-rich

spectral data. Excitation at 229 nm is applicable to a wide range of biologically relevant

compounds, Fig. 2-2, and to extracts from the CNS, Fig. 2-3. Outlined in later chapters

of this dissertation this instrument has been used for a number of tryptophan-related

measurements within a range of model neuronal systems. As tryptophan is the precursor

to a number of neuroactive compounds the ability to measure them at trace levels from

volume-limited samples is of high importance. The update of this instrument was

intended to enhance our laboratories ability to measure these metabolites at even lower

levels and higher throughput for the investigation of biological systems of interest.

2.6 Future Directions

The CE-LINF instrument has proven to be a valuable asset in the investigation of

tryptophan and tyrosine metabolism within neural systems and can be further developed

with improved hardware and software. While substantial improvements in LODs,

readout speed, versatility, and sample throughput have been made over the previous

versions of this instrument, continued development in the areas of automation for both

data analysis and sample handling, would increase the general applicability of the

method. Automated liquid handling would have the advantages of increased

reproducibility between samples as well as increased throughput. Automated methods

for data analysis using peak finding software would significantly increase the throughput

of data analysis. Also, the parameters describing the shape of the Gaussian filter was

optimized for average peak shapes observed. However, various peak shapes in both the

35

spectral and temporal domains often occur. Application of a “smart” filter which would

choose the optimal parameters for the local data environment would increase the quality

of the data without the need for manual manipulation.

36

2.7 Tables and Figures

Analyte LOD264 nm

(nM)

LOD229 nm

(nM)

Previous System

LOD257 nm (nM)59

Tryptophan 5 8 9.8

Serotonin 1 5 5.6

Tryptamine 1 6 4.3

5-Hydroxy Indole

Acetic Acid 50 27 NA

Tyrosine 88 47 45

Dopamine* 36 23 120

Epinephrine* 94 23 60

Norepinephrine* 91 22 3900

Octopamine 6 16 42

Kynurenic Acid 50 14 NA

Kynurenine 16 ND NA

Table 2-1 Limits of detection for natively fluorescent, neuroactive compounds from the

updated instrument at both 264 nm and 229 nm as well as our previous wavelength-

resolved system, demonstrating the improvements in sensitivity. The limits were

determined based on a 2 µM standards. * Separations performed in 25 mM citric acid

with all other performed in 50 mM borate. NA – Not applicable ND – Not detectable

37

Fig. 2-1 Diagram and photographs of the updated and enhanced wavelength-resolved CE-LINF instrument. A.) Injection stage and

nanovials used for volume-limited samples as low as 100 nL. B.) Spectrograph and UV enhanced CCD array. C.) Argon-ion,

frequency-doubled laser with variable outputs of 229, 257, and 264 nm for efficient excitation of both catecholamines and

indolamines. D.) Sheath-flow assembly allowing for post-capillary excitation to limit scatter and background fluorescence.

38

B

Fig. 2-2 Wavelength-resolved

electrophoregram for a

multianalyte standard set

including a variety of biologically

relevant, natively fluorescent

compounds (Top). Normalized

fluorescence spectra from signals

within the white rectangle are

provided in the bottom figure

demonstrating the ability to

identify closely eluting analytes

based on spectral differences.

Identified signals - 1.) tryptamine

2.) serotonin 3.) octopamine 4.)

tyramine 5.) dopamine 6.)

tyrosine 7.) tryptophan 8.)

norepinephrine 9.) tyrsoine 10.)

sulforhodamine 101 11.) 5-

hydroxy indole acetic acid 12.)

kynurenic acid 13.) FMN 14.)

fluorescein 15.) NAD 16.)

NADPH 300 350 400 450 500 550

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Wavelength (nm)

No

rma

lize

d In

ten

sity (

A.U

.)

Serotonin

Tryptamine

Octopamine

Wa

ve

len

gth

(n

m)

Time (min)

4 6 8 10 12 14 16 18 20 22 24

300

350

400

450

500

550

600

650

700

1

2

34

5 6

8

7

9

1112

10

13

14 15 16

1 2 3

39

Fig. 2-3 Wavelength-resolved electropherograms of brain stem extract at 229 m (panel A) and 267 (panel

B). Several identifiable peaks are observed using both excitation wavelengths. Identified analytes include

(1) serotonin, (2) tryptophan, (3) tyrosine, and (4) 5-hydroxy indole acetic acid.

Wav

elen

gth

(nm

)

Time (min)

4 6 8 10 12 14 16

300

350

400

450

500

550

600

650

700

1 2 3 4

Wav

elen

gth

(nm

)

Time (min)

4 5 6 7 8 9 10 11 12 13

300

350

400

450

500

550

600

650

700

1 2 3

4

A

B

40

2.8 References

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9. Schultz, W., Predictive reward signal of dopamine neurons. Journal of Neurophysiology 1998, 80 (1), 1-27.

10. Schultz, W., Getting formal with dopamine and reward. Neuron 2002, 36 (2), 241-263.

11. Daw, N. D.; Kakade, S.; Dayan, P., Opponent interactions between serotonin and dopamine. Neural Networks 2002, 15 (4-6), 603-616.


13. Powell, P. R.; Ewing, A. G., Recent advances in the application of capillary electrophoresis to neuroscience. Analytical & Bioanalytical Chemistry 2005, 382 (3), 581-591.

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17. Guijt, R. M.; Evenhuis, C. J.; Macka, M.; Haddad, P. R., Conductivity detection for conventional and miniaturised capillary electrophoresis systems. Electrophoresis 2004, 25 (23-24), 4032-4057.

18. Zemann, A. J., Capacitively coupled contactless conductivity detection in capillary electrophoresis. Electrophoresis 2003, 24 (12-13), 2125-2137.


20. Jorgenson, J. W.; Lukacs, K. D., Zone electrophoresis in open-tubular glass capillaries. Analytical Chemistry 1981, 53 (8), 1298-1302.

21. Poinsot, V.; Gavard, P.; Feurer, B.; Couderc, F., Recent advances in amino acid analysis by CE. Electrophoresis 2010, 31 (1), 105-121.

22. Waterval, J. C. M.; Lingeman, H.; Bult, A.; Underberg, W. J. M., Derivatization trends in capillary electrophoresis. Electrophoresis 2000, 21 (18), 4029-4045.

23. Underberg, W. J. M.; Waterval, J. C. M., Derivatization trends in capillary eletrophoresis: An update. Electrophoresis 2002, 23 (22-23), 3922-3933.

24. Pentoney Jr, S. L.; Huang, X.; Burgi, D. S.; Zare, R. N., On-line connector for microcolumns: Application to the on-column o-phthaldialdehyde derivatization of amino acids separated by capillary zone electrophoresis. Analytical Chemistry 1988, 60 (23), 2625-2629.

25. Nickerson, B.; Jorgenson, J. W., Characterization of a post-column reaction-laser-induced fluorescence detector for capillary zone electrophoresis. Journal of Chromatography A 1989, 480 (C), 157-168.

26. Hernandez, L.; Tucci, S.; Guzman, N.; Paez, X., In vivo monitoring of glutamate in the brain by microdialysis and capillary electrophoresis with laser-induced fluorescence detection. Journal of Chromatography 1993, 652 (2), 393-398.

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27. Creed, D., The photophysics and photochemistry of the near-UV absorbing amino acids. I. Tryptophan and its simple derivatives. Photochemistry and Photobiology 1984, 39, 537-562.

28. Creed, D., The photophysics and photochemistry of the near-UV absorbing amino acids. II. Tyrosine and its simple derivatives. Photochemistry and Photobiology 1984, 39 (4), 563-575.


30. Lillard, S. J.; Yeung, E. S.; McCloskey, M. A., Monitoring exocytosis and release from individual mast cells by capillary electrophoresis with laser-induced native fluorescence detection. Analytical Chemistry 1996, 68 (17), 2897-2904.

31. Bonnin, C.; Matoga, M.; Garnier, N.; Debroche, C., et al., 224 nm deep-UV laser for native fluorescence, a new opportunity for biomolecules detection. Journal of Chromatography A 2007, 1156 (1-2), 94-100.

32. Li, M. D.; Tseng, W. L.; Cheng, T. L., Ultrasensitive detection of indoleamines by combination of nanoparticle-based extraction with capillary electrophoresis/laser-induced native fluorescence. Journal of Chromatography A 2009, 1216 (36), 6451-6458.

33. Tseng, H.-M.; Barrett, D. A., Micellar electrokinetic biofluid analysis of biogenic amines using on-line sample concentration and UV laser-induced native fluorescence detection. Journal of Chromatography A 2009, 1216 (15), 3387-3391.

34. Tan, W.; Parpura, V.; Haydon, P. G.; Yeung, E. S., Neurotransmitter imaging in living cells based on native fluorescence detection. Analytical Chemistry 1995, 67 (15), 2575-2579.

35. Kobayashi, S.; Ueda, T.; Kikumoto, M., Photodiode array detection in high-performance capillary electrophoresis. Journal of Chromatography A 1989, 480 (C), 179-184.

36. Sweedler, J. V.; Shear, J. B.; Fishman, H. A.; Zare, R. N., et al., Fluorescence detection in capillary zone electrophoresis using a charge-coupled device with time-delayed integration. Analytical Chemistry 1991, 63 (5), 496-502.

37. Timperman, A. T.; Oldenburg, K. E.; Sweedler, J. V., Native fluorescence detection and spectral differentiation of peptides containing tryptophan and tyrosine in capillary electrophoresis. Analytical Chemistry 1995, 67 (19), 3421-3426.

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38. de Kort, B. J.; de Jong, G. J.; Somsen, G. W., Lamp-based wavelength-resolved fluorescence detection for protein capillary electrophoresis: Setup and detector performance. Electrophoresis 2010, 31 (17), 2861-2868.

39. de Kort, B. J.; ten Kate, G. A.; de Jong, G. J.; Somsen, G. W., Capillary electrophoresis with lamp-based wavelength-resolved fluorescence detection for the probing of protein conformational changes. Analytical Chemistry 2011, 83 (15), 6060-6067.


41. Hanning, A.; Lindberg, P.; Westberg, J.; Roeraade, J., Laser-induced fluorescence detection by liquid core waveguiding applied to DNA sequencing by capillary electrophoresis. Analytical Chemistry 2000, 72 (15), 3423-3430.



44. Stuart, J. N.; Zhang, X.; Jakubowski, J. A.; Romanova, E. V., et al., Serotonin catabolism depends upon location of release: Characterization of sulfated and γ-glutamylated serotonin metabolites in Aplysia californica. Journal of Neurochemistry 2003, 84 (6), 1358-1366.



47. Timperman, A. T.; Sweedler, J. V., Capillary electrophoresis with wavelength-resolved fluorescence detection. Analyst 1996, 121 (5), 45R-52R.

48. Oldenburg, K. E.; Xi, X. Y.; Sweedler, J. V., High resolution multichannel fluorescence detection for capillary electrophoresis - Application to multicomponent analysis. Journal of Chromatography A 1997, 788 (1-2), 173-183.

44






54. Benturquia, N.; Couderc, F.; Sauvinet, V.; Orset, C., et al., Analysis of serotonin in brain microdialysates using capillary electrophoresis and native laser-induced fluorescence detection. Electrophoresis 2005, 26 (6), 1071-1079.


56. Franks, W. A. R.; Kiik, M. J.; Nathan, A., UV-responsive CCD image sensors with enhanced inorganic phosphor coatings. IEEE Transactions on Electron Devices 2003, 50 (2), 352-358.

57. Blouke, M. M.; Cowens, M. W.; Hall, J. E.; Westphal, J. A., et al., Ultraviolet downconverting phosphor for use with silicon CCD imagers. Applied Optics 1980, 19 (19), 3318-3321.

58. Shortes, S. R.; Chan, W. W.; Rhines, W. C.; Barton, J. B., et al., Characteristics of thinned backside-illuminated charge-coupled device imagers. Applied Physics Letters 1974, 24 (11), 565-567.

59. Park, Y. H.; Zhang, X.; Rubakhin, S. S.; Sweedler, J. V., Independent optimization of capillary electrophoresis separation and native fluorescence detection conditions for indolamine and catecholamine measurements. Analytical Chemistry 1999, 71 (21), 4997-5002.

45

60. Perry, M.; Li, Q.; Kennedy, R. T., Review of recent advances in analytical techniques for the determination of neurotransmitters. Analytica Chimica Acta 2009, 653 (1), 1-22.

46

Chapter 3

Automated Method for Analysis of Tryptophan and Tyrosine

Metabolites Using Capillary Electrophoresis with Native Fluorescence

Detection


This work has been submitted to the journal Analytical and Bioanalytical

Chemistry under the above title by Christopher A. Dailey, Nicolas Garnier, Stanislav S.

Rubakhin, and Jonathan V. Sweedler and is currently under review. We would like to

thank Beckman Coulter, Inc. for kindly providing the prototype automated CE-LINF

platform for this study. We would also like to thank William Hug from Photon Systems

as well as Nicolas Garnier and Bruno de Vandiere from Flowgene for their efforts in

creating this system. The project described was supported by the following awards: R01

NS031609 from the National Institute of Neurological Disorders and Stroke, P30

DA018310 from National Institute on Drug Abuse, and CHE-11-11705 from the National

Science Foundation.

3.1 Introduction

The metabolism of tryptophan (Trp) and tyrosine (Try) within neural systems

produces a variety of signaling molecules, including neurotransmitters and

neuromodulators. The distribution and tissue-specific levels of these metabolites can be

correlated with functional, behavioral, and pathologic states. For example, serotonin, a

neurotransmitter derived from Trp, is well-known as a modulator of mood. It is also

critical to functions such as circadian rhythm, neurogenesis, memory, learning, and body

temperature regulation, and has been implicated in pathologies including depression,

47

schizophrenia, and bipolar disorder.1 Analytical techniques for measuring the abundance

and distribution of these metabolites within the brain are made challenging by its

complex cellular organization and the often vanishingly small quantities of analytes and

sample volumes available for analysis. The latest human metabolome database2 reported

more the 6800 identified metabolites, demonstrating the demand for selective and

information-rich analytical techniques capable of examining chemically complex

environments that consist of lipids, proteins, peptides, salts, and, the focus of this work,

small-molecule metabolites. While universal characterization approaches such as liquid

chromatography (LC) mass spectrometry (MS)3 and nuclear magnetic resonance

4 offer

great flexibility, the selectivity provided by separations hyphenated to electrochemical

(EC) and laser-induced fluorescence (LIF) detection offers advantages for investigating

many analyte classes, in applications ranging from clinical analysis to basic research.

Capillary electrophoresis (CE) is well-suited for the analysis of trace-level

signaling molecules due to its small sample-volume requirements (nanoliter to

femtoliter), high separation efficiencies (more than 106 theoretical plates), and online

sample concentration techniques, as reviewed by Lapainis et al.5 A variety of detection

modalities are available for use with CE (e.g., EC,6 UV absorbance,

7 LIF,

8-9 and MS

10).

Oftentimes, the universal nature of these approaches and the high chemical information

content they yield complements many studies. However, selective detection provided by

EC or LIF allows trace level characterization from complex samples as only a subset of

the molecules within the sample are detected. Both labeled and label-free methods can

be used for LIF detection schemes. Labeling target molecules by derivatization with

highly fluorescent dyes can achieve high sensitivities, but it is difficult to achieve

48

efficient labeling of small-volume samples without causing analyte dilution. In addition,

non-specific and incomplete reactions can make analysis challenging, particularly within

the chemically complex sample environment and limited sample volumes, such as those

of interest in the present work.

The quantitative nature of fluorescence is an advantage compared to other

detection approaches. Tryptophan and tyrosine metabolites both contain an aromatic ring

with S0 S1 or S0 S2 transitions upon deep ultraviolet (DUV) excitation (200–300

nm).11

These metabolites, along with Trp and Tyr containing peptides and proteins, have

fluorescence quantum yields ranging from 0.13–0.28, which are sufficient to take

advantage of this native fluorescence as a detection method at trace levels.12-13

Thus,

native fluorescence is an attractive detection choice for both traditional and microchip CE

systems.14

However, until recently, DUV excitation sources were large, costly, and/or

high maintenance, thereby limiting wide-spread application of CE systems using native

fluorescence. The first CE, laser-induced native fluorescence (LINF) instrumentation

used argon-ion (257, 305, and 275 nm)15-17

or frequency quadrupled Nd:YAG (266 nm)18

lasers as excitation sources. More recently, lower cost, stable DUV sources such as light

emitting diodes19-20

and hollow-cathode metal vapor lasers21

have been employed in the

effort to develop less expensive, more efficient detection systems.

Here, we describe a prototype of an automated CE-LINF instrument used for the

analysis of mammalian central nervous system (CNS) tissues and evaluate the analytical

figures of merit for several neuroactive metabolites of tryptophan and tyrosine. The

design criteria included automated sampling, DUV excitation, and flexible emission

wavelength detection. The system uses a pulsed He-Ag, metal-vapor laser with a 224 nm

49

output for excitation, and tunable emission wavelength selection via a spectrometer,

followed by detection with a photomultiplier tube, in many ways similar to systems we

have reported on previously.15, 21-22

Prior CE instrumentation using DUV native

fluorescence detection have been demonstrated to be effective for trace analyses of

catecholamines and indolamines in chemically complex samples.22-25

However, the

complexity and expense of both the excitation and wavelength-resolved (WR) detection

instruments in these earlier systems have limited their general availability and slowed

more widespread adoption of the technique.21, 25-31

The components of the instrument

described here are relatively low-cost and can be installed on an existing automated CE

instrument, allowing for high sample throughput and reproducibility.

The applicability and performance of the automated CE-LINF system is

demonstrated using brain stem tissue from rat brain and the results compared to those

from a WR-CE-LINF system. In addition to its ability to target selected metabolites, the

prototype instrument is applicable to a wide range of other analyte classes via native

fluorescence detection, including proteins and peptides containing Tyr and Trp residues.

3.2 Experimental

3.2.1 Chemicals, Solutions, and Materials

Epinephrine, norepinephrine, dopamine, tyramine, tryptamine, citric acid

monohydrate, 5-hydroxy indole acetic acid, melatonin, tryptophan, tyrosine, N-acetyl

serotonin, glacial acetic acid, and methanol were purchased from Sigma Aldrich (St.

Louis, MO, USA). Serotonin was obtained from Alfa Aeser (Ward Hill, MA, USA).

Chemicals for standards were purchased at the highest purity possible. Ultrapure water

50

for stock solutions and standards was obtained from an Elga PureLab Prima water

filtration system (Elga LLC, Woodridge, IL, USA) at a purity of 18.2 MΩ. Fused-silica

capillaries were obtained from Polymicro (Phoenix, AZ, USA).

The extraction media consisted of 49.5/49.5/1, methanol (LC-MS

grade)/water/glacial acetic acid (99%) by volume. The background electrolyte (BGE) for

separations was made by dissolving 5.25 g of citric acid monohydrate (25mM, pH 2.75)

in 1.0 L of ultrapure water and sonicated to dissolve and de-gas. Sodium hydroxide (0.1

N), used for capillary conditioning, was obtained from Beckman Coulter Inc. (Brea, CA,

USA).

3.2.2 Preparation of Standards

Individual, high concentration stocks for standards were prepared by weighing 1–

2 mg on a microbalance (Mettler Toledo; Columbus, OH, USA) and dissolving the

analyte into the extraction media. Stocks were stored at –80°C until dilution to the

working concentrations as indicated herein.

3.2.3 Tissue Extraction and Quantitation

Brainstem tissues were dissected from Sprague Dawley rats (Harlan, Inc.,

Indianapolis, IN, USA). Animal euthanasia was performed in accordance with animal

use protocols approved by the University of Illinois Institutional Animal Care and Use

Committee, and local and federal regulations. Brainstems were surgically removed

immediately after animal decapitation by sharp guillotine. The excised brain structures

were flash frozen in dry-ice cooled isopentane and transferred into individual capped

51

plastic tubes and stored at –80oC until extraction. Extraction media was added to the

weighed tissue at a concentration of 5 µL/mg of wet weight. The tissue was manually

homogenized with the extraction media and allowed to extract for 90 min, centrifuged at

16,000 g for 15 min, and the supernatant filtered using an Amicon 10 kDa molecular

weight cut-off filter (Millipore; Billerica, MA, USA) at 4oC. Samples were stored at –

80oC until analysis.

Working curves were established using dilutions of high stock standards at

concentrations of 1.00 µM, 500 nM, 250 nM 125 nM, and 62.5 nM. Each concentration

was analyzed in duplicate on the same day as the tissue analysis; linear regression

analysis was used to determine the working equation.

3.2.4 CE Separation Conditions

The CE separations for both the automated and laboratory-built systems were

performed using a BGE of 25 mM citric acid (pH 2.5) and an applied voltage of +30kV

(18–20 µAmp). The BGE was filtered using a 0.2 µm surfactant-free, cellulose acetate

syringe filter (Nalgene; Rochester, NY, USA) immediately prior to use. Between each

separation, the capillary was conditioned for 1 min with 0.1 N NaOH at 15 psi and then

rinsed with BGE for 2 min at 30 psi. Total analysis time, including capillary

conditioning, was approximately 10 min for each analysis.

3.2.5 Automated CE-LINF System

A prototype CE-LINF detection system was provided by Beckman Coulter, Inc.;

its fundamental design has been previously described in application with an HPLC

52

system.32

Briefly, a hollow-cathode, He-Ag laser operating at 224 nm is used as the

excitation source. The incident angle for excitation is set at 30o to reduce scatter and

power loss. Fluorescence is collected using a patented elliptical cell with the capillary

and the spectrometer entrance slit positioned at the two foci.33

We used a polyimide coated, fused silica capillary, 50 µm inner diameter (ID) ×

365 µm outer diameter (OD) and 48 cm in length, with a detection window created at 37

cm. The coating at the detection window was removed using a capillary window maker

(MicroSolv; Eatontown, NJ, USA) in lieu of the more traditional open flame method to

prevent excessive damage to the capillary surface. The bare fused-silica of the detection

window was cleaned by sonication for 10 min in ethanol and any remaining debris

removed using optical lens paper (Thor Labs; Newton, NJ, USA). Care was taken to

clean and protect the surface of the detection window as contaminates and/or defects

both on and in the surface of the capillary can increase background and scattering

significantly, particularly when using DUV radiation.

3.2.6 CE-WR-LINF System

The limits of detection (LODs) and analyte identification of the automated CE-

LINF instrument were compared against those of a laboratory-built CE-WR-LINF system

discussed in Chapter 2.25, 34

Briefly, this instrument uses 264 nm excitation (frequency

double of the 528 nm fundamental) from an Innova FreD 300C argon ion laser (Coherent,

Inc; Santa Clara, CA, USA). We used a separation capillary with a 50 µm ID × 150 µm

OD and a length of 50 cm, and post capillary excitation within a sheath-flow cell

53

containing 25 mM citric acid. The resulting fluorescence spectra were imaged onto a

charge-coupled device by a flat-field corrected spectrograph.

3.2.7 Automated System Optimization

In order to determine the optimal detection wavelength for the analytes of interest,

the fluorescence emission was scanned using the automated CE system. Each analyte

was dissolved at a concentration of 10 µM in ultrapure water and flowed through the

capillary using a positive pressure of 15 psi. Fluorescence spectra were scanned from

approximately 200–600 nm and the resulting spectra were normalized to the peak

intensity after background subtraction (Fig. 3-1). Subsets of these analytes that had peak

intensities at similar wavelengths were separated into two sets of standards for

evaluation—Set A: serotonin, tryptophan, and tryptamine, and Set B: tyrosine, dopamine,

epinephrine, norepinephrine, and tyramine. Both sets were evaluated using a grating

position of optimal fluorescence for the individual set of analytes, as indicated by the

vertical lines in Fig. 3-1.

In order to determine the sample volume for optimal reproducibility, varying

volumes of standard Set A were pressure-injected and the RSD calculated. Solutions

were injected using a pressure of 0.5 psi and the duration was varied from 3–25 s, with

total injected sample volumes ranging from 4 to 33 nL; injections at each volume were

repeated five times. The relative standard deviation (RSD) for both peak height and area

were calculated for tryptamine, serotonin and tryptophan. The injected volume with the

lowest RSD for the analytes was 20 nL, and this volume was used for subsequent

quantitative studies for both performance evaluation and tissue analysis.

54

3.2.8 Data Processing and Analysis

Data was processed using Origin 8.0 (OriginLab Corporation; Northhampton,

MA) for smoothing, background subtraction, and peak quantitation. The Savitzky-Golay

smoothing method was used to reduce the high-frequency noise from the

electropherograms using a window of five data points and a 2nd

order polynomial fitting.

The peak integration analysis function of the Origin software was used to subtract the

background and quantify several peak parameters: migration time, peak height, full width

at half maximum, and peak area.

3.2.9 Evaluation of Analytical Performance

LODs were defined as the concentration of analyte with a signal-to-noise ratio of

3, with the signal as the peak fluorescence intensity of the particular analyte peak, and the

noise as the standard deviation of the background immediately preceding the peak. Each

analyte was analyzed individually using a standard at a concentration of 500 nM and the

data processed as described in the Data Processing and Analysis section above.

Concentrations of 30, 20, 15, 10, and 5 nM for both standard sets were prepared from

high-concentration stocks for the determination of the limits of quantitation (LOQs).

Each concentration was analyzed at n = 5. Analytes were considered quantitative at a

particular concentration if the RSD was less than 20%. Within-day, day-to-day, and total

precision was evaluated over a period of five consecutive days using three concentrations

of both standard sets: 1.25 µM, 312 nM, and 78 nM. Each concentration was analyzed in

triplicate each day. Linearity was evaluated from 29 nM to 20 µM using mixed

55

standards. Both 1st and 2

nd order regression was used to determine the linearity of the

peak area.


3.3.1 Design and Application

CE-LINF as a detection method has proven to be a powerful tool, applicable to a

variety of fields of research. It has been used for the discovery of new serotonin

metabolites,23

and the study of single cells,25, 27

pharmaceutical metabolism,35

environmental monitoring,36

and sequencing.37-39

Oftentimes, a hurdle for its general

applicability is the cost and complexity of DUV excitation sources, which have been

primarily limited to large-frame, gas-ion and quadrupled Nd:YAG lasers. The hollow-

cathode HeAg laser emitting at 224 nm used here is economically competitive with other

fluorescence-based excitation sources used in commercial CE detection systems.

Excitation at 224 nm increases the sensitivity of catecholamines by accessing the S0 S2

transition, which has a higher absorbance cross section than more traditional DUV

excitation wavelengths (e.g., 257, 264, 266, 287 nm) that use the S0 S1 transition.11

Sheath-flow detection cells are typically used for off-column excitation due to the high

level of background and power losses associated with Raleigh scattering in DUV.40-41

Here, the instrumentation is enhanced by using on-column excitation at an incident angle

of 30o to reduce the scatter and a patented ellipsoid excitation cell to collect a larger

fraction of emitted photons as compared to traditional collection optics, making on-

column excitation competitive with more complicated off-column sheath-flow setups.

This configuration allows for application of LINF detection on existing automated CE

56

instrumentation, poising the technique for ready implementation by laboratories currently

using automated CE systems.

When one tags an analyte with a fluorophore, the fluorescence properties of the

tagged analyte tend to be dictated by the characteristics of the particular fluorescence tag.

Native fluorescence, with its inherent information-rich nature, means that individual

species will have differing maxima for their fluorescence spectra. For optimal

performance this requires that either the entire spectra be acquired or a predefined,

optimal wavelength be determined and monitored for the analytes of interest. Generally,

tyrosine metabolites have maxima around 300 nm and tryptophan metabolites around 350

nm. Small changes in the functional groups near the aromatic rings result in subtle

changes in the fluorescence spectra, creating the clusters of fluorescence maxima

observed in Fig. 3-1. Reconfiguration of the ring structure during metabolism can shift

the fluorescence maxima substantially, as is the case for kynurenine and kynurenic acid.

Here, the metabolites of interest were divided into two sets according to the

grouping of the fluorescence maxima: Set A (tyrosine metabolites) and Set B (tryptophan

metabolites). The two detection wavelengths of each set were chosen to correspond as

close to the maximum fluorescence for as many of the analytes as possible from each

cluster. The selected grating position for each is indicated by vertical lines in Fig. 3-1.

Some of the analytes for which fluorescence spectra are included in Fig. 3-1 were

insufficiently fluorescent at the chosen detection wavelengths and were excluded from

the performance studies, specifically, kynurenine, kynurenic acid, phenylalanine, and L-

DOPA. Electropherograms for each of the two sets are included in Fig. 3-2.

57

3.3.2 System Performance

Both systems were evaluated for several figures of merit for each mixed standard

set: LOD, linearity, LOQ, and precision across concentration ranges relevant to levels

that are typical for biological systems. LODs for the prototype system and our own

laboratory-built, WR instrument34

are given in Table 3-1 for comparison of their

performance. The CE-WR-LINF system was somewhat more sensitive for indolamines

(tryptophan, serotonin, tryptamine), likely due to a larger absorbance cross section for the

S0 S1 at 264 nm for indolamines than catecholamines. There was a substantial, more

than 5-fold, improvement in the detection limits for the catecholamines with the

automated CE-LINF over the WR system, also likely due to an increased S0 S2

absorbance cross section, in this case at 224 nm.

Typically, fluorescence demonstrates linearity across many orders of magnitude,

with the limiting factor being the method used for detection. Here, the gain settings were

optimized for the detection of low concentration analytes, which limits the dynamic

range, but is optimal for the analytes of interest in CNS tissues. Even so, as summarized

in Table 3-2, a minimum of two orders of magnitude of linearity was observed for the

analytes with higher fluorescent yields (serotonin, tryptophan, tryptamine, tyramine) and

three orders of magnitude for analytes with lower fluorescent yields (dopamine,

norepinephrine, epinephrine, tyrosine).

One of the many advantages of automated commercial CE systems is the level of

precision that can be achieved by limiting several of the more common sources of

variation inherent in laboratory-built systems. Automated systems offer temperature-

controlled capillary environments and pressure-controlled sample injections, two

58

variables that can substantially degrade reproducibility. Here, the variation associated

with sample introduction was minimized by optimizing the pressure–injected volume.

Fig. 3-3 clearly shows that the most reproducible injection volume is approximately 20

nL. Because LODs were not significantly dependent on injection volume, they were not

taken into consideration when choosing the injection volume. The intraday precision

ranged from 1–11% for the lowest concentrations, and 1–2% percent for the highest

concentrations (Table 3). As expected, the total precision over five days was higher than

intraday, but the data trending (not shown) showed a slight drift toward lower total signal,

indicating a degradation of the standards or minor changes in alignment. However, even

with the minor change in signal, the precision is still excellent and comparable with many

clinical assay performance characteristics.

3.3.3 Analysis of Mammalian CNS Tissue Extracts

The quantitation of trace levels of analyte from tissue extracts presents some

challenges, namely, successful extraction of analytes from tissue while minimizing

effects from interfering protein and peptides. By adding methanol to the extraction

media, we not only preserved the analytes from enzymatic degradation by deactivation of

the enzymes, but sufficiently lowered the conductivity to provide field-amplified stacking

to improve LODs and increase separation efficiencies. While CE is somewhat tolerant to

complex sample environments, excess amounts of proteins can coat the walls of the

capillary, degrading performance. This was mitigated in three ways. First, the total

amount of protein present in the sample was reduced by using a 10 kDa molecular weight

cut-off filter. Second, a low pH separation buffer was chosen. At low pH, the silanol

59

groups on the capillary surface are protonated, limiting the attraction between the

peptides and the surface. Finally, conditioning of the capillary surface with 0.1 N of

NaOH between each analysis removed any contaminants. The amount of sodium

hydroxide used to condition the capillary was kept at a minimum so as to preserve the

capillary and reduce the frequency of replacement. Flushing the capillary for 1 min at 15

psi with 0.1 N of NaOH produced reproducible electropherograms for the tissue extracts.

The analyses of rat brain stem tissue demonstrated the applicability of the

automated CE-LINF instrumentation and method to achieve reproducible quantitation

from CNS tissue extracts. Comparison electropherograms, extracted at the same

wavelength for the tissue extracts, are shown in Fig. 3-4 from both the automated CE-

LINF (Fig. 3-4a) and WR-CE-LINF (Fig. 3-4b) systems, with the full wavelength-

resolved electropherogram shown in Fig. 3-5. The peaks quantified using the automated

CE system are labeled in Fig. 3-4: serotonin, tryptophan, and one indolamine-like,

unidentified peak. Results were: serotonin, 1.25 (± 0.12) nmol/g; tryptophan, 10.6 (±

0.37) nmol/g; and the signal for an unidentified peak, 3.64 (± 0.42) A.U. While the two

electropherograms are similar, minor differences between the two can be attributed to

differences in the fluorescence responses between the two excitation wavelengths. The

utility of tunable monitoring of emission wavelength is further demonstrated by the

wavelength-resolved electropherogram in Fig. 3-5. The varying analyte bands detected

here have spectra ranging from 300-500 nm. Selecting optimal wavelengths for specific

analytes of interest simplifies the electropherogram, reducing potentially interfering

signals.

60

3.4 Conclusions

An automated, cost-effective, and sensitive CE-LINF instrument provides a

platform for routine analysis of tryptophan and tyrosine metabolites in complex sample

matrixes. The monitoring of a particular wavelength of fluorescence emission enables

fast and efficient data processing as compared to WR systems, which result in more

complex data output, requiring more time-consuming data analysis methods. Using a

single wavelength for detection substantially lowers the cost and maintenance over CCD

or array detection platforms. The system is made more versatile by a programmable

spectrograph that allows the optimal detection wavelength to be selected and tailored to

the analytes of interest. We look forward to the commercial availability of this

instrument for both research and clinical applications. While metabolite applications

have been emphasized here, CE-LINF with DUV is applicable to many areas, including

general characterization of peptides and proteins.16, 20, 42

The advantages of fluorescence

include its fairly constant response for many Try and Trp-containing proteins and ease of

quantitation. We expect the use of LINF to greatly expand with the availability of this

detection modality for CE.

61


Table 3-1 Summary of detection limits (S/N = 3) of the automated CE-LINF platform in

comparison to CE-WR-LINF system. The LODs for the tryptophan metabolites detected with

the automated system are competitive with the wavelength-resolved system, showing a 5-fold

improvement for tyrosine metabolites.

Analyte

Automated System

(nmol/L)/(Attomoles)

(224 nm)

Wavelength-Resolved System

(nmol/L)(Attomoles) (264 nm)

Tryptophan 18 / 342 5/50

Serotonin 4 / 76 1/10

5-HIAA 13 / 247 50/500

Melatonin 6 / 114 NA

NAS 9 / 171 NA

Tyrosine 15 / 285 88/880

Dopamine 16 / 304 36/360

Epinephrine 16 / 304 94/940

Norepinephrine 30 / 570 91/910

62

Table 3-2 Evaluation of linearity for the selected standards. Typical linearity descriptors

are included, along with the coefficient for the 2nd

order polynomial, which indicates a

negligible 2nd

order component to the fit.

Analyte

Linear

Range

(nM)

R-Squared

(1st order

regression)

Intercept Slope 2nd Order

Coefficient

Serotonin 40–1,250 0.9997 -0.12 0.0083 1.7x10-5

Tryptophan 40–2,500 0.9951 0.16 0.0045 -4.5x10-7

Tryptamine 40–1,250 0.9998 -0.09 0.0104 3.7x10-7

Dopamine 40–20,000 0.9993 -0.02 0.0006 2.6x10-9

Epinephrine 40–20,000 0.9995 0.10 0.0008 -2.7x10-9

Norepinephrine 40–10,000 0.9991 0.10 0.0014 -1.2x10-8

Tyramine 40–5,000 0.9959 0.07 0.0016 -7.2x10-8

Tyrosine 40–10,000 0.9979 0.16 0.0016 -2.3x10-8

63

Table 3-3 Summary of within-day, day-to-day, and total precision for the seven metabolite standards used to evaluate

the analytical merits of the methods used in this study.

Serotonin Tryptamine Tryptophan Tyramine Dopamine Epinephrine Norepinephrine

Level 1

(78 nM)

Within-Day 1% 2% 8% 6% 11% 11% 10%

Day-to-Day 14% 12% 13% 20% 36% 24% 14%

Total 14% 13% 15% 21% 38% 26% 18%

Level 2

(312

nM)

Within-Day 2% 2% 4% 2% 6% 4% 3%

Day-to-Day 9% 9% 10% 9% 10% 9% 9%

Total 9% 9% 10% 9% 11% 10% 10%

Level 3

(1.25

µM)

Within-Day 1% 1% 2% 2% 1% 1% 1%

Day-to-Day 4% 3% 3% 6% 8% 7% 7%

Total 4% 3% 3% 7% 8% 8% 8%

64

Fig. 3-1 Fluorescence profiles for tyrosine metabolites (a) and tryptophan metabolites (b)

were obtained using the automated CE-LINF by scanning the emission spectra of the

analyte being flowed through the capillary. The differences in fluorescence maxima for

the various metabolites require selection of a wavelength to monitor corresponding as

close as possible to the maxima of the analytes of interest. Vertical lines denote the

grating position for detection for each set of metabolites

400 450 500 550

0.6

0.8

1.0

No

rma

lize

d I

nte

nsity (

A.U

.)

Grating Position (A.U.)

Serotonin

HIAA

Tryptamine

NAS

Melatonin

Tryptophan

Kynurenine

Kynurenic Acid

A

275 288 300 313 325 338 350 363 375 388 400

0.6

0.8

1.0

Norm

aliz

ed

Inte

nsity (

A.U

.)

Grating Position (A.U.)

Norepinephrine

Tyrosine

Epinephrine

Dopamine

Tyramine

L-DOPA

PA

B

65

3 4 5

100

200

300

400

500

600

Norepinephrine

EpinephrineTyramine

Tryptophan

Dopamine

Tyrsoine

Serotonin

Inte

nsity (

A.U

.)

Time (min)

Tryptamine

Set A

Set B

Fig. 3-2 Stacked electropherograms of standard set A (tryptamine, tryptophan and

tryptophan) and B (tyramine, dopamine, epinephrine, norepinephrine, and tyrosine) from

the automated CE-LINF system. Electropherograms were monitored at the optimal

wavelengths determined for each set.

66

Fig. 3-3 Optimization of injection volumes. Varying volumes of standard Set A were

injected by applying pressure at 0.5 psi for varying durations. While there was minimal

variation in the LODs between the different injection volumes, the intra-day precision

was substantially improved at a volume of ~20 nL.

67

Fig. 3-4 Typical electropherograms for the same brain stem tissue extract from the (a)

automated prototype CE-LINF instrument provided by Beckman Coulter, Inc. and the (b)

laboratory built, wavelength-resolved instrument. The differences in capillary lengths

between the two instruments account for the differences in migration times. The

identified peaks from serotonin and tryptophan were aligned for comparison. While the

electropherograms are similar, the differences can be attributed to individual analyte

responses to the differing excitation wavelengths. Time scales have been aligned using

known peaks (numbered) Key: 1 – Unidentified, but quantified peak, 2 – serotonin, 3 –

tryptophan, 4 – tyrosine, * – unidentified peak

a

b

68

Fig. 3-5 Analysis of the same tissue extract Fig. 3-4 using wavelength-resolved native

fluorescence detection. Several of the peaks can be identified by comparison of spectral

and migration times to standards. The unidentified peaks are likely other tryptophan

metabolites or tryptophan-containing peptides/proteins. Key: 1 – Unidentified, but

quantified peak, 2 – serotonin, 3 – tryptophan, 4 – tyrosine, * – unidentified peak.

* *

*

*

*

*

*

69

3.6 References


2. Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R., et al., HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research 2009, 37 (SUPPL. 1), D603-D610.

3. Xiao, J. F.; Zhou, B.; Ressom, H. W., Metabolite identification and quantitation in LC-MS/MS-based metabolomics. TrAC - Trends in Analytical Chemistry 2012, 32, 1-14.

4. Bharti, S. K.; Roy, R., Quantitative 1H NMR spectroscopy. TrAC - Trends in Analytical Chemistry 2012, 35, 5-26.




8. Chang, H. T.; Yeung, E. S., Determination of catecholamines in single adrenal medullary cells by capillary electrophoresis and laser-induced native fluorescence. Analytical Chemistry 1995, 67 (6), 1079-1083.




12. Li, Q.; Seeger, S., Autofluorescence detection in analytical chemistry and biochemistry. Applied Spectroscopy Reviews 2010, 45 (1), 12-43.

13. Chattopadhyay, A.; Rukmini, R.; Mukherjee, S., Photophysics of a neurotransmitter: Ionization and spectroscopic properties of serotonin. Biophysical Journal 1996, 71 (4), 1952-1960.

70

14. Schulze, P.; Belder, D., Label-free fluorescence detection in capillary and microchip electrophoresis. Analytical and Bioanalytical Chemistry 2009, 393 (2), 515-525.



17. Milofsky, R. E.; Yeung, E. S., Native fluorescence detection of nucleic acids and DNA restriction fragments in capillary electrophoresis. Analytical Chemistry 1993, 65 (2), 153-157.

18. Chan, K. C.; Muschik, G. M.; Issaq, H. J., Solid-state UV laser-induced fluorescence detection in capillary electrophoresis. Electrophoresis 2000, 21 (10), 2062-2066.

19. Hapuarachchi, S.; Janaway, G. A.; Aspinwall, C. A., Capillary electrophoresis with a UV light-emitting diode source for chemical monitoring of native and derivatized fluorescent compounds. Electrophoresis 2006, 27 (20), 4052-4059.







71

26. Lillard, S. J.; Yeung, E. S.; McCloskey, M. A., Monitoring exocytosis and release from individual mast cells by capillary electrophoresis with laser-induced native fluorescence detection. Analytical Chemistry 1996, 68 (17), 2897-2904.

27. Yeung, E. S., Study of single cells by using capillary electrophoresis and native fluorescence detection. Journal of Chromatography A 1999, 830 (2), 243-262.

28. Gooijer, C.; Kok, S. J.; Ariese, F., Capillary electrophoresis with laser-induced fluorescence detection for natively fluorescent analytes. Analusis 2000, 28 (8), 679-685.


30. Miao, H.; Rubakhin, S. S.; Sweedler, J. V., Analysis of serotonin release from single neuron soma using capillary electrophoresis and laser-induced fluorescence with a pulsed deep-UV NeCu laser. Analytical and Bioanalytical Chemistry 2003, 377 (6), 1007-1013.

31. Benturquia, N.; Couderc, F.; Sauvinet, V.; Orset, C., et al., Analysis of serotonin in brain microdialysates using capillary electrophoresis and native laser-induced fluorescence detection. Electrophoresis 2005, 26 (6), 1071-1079.

32. Bonnin, C.; Matoga, M.; Garnier, N.; Debroche, C., et al., 224 nm deep-UV laser for native fluorescence, a new opportunity for biomolecules detection. Journal of Chromatography A 2007, 1156 (1-2), 94-100.

33. Debroche, C.; Crespeau, H.; Vandiere, B. D. Optical device for light detector. 7501639, 2009.

34. Park, Y. H.; Zhang, X.; Rubakhin, S. S.; Sweedler, J. V., Independent optimization of capillary electrophoresis separation and native fluorescence detection conditions for indolamine and catecholamine measurements. Analytical Chemistry 1999, 71 (21), 4997-5002.

35. Schappler, J.; Staub, A.; Veuthey, J. L.; Rudaz, S., Highly sensitive detection of pharmaceutical compounds in biological fluids using capillary electrophoresis coupled with laser-induced native fluorescence. Journal of Chromatography A 2008, 1204 (2), 183-190.


72

37. Neumann, M.; Herten, D. P.; Dietrich, A.; Wolfrum, J., et al., Capillary array scanner for time-resolved detection and identification of fluorescently labelled DNA fragments. Journal of Chromatography A 2000, 871 (1-2), 299-310.

38. Heller, C., Principles of DNA separation with capillary electrophoresis. Electrophoresis 2001, 22 (4), 629-643.

39. Zhang, J.; Yang, M.; Puyang, X.; Fang, Y., et al., Two-dimensional direct-reading fluorescence spectrograph for dna sequencing by capillary array electrophoresis. Analytical Chemistry 2001, 73 (6), 1234-1239.

40. Wu, S.; Dovichi, N. J., High-sensitivity fluorescence detector for fluorescein isothiocyanate derivatives of amino acids separated by capillary zone electrophoresis. Journal of Chromatography 1989, 480, 141-155.

41. Cheng, Y. F.; Wu, S.; Chen, D. Y.; Dovichi, N. J., Interaction of capillary zone electrophoresis with a sheath flow cuvette detector. Analytical Chemistry 1990, 62 (5), 496-503.

42. Shippy, S. A.; Jankowski, J. A.; Sweedler, J. V., Analysis of trace level peptides using capillary electrophoresis with UV laser-induced fluorescence. Analytica Chimica Acta 1995, 307 (2-3), 163-171.

73

Chapter 4

Serotonin of Mast Cell Origin Contributes to Hippocampal Function


This work was published under the title “Serotonin of Mast Cell Origin

Contributes to Hippocampal Function” by Nautiyal, K.M.; Dailey, C.A.; Jahn J.L.;

Rodriquez, E.; Son, N.H.; Sweedler J.V.; and Silver, R. in European Journal of

Neuroscience 2012 (currently in press, doi: 10.1111/j.1460-9568.2012.08138.x).1

Christopher Dailey and Son Nygun made the serotonin and tyrosine measurements using

the CE-LINF approach described in Chapter 2. Dr. Kate Nautiyal conducted the

behavioral and physiological studies which are summarized in the introduction along

with dissection and preparation of the hippocampal tissues at Columbia University for

CE-LINF analysis. The work was supported by National Institute of Drug Abuse under

award P30DA018310 and R01 DE018866.

4.1 Introduction

The hippocampus and the subventricular zone are areas of neurogenesis within

the central nervous system (CNS). The hippocampus is involved in spatial learning,

memory consolidation, and depression.2-4

Serotonin levels significantly alter

hippocampal function and associated behaviors both during development and throughout

adulthood.5-6

Specifically, alterations in serotonin signaling within the hippocampus have

been linked to rates of neurogenesis, changes in spatial learning, depression, and

anxiety.7-9

The traditional model of serotonin production within the CNS is that the raphe

nucleus cell bodies in the mid-brain produces serotonin and the afferents innervate and

74

deliver serotonin to the entire CNS.10-12

For many areas of the brain this experimental

evidence follows the traditional model well. Typically, when serotonergic projections to

a specific brain region from the Raphe are ablated serotonin levels in target regions are

depleted.13

However, when projections to the hippocampus are ablated only a 40-60%

reduction in serotonin content is observed, indicating a possible source of serotonin other

than that reported from the median raphe nuclei.13-14

An increasing number of

investigations have implicated the immune system in the function of the hippocampus.15-

18 Interestingly, mast cells are located adjacent to the hippocampus and are a potential

source of serotonin.

Mast cells are hemopoetic cells that are born in the bone marrow and migrate into

various tissues where they mature and are known to cross the blood-brain barrier and

migrate to the CNS of several species including the rat, mouse, dove, vole, and human.19-

21 However, the specific number and distribution of mast cells within the neural tissue

varies significantly between these species. In fact, mast cell population can vary based

on a number of physiological and environmental factors including, handling,22

sexual

behavior,23

isolation,24

pain,25

and stress.26

More than 60 mediators, including histamine,

serotonin, and cytokines, are known to be released by mast cells but the exact chemical

compliment is dependent upon specific tissue environment and type of stimulation.27-29

In recent years, the presence of serotonin within mast cells in a variety of tissues has been

established in animals, including humans.30-32

Serotonin specific antibodies were used to

reveal that mast cells within the CNS contain serotonin, but at an unknown level.33

This

evidence provided the motivation for this study to determine if parenchymal mast cells in

the CNS can contribute to the pool of serotonin of the hippocampus. If this is indeed the

75

case, we ask the questions: How much can this relatively low-abundant cell population

alter the hippocampal milieu?

A mast cell deficient strain of mouse, C57BL/6-KitW-sh/W-sh

(Wsh

/ Wsh

), has been

used extensively as a method to study mast cell biology in vivo. This strain has a

naturally occurring mutation in the white spotting locus, suppressing c-kit receptor

expression and preventing differentiation of mast cells from their progenitor cells.34

Our

collaborators at Columbia University have collected a significant amount of behavioral

and physiological data detailing many differences between the mast cell deficient and

their heterozygous litter mates. Results of staining for brain mast cells in +/+, Wsh

/+, and

Wsh

/Wsh

are seen in Fig. 4-1A.

Wsh

/Wsh

mice appear normal in the numbers of other

hematopoietic cells and in locomotor activity, increasing the confidence that the observed

physiological and behavioral differences are a result of mast cell deficiency.35-36

These

investigations centered on physiological characteristics of the hippocampus and the

behavioral traits known to be mediated by the hippocampus. Physiologically, the deficits

in neurogenesis were profound between the two populations. The area within the

hippocampus where neurogenesis occurs, the dentate gyrus, was 12.9% smaller in area,

while the other major regions within the hippocampus were not significantly different in

size. The only other known area of neurogenesis within the CNS, the subventricular

zone, which contains no mast cells, showed no differences in size or rates of neurogenesis

between the two populations. As Fig. 4-1C illustrates, the number of immature neurons

in the dentate gyrus is significantly lower in the mast cell deficient strain. Behavioral

tests were administered to determine anxiety, spatial memory, and hippocampal learning.

Results showed deficiencies in spatial memory and hippocampal learning as well as

76

increased levels of anxiety. Fig. 4-1D demonstrates the dramatic difference in spatial

memory by use of the Morris water maze. Mast cell deficient mice were slower to learn

the location of the hidden platform as compared to littermates. While administration of

fluoxetine ameliorates the deficit in neurogenesis behavior does not recover - implicating

a more profound developmental role for mast cells in the brain.

We have studied the chemical content of the hippocampus of Wsh

/Wsh

and Wsh

/+

strains for possible metabolomic differences. A laboratory-built instrument hyphenating

capillary electrophoresis separation with electrospray ionization mass spectrometry

detection37

was used to profile the global chemical content of the hippocampal extracts

for statistically significant differences.(Unpublished) Of the 41 metabolites monitored

the 11 analytes that were at statistically lower levels in the mast cell deficient population

were choline, nicotinamide, leucine, hypoxanthine, lysine, met, histidine, tyrosine,

carnosine, glycine, and alanine. The results of these studies suggest that mast cell

signaling has a profound effect of the chemical composition of the hippocampus and

further study is warranted.

The chemical investigations of the hippocampus were initiated to support the

physiological, behavioral, and emotional evidence obtained by our collaborators

describing significant deficits in the mast cell deficient model.35

Our primary goal was to

determine if serotonin is released into the hippocampus upon stimulated release of mast

cells and, if so, how much do these low-abundance cells alter the hippocampal serotonin

level. Quantitative measurements were made using the CE-LINF instrumentation

described in Chapter 2.

77

4.2 Experimental

4.2.1 Mast Cell Deficient Mouse Model

C57BL/6 wild-type (WT) and mast cell deficient Wsh

/Wsh

mice (B6.Cg-KitW-

sh/HNihrJaeBsmJ, C57BL/6 background) were purchased from Jackson Laboratories

(Bar Harbor, ME, USA) and bred to establish colonies at Columbia University animal

facilities. The Wsh

/Wsh

mice were crossed with WT C57BL/6 mice, to generate

heterozygote (Wsh

/+) mice which have mast cells.

4.2.2 Chemicals

Sigma-Aldrich (St. Louis, MO, USA) was the source for all chemicals and

standards, unless otherwise noted. Ultrapure water for buffers, standards, and other uses

was generated by an Elga Purelab water system (U.S. Filter; Lowell, MA, USA).

Methanol was of LC-MS grade (Chromasolve; Sigma Aldrich). Water used for the

preparation of the extraction media was LC/MS grade water (Thermo Scientific,

Waltham, MA, USA). Hank’s balanced salt solution (HBSS) was obtained from

Invitrogen (Carsbad, CA, USA).

4.2.3 Standards and Solutions

Extraction media was made using 49.5% Methanol, 49.5% water, and 1% acetic

acid by volume. Multi-analyte standards were made by dissolving high purity analytes in

extraction media of the same composition as that used for the extraction of hippocampal

tissue. Included analytes were serotonin, tryptophan, 5-hydroxy indole acetic acid (5-

HIAA), and n-acetyl serotonin (NAS). Separation buffer was borate (50 mM, pH 9.5)

78

and was made adding 9.2 g of sodium borate and 3 g of boric acid with 1.0 L ultrapure

water. Sheath-flow solution was citric acid (25 mM, pH 2.25) made by adding 5.25 g

citric acid monohydrate to 1 L ultrapure water.

4.2.4 Hippocampal Tissue Preparation

For each animal, a 300 µm coronal slice containing the rostral (bregma – 1.70

mm) and caudal (bregma - 2.92 mm) hippocampus was cut on a vibratome in ice cold

HBSS. To measure the contribution of mediators of mast cell origin, degranulation was

induced by applying 400 µL of Compound 48/80 (C48/80; Sigma Aldrich, St. Louis,

MO, USA) at a concentration of 0.1 mg/ml in HBSS, while the mast cells contribution at

baseline was examined after application of HBSS alone. The brain slice was incubated

with either C48/80 or the HBSS vehicle for 3 min. Next, the hippocampus was dissected

out and placed into Eppendorf tubes, weighed, and manually homogenized in 20 µl of

extraction matrix per mg of dissected wet tissue. Homogenized tissue was incubated with

the extraction matrix for 90 min at 4°C before centrifugation at 15,000 g for 15 min. The

supernatants were then transferred to PCR tubes and frozen at -80°C until CE-LINF

analysis.

4.2.5 CE-LINF Analysis of Hippocampal Tissue

Each hippocampal extract was analyzed in triplicate using a laboratory-built CE-

LINF instrument equipped with a wavelength-resolved detector described in Chapter 2.

Briefly, ~10 nL of the supernatant was hydrodynamically injected into a fused-silica

capillary and +21 kV applied for separation. Background electrolyte was 50 mM borate

79

and sheath liquid was 25 mM citric acid. Eluting analytes were excited using 264 nm

radiation from a frequency-doubled, argon-ion laser (Coherent, Inc.; Santa Clara, CA,

USA). The fluorescence emission was collected and the spectrum imaged onto liquid

nitrogen cooled CCD array (Princeton Instruments; Trenton, NJ, USA) where it was

recorded at a rate of 2 Hz. An example of the resulting wavelength-resolved

electropherogram is provided in Fig. 4-2.

4.2.6 Data Analysis

Analyte bands were identified by comparison of both migration time and

fluorescence emission characteristics to those contained in the standard. For quantitation,

analytes bands for both samples and standards were quantified by integrating the signal

across both time and wavelength. Standards at concentrations of 1000, 500, 250, 125,

and 62.5 nM were used for the working curves. The mass of serotonin was normalized to

the mass of dissected hippocampal tissue. The signal from tyrosine was normalized to

the standard serotonin signal from the 1000 nM standard as tyrsoine was not originally

included in the multi-anlayte standard set.

Statistical differences in the populations were evaluated by the Kruskal-Wallis

non-parametric analysis using Origin 8.0.38

The non-Gaussian statistical treatment was

chosen because the total number of biological replicates for each population ranged from

5 to 9. More common Gaussian statistics such as ANOVA and the Students t-test rely on

a sufficiently large number of samples to establish a Gaussian distribution. Outlying data

points were removed based on the results of the Q-test. If either of the two sections of

80

hippocampus were found to be a statistical outlier the corresponding data for the

remaining section of hippocampus from the same animal was removed.


Mast cells are found throughout the body and play key roles within the immune

system, but as of yet their specific actions within the CNS has undergone relatively little

investigation. Although, a dramatic report shows that blocking mast cell release during

hypoxia-ischemia decreases brain injury and atrophy in the long term.39

As seen in Fig.

4-1A, mast cells lie within and near the hippocampus. Upon activation, mast cell granules

release their contents over a substantial volume of tissue.35

Thus, following

degranulation, the distribution of granular contents from mast cells lying both within and

nearby the hippocampus can be measured. This paradigm provides a method to study the

levels and distribution of mast cell release within the brain. However, due to complete

degranulation upon activation with compound 48/80, this model represents the highest

levels of analytes that are prepackaged and ready for release at the time of stimulation.

Specifically, here we use this genetic variant to determine the amount of serotonin mast

cells may contribute to the hippocampal milieu.

4.3.1 Serotonin Results

In the rostral hippocampus no significant differences between Wsh

/+ (n=4) and

Wsh

/Wsh

(n=5) mice in baseline levels of serotonin were measured following application

of HBSS vehicle (H1=0.00, p>0.05; Fig. 4-4). Additionally, following the application of

81

C48/80, there were no differences in serotonin content in the rostral hippocampus

between Wsh

/+ (n=7) and Wsh

/Wsh

(n=7) mice (H1=0.10, p>0.05), Fig. 4-4

In the caudal hippocampus, there were no significant differences between Wsh

/+

and Wsh

/Wsh

mice in baseline levels of serotonin measured in HBSS vehicle condition

(H1=0.02, p>0.05;). In contrast, following application of C48/80 to the slices, serotonin

content in Wsh

/+ mice (n=7) was increased compared to their HBSS baseline and to that

of Wsh

/Wsh

(n=8) mice (H1=6.48, p<0.05). Approximately 50% more serotonin was

measured in Wsh

/+ mice compared to Wsh

/Wsh

mice in C48/80 stimulated caudal

hippocampus. This represents a dynamic mast cell-mediated increase in serotonin to the

hippocampus. Interestingly, this increase corresponds to the larger population of mast

cells resident near the caudal vs. rostral hippocampus and indicates that degranulation of

mast cells in brain tissue contributes measurable levels of this neurotransmitter to the

hippocampal milieu.

In an experiment comparing the serotonin levels of the caudal portion of the

hippocampus for WT, heterozygous, and homozygous mice a possible reduction in

serotonin variation in the mast cell deficient mice as compared to the WT strain in non-

degranulated hippocampal slices was observed. Given the high variability of the number

of mast cells the lower variability shown in Fig. 4-3B for the mast cell deficient strain is

not too surprising. However, in the experiments when mast cells were degranulated this

trend was not observed.

82

4.3.2 Tyrosine Results

Levels of tyrosine in Wsh

/+ and Wsh

/Wsh

animals did not change with mast cell

stimulation by C48/80, Fig. 4-4, in either the rostral or caudal sections of the

hippocampus. However, the basal level of histamine between the two populations was

statistically different without regard to stimulation of mast cell release in the caudal

portion, the area of the hippocampus near larger populations of mast cells. Tyrosine is

not known to be synthesized or released from mast cells and the data collected here

illustrates the difference in concentrations between Wsh

/+ and Wsh

/Wsh

is independent of

mast cell stimulation. These results supports the assertion that hippocampal chemical

content is altered beyond direct contributions such as that observed from serotonin which

we have demonstrated here.

4.3.3 Chemical Analysis in Support of Behavioral and Physiological Results

The physiological and behavioral data published along with these results present

compelling evidence that mast cells are integral to normal hippocampal functioning and

development. Deficits in neurogenesis, spatial memory, learning, and anxiety have all

been linked to dentate gyrus function and correspondingly modulated by serotonin

levels.40-41

As has been demonstrated in the corresponding publication to this work,

neurogenesis may recover with selective serotonin reuptake inhibitor, fluoxetine,

administration but behavior is not recovered, suggesting a more fundamental role of mast

cells during development.

While the serotonin results reveal the direct mast cell influence to the

hippocampus, the results of tyrosine measurements indicate possible secondary changes

83

in the chemical content of the hippocampus resulting from mast cell presence. The

difference of tyrosine was confirmed by the CE-ESI-MS metabolomics analysis of these

samples increasing the confidence that measured changes are not likely attributable to

statistical variations. The profound effect the lack of mast cells has on hippocampal

metabolism is surprising considering the mast cell population only makes up less than

0.1% of the total hippocampal cells.

4.4 Conclusions

The analysis of serotonin content of the hippocampus represents the first

quantitative measurement of serotonin contribution to the hippocampus by mast cells.

We have demonstrated that mast cells can contribute significant amounts of serotonin to

the hippocampus when stimulated by compound 48/80 to release stored granules. The

results here add even more evidence that link cells associated with the immune system to

neural functioning.15-17, 42

The observed serotonin increase was specific to the area of the hippocampus

adjacent to the parenchymal mast cell population whereas the serotonin levels in rostral

portion of the hippocampus were not significantly changed upon mast cell stimulation.

This data along with the behavioral and physiological results show a profound effect of

mast cells on both hippocampal functioning and chemical content. Given the relatively

low abundance and variability of the number of mast cells within the brain, such striking

and profound changes will hopefully promote further studies into their role in

neurological functioning and pathologies.

84


This work has demonstrated that mast cells can have a profound influence on the

hippocampus which is integral to many of the most fundamental functions of the brain

including learning, memory, and spatial reasoning even though they represent less than

0.1% of the total number of cells compared to the entire hippocampus. The significant

roles of serotonin, both as a neurotransmitter and trophic factor in the hippocampus, in

mediating mood, neurogenesis, and body temperature require an in-depth understanding,

warranting further investigations. Now that the significant impact mast cells can have to

the hippocampus at the adult stage, more specific questions of mast cells involvement in

the development of the hippocampus is of interest. Further studies should focus on the

additional mediators mast cells could release in paradigms that investigate development

and pathological conditions in which mast cells are known to be activated.

85

4.6 Figures

5 µm

A B

C

Fig. 4-1 Summary of behavioral and physiological results performed by our collaborators. A.) Distribution of mast cells plotted on a

sagittal diagram of the mouse brain. Areas corresponding to the hippocampus are marked in pink with mast cells marked as blue dots.

B.) An individual mast cell stained with touludine blue with the individual granules visible. C.) The dentate gyrus stained with DCX to

visualize immature neurons. D.) Performance comparison in the Morris water maze with the platform both hidden and visible. Mast

cell deficient mice (solid line) show a significantly longer time to escape than mast cell competent mice. Nautiyal, K. M.; Dailey, C. A.;

Jahn, J. L.; Rodriquez, E.; Nguyen, H. S.; Sweedler, J. V.; Silver R. Serotonin of Mast Cell Origin Contributes to Hippocampal

Function. European Journal of Neuroscience 2012, (In Press). Adapted with permission.

D

86

Fig. 4-2 Example of the wavelength-resolved electrophoreograms from the analysis of

hippocampal samples. (a) Serotonin, (b) neutral analyte band, (c) tryptophan, (d) tyrosine,

and (e) 5-hydroxy indole acetic acid.

Wa

ve

len

gth

(n

m)

Time (min)7 8 9 10 11 12 13 14 15 16

300

350

400

450

500

550

600

650

700

a d

c e b

87

Fig. 4-3 A.) Counts of mast cells present in coronal hippocampal slices. B.) Measurement

of serotonin levels in genetically matched wild type (WT), heterozygous (Het), and

homozygous (SASH) littermates.

Serotonin

Unknown 1 and 2

Tryptophan A

B

88

Fig. 4-4 Effects of mast cell degranulation on hippocampal serotonin content in mast cell

deficient Wsh

/Wsh

and Wsh

/+ mice. Increase in serotonin content of the caudal

hippocampus (~50%) in mast cell-competent mice (WSh

/+) upon stimulation correlates

with the co-localization of mast cells to the caudal but not rostral portion of the

hippocampus. Likewise, no increase is observed in the mast cell-deficient mice (WSh

/WSh

)

(p=0.01 using Kruskal–Wallis one-way analysis of variance). Tyrosine, a non-mast cell

related analyte is lower in concentration between the two populations but is not changed

by degranulation. Error bars indicate standard error. Nautiyal, K. M.; Dailey, C. A.;

Jahn, J. L.; Rodriquez, E.; Nguyen, H. S.; Sweedler, J. V.; Silver R. Serotonin of Mast

Cell Origin Contributes to Hippocampal Function. European Journal of Neuroscience

2012,(In Press). Adapted with permission.

89

4.7 References


2. Scoville, W. B.; Milner, B., Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery and Psychiatry 1957, 20 (1), 11-21.

3. Gray, J. A.; McNaughton, N., Comparison between the behavioural effects of septal and hippocampal lesions: a review. Neuroscience and Biobehavioral Reviews 1983, 7 (2), 119-88.

4. Santarelli, L.; Saxe, M.; Gross, C.; Surget, A., et al., Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants. Science 2003, 301 (5634), 805-9.

5. Lauder, J. M.; Krebs, H., Serotonin as a differentiation signal in early neurogenesis. Developmental Neuroscience 1978, 1 (1), 15-30.

6. Altman, H. J.; Normile, H. J., What is the nature of the role of the serotonergic nervous system in learning and memory: prospects for development of an effective treatment strategy for senile dementia. Neurobiology of Aging 1988, 9 (5-6), 627-38.


8. Gould, E.; Woolley, C. S.; McEwen, B. S., Adrenal steroids regulate postnatal development of the rat dentate gyrus: I. Effects of glucocorticoids on cell death. The Journal of Comparative Neurology 1991, 313 (3), 479-85.

9. Benninghoff, J.; Gritti, A.; Rizzi, M.; Lamorte, G., et al., Serotonin depletion hampers survival and proliferation in neurospheres derived from adult neural stem cells. Neuropsychopharmacology 2010, 35 (4), 893-903.

10. Webster, R. A., Neurotransmitters, drugs, and brain function. Wiley: 2001.

11. Sodhi, M. S. K.; Sanders-Bush, E., Serotonin and brain development. 2003; Vol. 59, pp 111-174.

12. Jacobs, B. L.; Azmitia, E. C., Structure and function of the brain serotonin system. Physiological Reviews 1992, 72 (1), 165-230.

90

13. Altman, H. J.; Normile, H. J.; Galloway, M. P.; Ramirez, A., et al., Enhanced spatial discrimination learning in rats following 5,7-DHT-induced serotonergic deafferentation of the hippocampus. Brain Research 1990, 518 (1-2), 61-6.

14. Zhou, F. C.; Azmitia, E. C., Effects of 5,7-dihydroxytryptamine on HRP retrograde transport from hippocampus to midbrain raphe nuclei in the rat. Brain Research Bulletin 1983, 10 (4), 445-51.

15. Raison, C. L.; Capuron, L.; Miller, A. H., Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends in Immunology 2006, 27 (1), 24-31.

16. Ziv, Y.; Ron, N.; Butovsky, O.; Landa, G., et al., Immune cells contribute to the maintenance of neurogenesis and spatial learning abilities in adulthood. Nature Neuroscience 2006, 9 (2), 268-75.

17. Goshen, I.; Kreisel, T.; Ben-Menachem-Zidon, O.; Licht, T., et al., Brain interleukin-1 mediates chronic stress-induced depression in mice via adrenocortical activation and hippocampal neurogenesis suppression. Molecular Psychiatry 2008, 13 (7), 717-28.

18. Koo, J. W.; Duman, R. S., IL-1beta is an essential mediator of the antineurogenic and anhedonic effects of stress. Proc Natl Acad Sci U S A 2008, 105 (2), 751-6.

19. Zhuang, X.; Silverman, A. J.; Silver, R., Reproductive behavior, endocrine state, and the distribution of GnRH-like immunoreactive mast cells in dove brain. Hormones and Behavior 1993, 27 (3), 283-95.

20. Dropp, J. J., Mast cells in mammalian brain. ACTA Anatomica (Basel) 1976, 94 (1), 1-21.

21. Dropp, J. J., Mast cells in the human brain. ACTA Anatomica (Basel) 1979, 105 (4), 505-13.

22. Persinger, M. A., Handling factors not body marking influence thalamic mast cell numbers in the preweaned albino rat. Behavioral and Neural Biology 1980, 30 (4), 448-459.

23. Silver, R.; Zhuang, X.; Silverman, A. J., Immunocompetence, mast cells and sexual behaviour. Ibis 1996, 138 (1), 101-111.

24. Bugajski, A. J.; Chlap, Z.; Gadek-Michalska, A.; Bugajski, J., Effect of isolation stress on brain mast cells and brain histamine levels in rats. Agents and Actions 1994, 41 (SPEC. ISS.), C75-C76.

25. Xanthos, D. N.; Gaderer, S.; Drdla, R.; Nuro, E., et al., Central nervous system mast cells in peripheral inflammatory nociception. Molecular Pain 2011, 7.

91

26. Cirulli, F.; Pistillo, L.; De Acetis, L.; Alleva, E., et al., Increased number of mast cells in the central nervous system of adult male mice following chronic subordination stress. Brain, Behavior, and Immunity 1998, 12 (2), 123-133.

27. Dvorak, A. M.; McLeod, R. S.; Onderdonk, A.; Monahan-Earley, R. A., et al., Ultrastructural evidence for piecemeal and anaphylactic degranulation of human gut mucosal mast cells in vivo. Int Arch Allergy Immunol 1992, 99 (1), 74-83.

28. Kraeuter Kops, S.; Theoharides, T. C.; Cronin, C. T.; Kashgarian, M. G., et al., Ultrastructural characteristics of rat peritoneal mast cells undergoing differential release of serotonin without histamine and without degranulation. Cell Tissue Res 1990, 262 (3), 415-24.

29. Marshall, J. S., Mast-cell responses to pathogens. Nat Rev Immunol 2004, 4 (10), 787-99.

30. Kushnir-Sukhov, N. M.; Brown, J. M.; Wu, Y.; Kirshenbaum, A., et al., Human mast cells are capable of serotonin synthesis and release. Journal of Allergy and Clinical Immunology 2007, 119 (2), 498-9.

31. Ringvall, M.; Ronnberg, E.; Wernersson, S.; Duelli, A., et al., Serotonin and histamine storage in mast cell secretory granules is dependent on serglycin proteoglycan. Journal of Allergy and Clinical Immunology 2008, 121 (4), 1020-6.

32. Marathias, K.; Lambracht-Hall, M.; Savala, J.; Theoharides, T. C., Endogenous regulation of rat brain mast cell serotonin release. International Archives of Allergy and Applied Immunology 1991, 95 (4), 332-40.

33. Kovács, P.; Hernádi, I.; Wilhelm, M., Mast cells modulate maintained neuronal activity in the thalamus in vivo. Journal of Neuroimmunology 2006, 171 (1-2), 1-7.

34. Duttlinger, R.; Manova, K.; Chu, T. Y.; Gyssler, C., et al., W-sash affects positive and negative elements controlling c-kit expression: ectopic c-kit expression at sites of kit-ligand expression affects melanogenesis. Development 1993, 118 (3), 705-17.

35. Nautiyal, K. M.; Ribeiro, A. C.; Pfaff, D. W.; Silver, R., Brain mast cells link the immune system to anxiety-like behavior. Proceedings of the National Academy of Sciences 2008, 105 (46), 18053-18057.

36. Galli, S. J.; Grimbaldeston, M.; Tsai, M., Immunomodulatory mast cells: negative, as well as positive, regulators of immunity. Nature Reviews Immunology 2008, 8 (6), 478-86.

92


38. Kruskal, W. H.; Wallis, W. A., Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association 1987, (40), 20-20.

39. Jin, Y.; Silverman, A. J.; Vannucci, S. J., Mast cells are early responders after hypoxia-ischemia in immature rat brain. Stroke; a journal of cerebral circulation 2009, 40 (9), 3107-12.

40. Djavadian, R. L., Serotonin and neurogenesis in the hippocampal dentate gyrus of adult mammals. Acta Neurobiologiae Experimentalis 2004, 64 (2), 189-200.

41. Richter-Levin, G.; Segal, M., Serotonin, aging and cognitive functions of the hippocampus. Reviews in the Neurosciences 1996, 7 (2), 103-113.

42. Rook, G. A.; Lowry, C. A., The hygiene hypothesis and psychiatric disorders. Trends in Immunology 2008, 29 (4), 150-8.

93

Chapter 5

Detection of Kynurenine and Kynurenic Acid Using Wavelength-

Resolved Capillary Electrophoresis


This chapter focuses on the extension of the system described in Chapter 2 to

include an increased number of tryptophan metabolites. I would like to thank Dr. Peter

Yau at the Biotechnology Center for his discussions about tissue extraction protocols.

Funding for this work was provided by National Institute of Neurological Disease and

Stroke through NS031609 and the National Institute of Dental and Craniofacial Research

through DE018866.

5.1 Introduction

Tryptophan is an essential amino acid used in protein synthesis and is also

involved in several important metabolic pathways. As outlined in prior chapters these

include the synthesis of serotonin. The majority, more than 90%, of tryptophan

metabolism in mammals is through the kynurenine pathway (KP).1 Within this pathway

oxidation of tryptophan occurs by one of two enzymes: tryptophan 2,3-dioxygenase2

(TDO) primarily located in the liver and indoleamine 2,3-dioxygenase3 (IDO) which is

found in rest of the body, including the central nervous system (CNS).4-6

As tryptophan

is an essential amino acid, the total, available pool is limited to dietary intake, making KP

metabolism interesting, not only for the direct metabolites it produces, but the potential

secondary effects on other metabolic pathways based on changes in precursor availability

such as the synthesis of serotonin.7 Even though serotonin production represents only a

94

small fraction of the total tryptophan metabolism it has undergone significant research,8-11

while methods for KP metabolites have undergone limited development.

The downstream metabolites include the neuroactive compounds kynurenine,

kynurenic acid, and quinolicic acid. These metabolites have both neuroprotective and

neurotoxic actions on the brain.5 The first stable metabolite, kynurenine, has not been

shown to have a direct action on neurons, but there is some evidence of signaling for

nerve growth factor production from glial cells.12-13

Kynurenic acid is formed from

kynurenine by the enzyme kynurenine aminotransferase14

(KAT) which is preferentially

localized in glial cells and exhibits anticonvulsant and neuroprotective properties.15

Kynurenine not converted to kynurenic acid continues along the metabolic pathway to

produce nicotinamide adenine dinucleotide (NAD). One neuroactive metabolite in that

branch, quinolinic acid, is an N-Methyl-D-aspartate (NMDA) receptor agonist and can be

excitotoxic to these neurons.16

Depending on the expression of these enzymes in local

environments tryptophan metabolites can be either neuroprotective or neurotoxic.

Pro-inflammatory cytokines can induce or suppress expression of IDO,

dramatically changing the fate of tryptophan in local environments.17

For example,

expression of IDO has been shown to be upregulated by IFN-α, IFN-γ, and TNF-α.18

As

these cytokines are regulated by the immune system IDO expression is largely controlled

by the immune system,19

linking the biochemical content of the brain to the activity of the

immune system.20-21

In fact, it is interesting to note that SSRIs and SSNRIs have been

shown to target proinflammatory cytokines known to reduce neurogenesis.22-23

The

relationship between specific KP metabolites and numerous pathologies of the CNS are

reviewed by Stone (and illustrated in Figure 5-1).24

Briefly, KP metabolites deviate from

95

homeostatic levels in neurodegenerative diseases including Huntington’s, Alzheimer’s,

and multiple sclerosis as well as chronic infections such as Lyme disease, malaria, and

AIDS.25

Activation of the immune system is correlated in large changes in KP

metabolites in blood resulting from trauma ranging from a chronic brain injury26

to

hypoxic ischemia.27

With neurogenesis being related to physiological symptoms of

depression, this further supports the inflammatory theory of depression.16

The impact of

KP metabolites on the CNS has created interest in developing pharmaceutical-based

therapies.24, 28-29

Current methods for the detection and quantitation of KP metabolites have

focused on analysis of the biofluids plasma/serum, cerebrospinal fluid (CSF), and urine

as outlined in Table 5-1. The most common technique for clinical research has been

HPLC with ultraviolet (UV) absorbance or fluorescence detection.30-31

These techniques

have shown a complex relationship between the metabolism of tryptophan and numerous

diseases. Many of these changes only correlate with pathologies. However, more

detailed, mechanistic investigations need sufficiently sensitive methods capable of

measuring these levels from tissue. This has been partially addressed by the analysis of

microdialysates to measure in vivo levels.32

While these prior studies used LC

techniques, investigations using CE have been limited. CE would allow volume-limited

samples to be analyzed, aiding the analysis of tissue samples from the CNS. The

reported CE methods have detection limits in the low nM range, competitive with the

HPLC methods.

Here we extend our CE with wavelength resolved LINF detection33

to include

detection of KP metabolites kynurenine and kynurenic acid. The work includes

96

investigations to optimize the fluorescence yield using either Zn2+

or nonaqueous buffers

to improve sensitivity by increasing the fluorescence yield.

5.2 Experimental

5.2.1 Chemicals, Solutions, and Materials



from Alfa Aeser (Ward Hill, MA, USA). Water for solutions and standards was obtained

from an Elga PureLab Prima water filtration system (Woodridge, IL, USA) at purity of

18.2 MΩ.

Extraction media consisted of 49.5/49.5/1, methanol/water/glacial acetic acid

(99%) by volume. Standards were weighed (1-2 mg) on a microbalance (Mettler Toledo;

Columbus, OH, USA) and dissolved in extraction media for high concentration stock

solutions. These stocks were stored at -80°C until use for dilution to working

concentrations. Borate buffer was prepared at 50 mM at a pH of 9.5 and citric acid was

prepared at 25 mM at a pH of 2.5. Both the citric acid and borate buffers were filtered

using a 0.22 µm vacuum filter and degassed by maintaining the vacuum for 20 min while

stirring.

5.2.2 Absorption spectra

Because many of the analytes in the KP are not spectroscopically characterized,

we obtained absorption spectra to determine if detection is possible with LINF at the

97

available wavelengths of 264 or 229 nm. Absorption spectra were obtained using

standards dissolved in 50 mM borate at a concentration of 1 µM.

5.2.3 Capillary Electrophoresis Analysis

A laboratory constructed CE-LINF system was used to perform all measurements

as described in Chapter 2 with modifications detailed below.33

The CE separations were

performed using 50 mM borate (applied voltage +21 kV) and 25 mM citric acid used in

the sheath flow. Approximately 10 nL of sample was hydrodynamically injection into

the capillary by lowering the outlet for 30 s.

Limits of detection reported in Table 5-3 were defined as the concentration of

analyte with a signal-to-noise ratio of 3 with the signal as the peak fluorescence intensity

of the particular analyte and the noise as the standard deviation of the background

immediately preceding the peak from the extracted electropherogram.

5.2.4 Modifications for Non-Aqueous Solvents

High Purity perfluoro alkoxy alkane (PFA) (PN1640; Upchurch Scientific; Oak

Harbor, WA, USA) tubing replaced the previous Tygon® tubing for non-aqueous sheath-

flow or separations. The metering valve used to regulate the sheath-flow rate was

replaced with a newer model using Teflon for all liquid contacting components (Cole-

Parmer; EW06394-00; Vernon Hills, IL, USA).

98


Of the tryptophan metabolites outlined in Fig. 5-1, serotonin, tryptophan,

tryptamine, NAS, melatonin, HIAA, kynurenine, kynurenic acid, NAD, NADPH are all

detectable our by CE-LINF system. These compounds represent the majority of the

known, stable, and bioactive metabolites within the tryptophan metabolic pathway. The

available methods to date that have focused on measurement of KP metabolites have

primarily been applied to the analysis of biofluids – serum, plasma, urine, and CSF with

few techniques developed for their measurement within tissues.4 Table 5-1 summarizes

many of the methods used for measuring KP metabolites and includes the reported

detection limits. The most widely used separation method is HPLC which has been

largely used for clinical investigations. With the ever increasing evidence of the effects

these metabolites have on the CNS, microscale methods compatible with samples of

limited volume that are typical in neuroscience studies will be necessary. While a limited

number of CE methods have been developed which have competitive sensitivities, their

more wide spread use has been limited. These methods have used a number of detection

methods including mass spectrometry,34

UV absorption,35

fluorescence,36

and

electrochemical32, 37

with detection limits in the low nM range. The primary motivation

to use CE in this work is to take advantage of its compatibility with low-volume samples

to measure KP metabolites from small brain regions which would be difficult in the more

traditional HPLC systems.

Our method using a separation buffer of 50 mM borate and a sheath liquid of 25

mM citric acid resulted in detection limits that are competitive with other HPLC and CE

techniques reported in Table 5-1. Results from 1 µM standards are presented in Fig. 5-2

99

and Table 5-1. The limits of detection were 50 nM for kynurenine and 15 nM for

kynurenic acid (Table 5-3). Reports of kynurenic acid levels in various areas of the brain

have been previously reported to be between 18 and 29 pmol per gram of tissue, with the

highest the concentration found in the brainstem Table 5-2. Even at an extraction ratio of

1 µL of media to 1 mg of tissue, which is very low, results would be at 28.9 nM

concentration sample for the brainstem, below the detection limits using this standard

protocol. To accomplish detection within tissue the sensitivity of the method needs to be

improved or approaches that concentrate the KP products from larger samples into a

small enough volume to allow for detection developed.

As the LODs of our current method were not sufficient for tissue analysis, we

investigated two methods to increase the fluorescence quantum yield of kynurenine and

kynurenic acid - non-aqueous separation and detection conditions as well as addition of

Zn2+

. Non-aqueous separation and detection conditions were based on a thorough report

of the excited-state dynamics of kynurenine by Sherin et al.38

Their study showed a

significant increase in fluorescence quantum yield in non-aqueous over aqueous solvents.

Reported increases were one order of magnitude for kynurenine dissolved in methanol

and two orders of magnitude for dimethyl sulfoxide as the solvent as compared to water.

The advantage of our sheath-flow setup is the ability to decouple separation and detection

conditions. A first choice test was to add non-aqueous solvents to the sheath-flow as the

separation conditions were already developed. Ultrapure solvents that were used as

sheath-flow liquid included dimethyl sulfoxide, ethanol, methanol, and acetonitrile. All

of these solvents resulted in significant background when using 264 nm light as the

100

excitation source. After replacing all tubing with more the chemically resistant PFA

tubing, the background was improved, but still overwhelmed the CCD detector.

Zn2+

has been shown to increase the fluorescence yield from KA.39-40

However,

the increase in fluorescence is dependent on pH, with significant improvements in

florescence above a pH of 6. An improvement of an order of magnitude is reported at a

pH of 6.75. Here, A 100 mM zinc acetate solution for the sheath-flow conditions was

used, but at pH conditions above 5, where increased fluorescence occurs, a precipitate

formed on the capillary outlet, presumably zinc hydroxide, which disrupted excitation

and eventually obstructed the capillary completely.

5.4 Conclusions

Here we have demonstrated that the CE-LINF instrumentation and method

described in Chapter 2 is able to be expanded to include a broader range of tryptophan

metabolism which include the kynurenine pathway metabolites kynurenine and

kynureneic acid. With the increasing interest and evidence of significant roles in

connecting the brain with the immune system, new methods are needed for their analysis.

The detection limits are comparable with those already reported and included in Table 5-

1, but the trace levels found in tissue make analysis difficult and even more sensitive

methods are needed to expand this area of research.


Insights into the profound influence of the immune system on the brain through

the metabolites of tryptophan via the KP is in the process of rapid expansion. It is my

101

viewpoint that significant medical therapies will evolve from the mediation of the KP.

Greater understanding of the mechanisms by which this pathway acts, the better targeted

these new therapies can be and those studies will require highly sensitive analytical

techniques. While the performance demonstrated here is impressive, it is not yet

sensitive enough for detection from tissue at endogenous concentrations. From the

increase in fluorescence yield reported by Sherin et al. efforts to use native fluorescence

in non-aqueous solvents should be continued, but at longer wavelengths, ~350 nm, which

is another absorption peak for kynurenine metabolites, Fig. 5-3. This may allow the use

of non-aqueous solvents to increase fluorescence while limiting the excess background

observed by the current method at 264 nm. The more traditional option for increasing the

fluorescent yield of these metabolites is derivatization with a fluorophore. An example of

derivatization with kynurenine metabolites was demonstrated by Mitsuhashi et al.41

These methods should be investigated given the therapeutic potential of KP metabolits.

102


Separation Detection Method

Analytes LOD Matrix Reference

HPLC Tandem Mass

Spec

Trp 30 nM Human Plasma

42 Kyn 1 nM

3-HK 5 nM

HPLC UV Trp 8 nmol Human

Plasma 30

Kyn 6 nmol

HPLC UV and

Fluorescence Trp 7 mmol Human

Serum 31

Kyn 2 nmol

HPLC UV

Trp 134 nM Human Plasma

43 Kyn 16 nM

5-HT 2 nM

HPLC MS KynA 200 fmol Blood – Mouse

44

HPLC Tandem Mass

MS

Trp 17.6 nM

Rat Plasma 45 Kyn 17.3 nM

KynA 17.5 nM

QA 48.2 nM

CE MS

Trp 20 nM

CSF 34 Kyn 20 nM

KynA 20 nM

CE LIF KA 1 nM CSF 36

CE UV Trp 400nM Human

Plasma 35

Kyn 150 nM

CE EC

Trp 4.8 amol

Brain Dialysate

32

Kyn 3.1 amol

3-HKyn 0.4 amol

Anth. Acid 3.3 amol

KynA 22.2 amol

XA 0.6 amol

CE-MEKC AD

3-HK 7.42 nM

Urine 37

5-HTP 5.18 nM

Kyn 34.6 nM

Trp 3.99 nM

5-HIAA 15.1 nM

XA 12.7 nM

5-HT 6.72 nM

Trp 8.01 nM

Table 5-1 Quantitative methods have been limited in development with the

majority of methods targeting biofluids (serum, plasma, CSF, and urine).

103

Kynurenic Acid inTissues

Region pmol/g Tissue wet Weight4

fmol/mg protein39

Cortex 18.1 32.5

Hippocampus 27.1 54.9

Cerebellum 17.9 63.6

Brain Stem 28.9 149

Striatum 23.2 51.6

Biofluids

Kynurenic Acid Kynurenine

Plasma40 24.7 nmol/L 2.2umol/L

CSF39 5.09

Analyte LOD264 nm (nM)

Tryptophan 5.0

Serotonin 1

5-HIAA 50

Kynurenine 50

Kynurenic Acid 16

Table 5-2 While tissue quantitation has undergone limited

investigation, a few studies have provided insight to the target

values for kynurenic acid in various parts of the brain.

Table 5-3 Detection limits for major

tryptophan metaoblites using 50 mM

borate separation buffer and sheath-flow

liquid using the wavelength-resolved CE

system.

104

Fig. 5-1 Scheme of tryptophan metabolism in mammalian biology.46-47

The majority of tryptophan is metabolized by IDO or

TDO into the kynurenine pathway. 1, 47

105

Wav

elen

gth

(nm

)

Time (min)

6 7 8 9 10 11 12 13 14

300

350

400

450

500

550

600

650

700

Serotonin Tryptophan

Kynurenine

5-Hydroxyindoleacetic acid

Kynurenic Acid

Sulforhodamine 101

Fig. 5-2 Wavelength-resolved electropherogram of the major, natively fluorescence

tryptophan metabolites. Each analyte is at a concentration of 1 µM except

sulforhodamine 101 which at a concentration of 20 nM.

106

200 250 300 350 400

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Absorb

ance (

A.U

.)

Wavelength (nm)

Tryptophan

Kynurenic Acid

Serotonin

Kynurenine

Fig. 5-3 Absorbance spectra for the metabolites investigated. While

serotonin and tryptophan are optimal in the 250-300 nm after the ring

rearrangement by IDO 350 nm might be applicable.

107

5.7 References

1. Wolf, H., Studies on tryptophan metabolism in man. The effect of hormones and vitamin B6 on urinary excretion of metabolites of the kynurenine pathway. Scandinavian Journal of Clinical and Laboratory Investigation 1974, 33 (sup136).

2. Ren, S.; Correia, M. A., Heme: A regulator of rat hepatic tryptophan 2,3-dioxygenase? Archives of Biochemistry and Biophysics 2000, 377 (1), 195-203.

3. Ball, H. J.; Yuasa, H. J.; Austin, C. J. D.; Weiser, S., et al., Indoleamine 2,3-dioxygenase-2; a new enzyme in the kynurenine pathway. International Journal of Biochemistry and Cell Biology 2009, 41 (3), 467-471.

4. Moroni, F.; Russi, P.; Lombardi, G.; Beni, M., et al., Presence of kynurenic acid in the mammalian brain. Journal of Neurochemistry 1988, 51 (1), 177-180.

5. Moroni, F., Tryptophan metabolism and brain function: Focus on kynurenine and other indole metabolites. European Journal of Pharmacology 1999, 375 (1-3), 87-100.

6. Kwidzinski, E.; Bechmann, I., IDO expression in the brain: a double-edged sword. Journal of Molecular Medicine 2007, 85 (12), 1351-1359.

7. Widner, B.; Laich, A.; Sperner-Unterweger, B.; Ledochowski, M., et al., Neopterin production, tryptophan degradation, and mental depression - What is the link? Brain, Behavior, and Immunity 2002, 16 (5), 590-595.

8. Jacobs, B. L.; Azmitia, E. C., Structure and function of the brain serotonin system. Physiological Reviews 1992, 72 (1), 165-230.

9. Sternbach, H., The serotonin syndrome. American Journal of Psychiatry 1991, 148 (6), 705-713.


11. Barnes, N. M.; Sharp, T., A review of central 5-HT receptors and their function. Neuropharmacology 1999, 38 (8), 1083-1152.

12. Dong-Ryul, L.; Kondo, H.; Furukawa, S.; Nakano, K., Stimulation of NGF production by tryptophan and its metabolites in cultured mouse astroglial cells. Brain Research 1997, 777 (1-2), 228-230.

108

13. Dong-Ruyl, L.; Sawada, M.; Nakano, K., Tryptophan and its metabolite, kynurenine, stimulate expression of nerve growth factor in cultured mouse astroglial cells. Neuroscience Letters 1998, 244 (1), 17-20.

14. Han, Q.; Cai, T.; Tagle, D. A.; Li, J., Structure, expression, and function of kynurenine aminotransferases in human and rodent brains. Cellular and Molecular Life Sciences 2010, 67 (3), 353-368.

15. Ceresoli-Borroni, G.; Guidetti, P.; Schwarcz, R., Acute and chronic changes in kynurenate formation following an intrastriatal quinolinate injection in rats. Journal of Neural Transmission 1999, 106 (3-4), 229-242.

16. McNally, L.; Bhagwagar, Z.; Hannestad, J., Inflammation, glutamate, and glia in depression: A literature review. Cns Spectrums 2008, 13 (6), 501-510.

17. Hosseini-Tabatabaei, A.; Poormasjedi-Meibod, M. S.; Jalili, R. B.; Ghahary, A., Immunomodulatory role of IDO in physiological and pathological conditions. Current Trends in Immunology 2011, 11, 51-63.

18. Capuron, L.; Miller, A. H., Immune system to brain signaling: Neuropsychopharmacological implications. Pharmacology & Therapeutics 2011, 130 (2), 226-238.

19. Moffett, J. R.; Namboodiri, M. A., Tryptophan and the immune response. Immunology and Cell Biology 2003, 81 (4), 247-265.

20. Yuan, W.; Collado-Hidalgo, A.; Yufit, T.; Taylor, M., et al., Modulation of cellular tryptophan metabolism in human fibroblasts by transforming growth factor-β: Selective inhibition of indoleamine 2,3- dioxygenase and tryptophanyl-tRNA synthetase gene expression. Journal of Cellular Physiology 1998, 177 (1), 174-186.

21. Musso, T.; Gusella, G. L.; Brooks, A.; Longo, D. L., et al., Interleukin-4 inhibits indoleamine 2,3-dioxygenase expression in human monocytes. Blood 1994, 83 (5), 1408-1411.

22. Basterzi, A. D.; Aydemir, Ç.; Kisa, C.; Aksaray, S., et al., IL-6 levels decrease with SSRI treatment in patients with major depression. Human Psychopharmacology 2005, 20 (7), 473-476.

23. Tsao, C. W.; Lin, Y. S.; Chen, C. C.; Bai, C. H., et al., Cytokines and serotonin transporter in patients with major depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry 2006, 30 (5), 899-905.

24. Stone, T. W., Kynurenines in the CNS: From endogenous obscurity to therapeutic importance. Progress in Neurobiology 2001, 64 (2), 185-218.

109

25. Baig, S.; Halawa, I.; Qureshi, G. A., High performance liquid chromatography as a tool in the definition of abnormalities in monoamine and tryptophan metabolites in cerebrospinal fluid from patients with neurological disorders. Biomedical Chromatography 1991, 5 (3), 108-112.

26. Mackay, G. M.; Forrest, C. M.; Stoy, N.; Christofides, J., et al., Tryptophan metabolism and oxidative stress in patients with chronic brain injury. European Journal of Neurology 2006, 13 (1), 30-42.

27. Jin, Y.; Silverman, A. J.; Vannucci, S. J., Mast cells are early responders after hypoxia-ischemia in immature rat brain. Stroke; a journal of cerebral circulation 2009, 40 (9), 3107-12.

28. Schwarcz, R.; Pellicciari, R., Manipulation of brain kynurenines: Glial targets, neuronal effects, and clinical opportunities. Journal of Pharmacology and Experimental Therapeutics 2002, 303 (1), 1-10.

29. Stone, T. W.; Darlington, L. G., Endogenous kynurenines as targets for drug discovery and development. Nature Reviews Drug Discovery 2002, 1 (8), 609.

30. Kawai, K.; Ishikawa, H.; Ohashi, K.; Itoh, Y., et al., Rapid, simple and simultaneous measurement of kynurenine and tryptophan in plasma by column switching-HPLC method. International Congress Series 2007, 1304, 415-419.

31. Vignau, J.; Jacquemont, M. C.; Lefort, A.; Imbenotte, M., et al., Simultaneous determination of tryptophan and kynurenine in serum by HPLC with UV and fluorescence detection. Biomedical Chromatography 2004, 18 (10), 872-874.

32. Malone, M. A.; Zuo, H.; Lunte, S. M.; Smyth, M. R., Determination of tryptophan and kynurenine in brain microdialysis samples by capillary electrophoresis with electrochemical detection. Journal of Chromatography A 1995, 700 (1-2), 73-80.


34. Arvidsson, B.; Johannesson, N.; Citterio, A.; Righetti, P. G., et al., High throughput analysis of tryptophan metabolites in a complex matrix using capillary electrophoresis coupled to time-of-flight mass spectrometry. Journal of Chromatography A 2007, 1159 (1-2), 154-158.

35. Zinellu, A.; Sotgia, S.; Deiana, L.; Talanas, G., et al., Simultaneous analysis of kynurenine and tryptophan in human plasma by capillary electrophoresis with UV detection. Journal of Separation Science 2012, 35 (9), 1146-1151.

110

36. Hansen, D. K.; Lunte, S. M., Determination of kynurenic acid by capillary electrophoresis with laser-induced fluorescence detection. Journal of Chromatography A 1997, 781 (1-2), 81-89.

37. Wang, W.; Qiu, B.; Xu, X.; Zhang, L., et al., Separation and determination of L-tryptophan and its metabolites by capillary micellar electrokinetic chromatography with amperometric detection. Electrophoresis 2005, 26 (4-5), 903-910.

38. Sherin, P. S.; Grilj, J.; Tsentalovich, Y. P.; Vauthey, E., Ultrafast excited-state dynamics of kynurenine, a UV filter of the human eye. Journal of Physical Chemistry B 2009, 113 (14), 4953-4962.

39. Swartz, K. J.; Matson, W. R.; MacGarvey, U.; Ryan, E. A., et al., Measurement of kynurenic acid in mammalian brain extracts and cerebrospinal fluid by high-performance liquid chromatography with fluorometric and coulometric electrode array detection. Analytical Biochemistry 1990, 185 (2), 363-376.

40. Zhao, J.; Gao, P.; Zhu, D., Optimization of Zn2+-containing mobile phase for simultaneous determination of kynurenine, kynurenic acid and tryptophan in human plasma by high performance liquid chromatography. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 2010, 878 (5-6), 603-608.

41. Mitsuhashi, S.; Fukushima, T.; Tomiya, M.; Santa, T., et al., Determination of kynurenine levels in rat plasma by high-performance liquid chromatography with pre-column fluorescence derivatization. Analytica Chimica Acta 2007, 584 (2), 315-321.

42. de Jong, W. H. A.; Smit, R.; Bakker, S. J. L.; de Vries, E. G. E., et al., Plasma tryptophan, kynurenine and 3-hydroxykynurenine measurement using automated on-line solid-phase extraction HPLC-tandem mass spectrometry. Journal of Chromatography B 2009, 877 (7), 603-609.

43. Zhen, Q.; Xu, B.; Ma, L.; Tian, G., et al., Simultaneous determination of tryptophan, kynurenine and 5-hydroxytryptamine by HPLC: Application in uremic patients undergoing hemodialysis. Clinical Biochemistry 2011, 44 (2-3), 226-230.

44. Zádori, D.; Ilisz, I.; Klivényi, P.; Szatmári, I., et al., Time-course of kynurenic acid concentration in mouse serum following the administration of a novel kynurenic acid analog. Journal of Pharmaceutical and Biomedical Analysis 2011, 55 (3), 540-543.

45. Möller, M.; Du Preez, J. L.; Harvey, B. H., Development and validation of a single analytical method for the determination of tryptophan, and its kynurenine

111

metabolites in rat plasma. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 2012, 898, 121-129.

46. Jones, R. S. G., Tryptamine: a neuromodulator or neurotransmitter in mammalian brain? Progress in Neurobiology 1982, 19 (1-2), 117-139.

47. Nemeth, H.; Toldi, J.; Vecsei, L., Role of kynurenines in the central and peripherial nervous systems. Current Neurovascular Research 2005, 2 (3), 249-260.

112

Chapter 6

Investigation of Basal Phyla for Presence of Classical Signaling

Molecules


The following chapter was written in collaboration with Prof. Leonid Moroz from

the University of Florida and is presented here as a part of a larger effort that includes

genomic, transcriptomic, and peptidomic methods to accurately describe the phylogeny

of Ctenophora. Prof. Moroz initiated this project with his genome sequencing efforts of

the Ctenophores and kindly provided the animals and guidance on dissection procedures.

Our work is intended to provide a functional perspective on the actual presence of

specific small-molecule transmitters. Dr. Peter Nemes and Jordan Aerts assisted with the

CE-ESI-MS analysis. We gratefully acknowledge the support of NS031609 and NSF

CHE 11-11705 for the instrumentation and work performed at UIUC in the course of

these experiments.

6.1 Introduction

The evolution of a central nervous system (CNS) was the corner stone for the

occurrence of animal development. Cell-to-cell signaling in the CNS neurons and

between neurons and other cell types allows the synchronization of activity and function,

thus allowing coordinated behaviors in complex, multicellular organisms. These nervous

systems have their origins in early life with presynaptic proteins present in unicellular

organisms.1 The theoretical, although still not completely described, common ancestor

of most higher order animals is the now extinct Urbilateria, which would have diverged

113

around 600 million years ago.2 The evolution of neurons and the centralized nervous

systems prior to this divergence are still under investigation without a predominate

theory having been accepted.3 The evolutionary theories for the occurrence of complex,

centralized nervous systems can be categorized into two broad concepts: monophyletic

and polyphyletic in origin.4 In brief, a consensus has not been agreed upon if the CNS

present in higher order life resulted from a single, ancient genome (monophyletic) or if it

was a confluence of multiple evolutionary lineages (polyphyletic).

Traditional evolutionary theories were based on the observation of morphological

characteristics to determine phylogenic relationships. Insights from molecular biology

have changed the landscape in understanding evolution based on gene networks, a

concept termed evolutionary developmental biology (Evo-Devo).4-6

The genes that

control body patterning, development, and differentiation of the nervous system are more

conserved across much of animal life than previously realized.7-9

This new insight for

studying the evolutionary taxonomy substantially changed phylogenic relationships,

particularly among basal phyla based on revelations in genomic similarities.6, 10-11

Cnidaria and Ctenophora have been moved to sister phyla instead of basal to Bilateria.

Just as genetic conservation provides insight into evolution, so does gene loss. Many of

the basic chemical signaling pathways and their synthetic pathways such as serotonin are

well conserved from these early organisms, while others appear to have been lost. Not

all evolution leads to increased complexity; examples such as vestigial structures show

decreased complexity/simplifications can occur.

One of the tools in understanding the evolution of signaling pathways is to study

these ancient nervous systems of basal species.12

While the CNS of Bilateria have been

114

studied extensively, information about systems present in more basal taxa is limited.13

Ctenophores, also known as comb jellies, are primitive animals and represent one of the

earliest occurrences of a differentiated nervous system with synapses. These animals

diverged prior to the occurrence of Bilateria. In fact, it is one of the most basally

branching lineages of metazoan life, representing the early emergence of a neural system.

However, the exact placement of Ctenophores along with Cnidaria and Porifera as sister

phyla to Bilateria is largely unresolved due to a general lack of genomic and molecular

studies on the species within these phyla.5, 8, 13-15

One interesting genus of the Ctenophore phylum is the Pleurobrachia bachei,

many of which are bioluminescent.16

These animals use a rudimentary, diffuse nervous

system which is not centralized and is best described as a neural net.17

However, even

without the advanced organization seen in higher species it can still feed, reproduce, and

defend itself. Immunofluorescent staining techniques have shown that Ctenophore

neurons are both neuropeptidergic and heterogeneous, implying an interesting level of

complexity for such a relatively simple system.18

These animals are one of the few major

animals prior to Bilateria divergence without significant genomic studies available.19

While genetic studies have redefined much of Bilateria it has not been successful in

resolving the taxonomy of basal phyla – Ctenophores, Cnidaria, and Poriferans.

The metabolism of tryptophan into serotonin (5-hydroxytryptamine or 5-HT) is a

well-conserved pathway originating in unicellular organisms around 3 billion years ago.20

Its early appearance allowed its near ubiquitous use in life and eventually as a cell-to-cell

signaling molecule along with its corresponding receptors.21

The first appearance of the

G protein-coupled 5-HT receptor was more than 750 million years ago, demonstrating the

115

evolutionarily early use of 5-HT as a signaling molecule.22

In fact, there is strong

evidence that the first 5-HT receptor was the basis for the biogenic amine receptor system

in general.22-23

Given the molecules pervasive presence throughout both plant and animal

life, genomic homologies in these signaling pathways can help inform about phylogenic

relationships.

The goal of the research presented here is to compliment current genomic

investigations for Pleurobrachia bachei.19, 24

The use of multiple techniques for the

analysis of the molecular makeup of an animal can establish congruence which imparts

more certainty to claims of phylogenic placement. Here, our results agree with genomic

data suggesting the absence of many classical neurotransmitters including serotonin,

acetylcholine, epinephrine, dopamine, norepinephrine, and histamine and the presence of

glutamate and gamma-aminobutyric acid (GABA).

6.2 Experimental

6.2.1 Chemicals and Materials



from Alfa Aeser (Ward Hill, MA, USA). Water for all solutions and standards was

obtained from an Elga PureLab Prima water filtration system (Woodridge, IL, USA) at

purity of 18.2 MΩ.

116

6.2.2 Standards and Solutions

Extraction media consisted of 49.5/49.5/1, methanol (LC-MS grade)/water/glacial

acetic acid (99%) by volume. Standards were weighed (1-2 mg) on a microbalance

(Mettler Toledo; Columbus, OH, USA) and dissolved in extraction matrix for high

concentration stock solutions. These stocks were stored at -80°C until use for dilution to

working concentrations. Citric acid (25 mM, pH 2.5) was prepared by dissolving 5.25 g

citric acid in 1.0 L ultrapure water. Borate buffer (50 mM, pH 9.25) for separations was

prepared by dissolving 9.2 g of sodium borate with 3.0 g of boric acid in 1 L of ultrapure

water. Both the citric acid and borate buffers were filtered using a 0.22 µm vacuum filter

and degassed by maintaining the vacuum for 20 min while stirring. Artificial sea water

(ASW) consisted of (mM) 460 NaCl, 10 KCl, 10 CaCl2, 22 MgCl2, 26, MgSO4, and 10

HEPES (pH 7.7).

6.2.3 Animal Collection and Species

The species included in this work were Pleurobrachia bachei, Bolinopsis

infundibulum, Bereo abyssicola, Polyorchis penicillatus and Soccoglossus. Animals

were collected from the coast of Friday Harbor in Washington State, USA by Leonid

Moroz and his colleagues and shipped live to UIUC; all species identifications were

performed by them. One animal of each species were collected and shipped in October,

2010 and additional 3 specimens of Pleurobrachia bachei collected March, 2012.

Animals were shipped overnight in filtered sea water on wet ice.

117

6.2.4 Sample Preparation

6.2.4.1 Dissection

When received, the animals were kept in seawater at 0 – 10°C and used within 36

hours of arrival. Pleurobrachia bachei, Bolinopsis infundibulum, Polyorchis Penicillatus

animals were individually placed in a petri dish filled with filtered sea water under a 10x

stereoscope. From the Pleurobrachia, tentacles, combs, tentacular sheaths, and stomach

were individually removed as indicated in Fig. 6-3 using sharp dissection scissors. From

the Bolinopsis, the abhoral region, combs, and tissue surrounding the combs were

removed. From Polyorchis, the aurial, lobes, mouth were dissected. Each dissected

tissue was quickly dipped into ultrapure water immediately after dissection and prior to

extraction to reduce contamination and salt from the filtered sea water.

Bereo abyssicola was pharmacologically treated with 5-hydroxytryptophan (5-

HTP). The whole animal was placed in a solution of artificial sea water at a

concentration of 250 µM of 5-HTP for 60 min at 4°C. Combs, mouth, and tissue

surrounding combs were dissected and placed into a tube maintained at 4°C

6.2.4.2 Tissue Extraction

Extraction solution was added to each tissue at a volume approximately 2x the

volume of tissue collected. Each tissue sample was sonicated for 1-2 min for complete

homogenization with extraction media. Then, each sample was placed at 4°C for 90 min

for extraction. Afterwards, samples were centrifuged at 15,000 for 15 min and the

supernatant removed and frozen at -80°C until analysis.

118

6.2.5 Analysis Methods

6.2.5.1 Capillary Electrophoresis – Laser-Induced Native Fluorescence

A laboratory-constructed CE-LINF system was used to analyze samples for

indolamines and catecholamines.25

The CE separations were performed using a BGE of

25 mM citric acid (pH 2.5, applied voltage +30 kV) or 50 mM borate (pH 9.5, applied

voltage +21 kV). BGE was filtered using a 0.2 µm surfactant-free, cellulose acetate

syringe filter (Nalgene; Rochester, NY, USA) immediately prior to use. Between each

separation the capillary was conditioned for 5 min with 0.1 N NaOH and then rinsed for

5 min with the BGE. Approximately 10 nL of sample was hydrodynamically injection

into the capillary.

Limits of detection reported in Table 6-1 were defined as the concentration of

analyte with a signal-to-noise ratio of 3 with the signal as the peak fluorescence intensity

of the particular analyte peak and the noise the standard deviation of the background

immediately preceding the peak.

6.2.5.2 Capillary Electrophoresis – Mass Spectrometry

CE-ESI-MS analysis was accomplished by using instrumentation previously

reported by our group using either a Bruker Microtof or a Maxis (Bruker Daltonics;

Billerica, MA, USA) for detection modes.26-27

Briefly, all separations were performed

using 1% formic acid in water as the electrolyte and applied voltage of +30 kV. The

sheath liquid was 0.1% formic acid in 50/50 methanol/water. Samples were

hydrodynamically injected for a total volume of ~ 6 nL. Mass spectra were collected and

recorded at a rate of 2 Hz with calibration was performed using sodium formate clusters.

119


The resolution of the relative placement of basal phyla is poor, partially because

of the inability to study intermediate, extinct species and partially due to the lack

molecular and genomic analysis of existing species in the basal phyla. A comparative

approach can be taken which studies morphological, cellular, and molecular features of

species within these basal phyla, including the ctenophore.28

Several species of

ctenophore, Pleurobrachia bachei, Bolinopsis infundibulum, Bereo abyssicola, were

collected and analyzed to determine the presence of classical neurotransmitters as well as

a cnidarian Polyorchis penicillatus and a control animal from bilateria, Soccoglossus.

The focus of this work was on serotonin due to its wide spread use in life and previous

genomic and transcriptomic studies conducted by our collaborators which showed an

interesting absence of enzymes used for serotonin synthesis. This result being significant

enough that further confirmation was warrented to rule out errors in the genomic data or

unidentified synthetic pathways. Of course, approaches for indole measurements have

been well-documented in prior chapters.

The targeted areas indicated in Fig. 6-2 are nerve-rich tissues which would be the

most probable areas for serotonin production as well as other neural signaling molecules.

While extensive measurements for serotonin in Pleurobrachia revealed no detectable

levels of 5-HT, the Cnidarian Polyorchis Penicillatus did, as well as our control species

from Bilateria phyla, Soccoglossus, as is demonstrated by Fig. 6-3 and 6-4. Fig. 6-3

compares the wavelength-resolved electropherogram from a serotonin-rich tissue of

Soccoglossus, Panel A, and the typical analysis of tissue from Pleurbrachia, Panel B.

The presence of 5-HT in the two control species demonstrates the CE-LINF technique is

120

applicable for analysis of these systems, but if any 5-HT is present within Pleurobrachia

it is below the instruments sensitivity 1 nM. Without evidence from genomic or

molecular analysis for the presence of a serotonergic system from Pleurobrachia bachei,

Bolinopsis infundibulum, or Bereo abyssicola it is increasingly unlikely the phyla uses

serotonin for signaling. This is a surprising result given the widespread occurrence of

serotonin throughout the Metazoa.

While our primary goal was evaluation for serotonin, the opportunity was taken to

determine if a number of other signaling compounds were present including dopamine,

acetylcholine, GABA, and glutamate using CE-ESI-MS for detection. As indicated in

Table 6-1 tryptophan, tyrosine, serine, GABA, aspartate, glutamate were all present

within Pleurobrachia bachei tissue, matching both migration times and monoisotopic

mass. Extracted chromatograms for the corresponding masses are provided in Fig. 6-5.

As both GABA and glutamate signaling systems are also widespread throughout

Metazoa, their presence is not surprising.12

6.4 Conclusions

Advances in molecular and genetic methods over the past decade have

demonstrated prior misconceptions on relationships between taxa and the power of these

new insights. Bilateria have been extensively investigated, but more basal phyla have are

still under analysis. The lack of detectable serotonin in multiple species of Ctenophora is

an interesting observation in the context of evolution of the nervous system. However,

while serotonin is well conserved it is only one of many signaling systems. We have

shown here a surprising lack of serotonin within these animals as well as no evidence of

121

many classical neurotransmitters found throughout life. However, there are indications

of GABA and glutamate.


Future studies into the nervous system of Ctenophora should focus on which

signaling systems are present. Now that GABA and glutamate have been identified

within the Pleurobrachia bachei mechanistic studies can be conducted to further

investigate their role in these early nervous systems. Perhaps most intriguing is to

determine which neurochemical signaling systems have taken over the roles of 5-HT in

development, body plan formation and cell-cell signaling.

122


Analyte CE-LINF CE-ESI-MS

Detection

Limit

(nM)

Result in

Pleurobrachia

Detection

Limit

(nM)

Result in

Pleurobrachia-

Mass Match

(mDa)

Serotonin 1 ND NA --

Tryptophan 5 D <125 nM D-2.2

Tyrosine 88 D <125 nM D-1.0

Dopamine 36 ND <125 nM ND

Norepinephrine 91 ND <125 nM ND

Epinephrine 94 ND <125 nM ND

Octopamine 10 ND <125 nM ND

L-Dopa NA -- <125 nM ND

5-Hydroxy

Tryptophan

NA -- <125 nM ND

Tryptamine 2 ND <125 nM ND

Indole 3 Acetic

acid

6 ND NA --

Indole butyric acid 13 ND NA --

Histamine NA -- <125 nM ND

Acetocholine NA -- <125 nM ND

Tyramine 25 ND <125 nM ND

Serine NA -- <125 nM D-1.5

Gaba NA -- <125 nM D-1.5

Aspartate NA -- <125 nM D-1.6

Glutamate NA -- <125 nM D-0.6

D – Detected

ND – Detectable in standard, but not observed in any Ctenophore tissues

NA – Not detectable by the method

Table 6-1 Summary of analyses of Pleurobrachia bachei combs for both CE-LINF

and CE-ESI-MS methods. Several combs were pooled from 2 animals.

123

Fig. 6-1 Classical representation of a progressive acquisition of a centralized nervous

system, with ctenophores and cnidarians placement largely unresolved, but prior to

Bilaterians. Reproduced from Galliot, B.; Quiquand, M.; Ghila, L.; de Rosa, R., et al.,

Origins of neurogenesis, a cnidarian view. 2009 Developmental Biology, 332 (1), 2-24.

with permission.29

124

Fig. 6-2 Photograph of one of the Pleurobrachia bachei used during these measurements,

with labels indicating the target tissue for analysis. These areas were chosen as they are

nerve rich tissues and are more likely contain neuroactive compounds.

Combs

Stomach

Tentacle

125

Fig. 6-3 Representative wavelength-resolved electropherograms from soccogloassis

(Panel A) and Pleurobrachia bachei (Panel B). Soccogloasis was used as a control

animal and serotonin was detectable in all tissues collected. However, no serotonin was

detectable in any animals or tissues collected for Pleurobrachia bachei.

Wavele

ngth

(nm

)

Time (min)

6 8 10 12 14 16 18 20 22

350

400

450

500

550

600

650

700

750

Wavele

ngth

(nm

)

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4 5 6 7 8 9 10 11 12

300

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400

450

500

550

600

650

700

Tryptophan

Tyrosine

Neutral band

A - Soccoglossus

B - Pleurobrachia

Serotonin

126

Fig. 6-4 Single-channel electropherograms for the wavelength corresponding to the

maximum fluorescence of serotonin ~335 nm. Traces include analysis from all tissues

collected from Pluerobrachia bachei and the standard containing serotonin. This

represents that even at detection limits of 1 nM serotonin was undetectable in any tissue

for this species of Ctenophore.

4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6

0

200

400

600

800

1000

1200

1400

1600

1800

2000

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.)

127

14 16 18 20 22 24 26 Time [min]0.0

0.5

1.0

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2.5

6x10

Intens.

20120329 01_combs_01_JA.d: EIC 104.0706±0.01 + FullScan, Smoothed (1.51,1,GA) 20120329 01_combs_01_JA.d: EIC 106.0499±0.01 + FullScan, Smoothed (1.51,1,GA)



1

2

3 4

5

6

Fig. 6-5 The extracted mass chromatograms for the detected compounds indicated in Table 1.

(1) GABA, (2) serine e (3) tryptophan (4) glutamate (5) tyrosine (6) aspartate

128

6.7 References

1. Ryan, T. J.; Grant, S. G. N., The origin and evolution of synapses. Nature Reviews Neuroscience 2009, 10 (10), 701-712.

2. De Robertis, E. M.; Sasai, Y., A common plan for dorsoventral patterning in Bilateria. Nature 1996, 380 (6569), 37-40.

3. Moroz, L. L., On the independent origins of complex brains and neurons. Brain, Behavior and Evolution 2009, 74 (3), 177-190.

4. Hirth, F.; Reichert, H., Basic nervous system types: One or many? In Evolution of Nervous Systems, Editor-in-Chief: Jon, H. K., Ed. Academic Press: Oxford, 2007; pp 55-72.

5. De Robertis, E. M., Evo-Devo: Variations on ancestral themes. Cell 2008, 132 (2), 185-195.

6. Philippe, H.; Telford, M. J., Large-scale sequencing and the new animal phylogeny. Trends in Ecology and Evolution 2006, 21 (11), 614-620.

7. Chan, Y. M.; Jan, Y. N., Conservation of neurogenic genes and mechanisms. Current Opinion in Neurobiology 1999, 9 (5), 582-588.

8. Carroll, S. B., Evo-Devo and an expanding evolutionary synthesis: A genetic theory of morphological evolution. Cell 2008, 134 (1), 25-36.

9. Mikhailov, K. V.; Konstantinova, A. V.; Nikitin, M. A.; Troshin, P. V., et al., The origin of Metazoa: A transition from temporal to spatial cell differentiation. Bioessays 2009, 31 (7), 758-768.

10. Adoutte, A.; Balavoine, G.; Lartillot, N.; Lespinet, O., et al., The new animal phylogeny: Reliability and implications. Proceedings of the National Academy of Sciences of the United States of America 2000, 97 (9), 4453-4456.

11. Halanych, K. M., The new view of animal phylogeny. 2004; Vol. 35, pp 229-256.

12. Walker, R. J., Evolution and overview of classical transmitter molecules and their receptors. Parasitology 1996, 113 (SUPPL.), S3-S33.

13. Nickel, M., Evolutionary emergence of synaptic nervous systems: What can we learn from the non-synaptic, nerveless Porifera? Invertebrate Biology 2010, 129 (1), 1-16.

129

14. Dunn, C. W.; Hejnol, A.; Matus, D. Q.; Pang, K., et al., Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 2008, 452 (7188), 745-749.

15. Miller, G., On the Origin of the nervous system. Science 2009, 325 (5936), 24-26.

16. Haddock, S.; Case, J. F., Not all ctenophores are bioluminescent: Pleurobrachia. The Biological Bulletin 1995, 189 (3), 356-362.

17. Hernandez-Nicaise, M. L., The nervous system of ctenophores - I. Structure and ultrastructure of the epithelial nerve-nets. Le Système Nerveux des Cténaires - I. Structure et Ultrastructure des Réseaux Epithéliaux 1973, 137 (2), 223-250.

18. Jager, M.; Chiori, R.; Alie, A.; Dayraud, C., et al., New insights on ctenophore neural anatomy: immunofluorescence study in Pleurobrachia pileus (Muller, 1776). J Exp Zool B Mol Dev Evol 2011, 316B, 171 - 187.

19. Kohn, A. B.; Citarella, M. R.; Kocot, K. M.; Bobkova, Y. V., et al., Rapid evolution of the compact and unusual mitochondrial genome in the ctenophore, Pleurobrachia bachei. Molecular Phylogenetics and Evolution 2012, 63 (1), 203-207.

20. Garattini, S.; Valzelli, L., Serotonin. Elsevier Publishing Co.: Amsterdam, London, New York, 1965.


22. Peroutka, S. J.; Howell, T. A., The molecular evolution of G protein-coupled receptors: Focus on 5-hydroxytryptamine receptors. Neuropharmacology 1994, 33 (3-4), 319-324.

23. Peroutka, S. J., 5-Hydroxytryptamine receptors in vertebrates and invertebrates: Why are there so many? Neurochemistry International 1994, 25 (6), 533-536.

24. Pett, W.; Ryan, J. F.; Pang, K.; Mullikin, J. C., et al., Extreme mitochondrial evolution in the ctenophore Mnemiopsis leidyi: Insight from mtDNA and the nuclear genome. Mitochondrial DNA 2011, 22 (4), 130-142.


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27. Nemes, P.; Knolhoff, A. M.; Rubakhin, S. S.; Sweedler, J. V., Metabolic differentiation of neuronal phenotypes by single-cell capillary electrophoresis-electrospray ionization-mass spectrometry. Analytical Chemistry 2011, 83 (17), 6810-6817.

28. Lichtneckert, R.; Reichert, H., 1.19 - Origin and evolution of the first nervous system. In Evolution of Nervous Systems, Editor-in-Chief: Jon, H. K., Ed. Academic Press: Oxford, 2007; pp 289-315.

29. Galliot, B.; Quiquand, M.; Ghila, L.; de Rosa, R., et al., Origins of neurogenesis, a cnidarian view. Developmental Biology 2009, 332 (1), 2-24.

131

Chapter 7

Single Neuron Analysis from Drosophila Melanogaster


The work presented within this chapter was completed in collaboration with Dr.

Suzanne Neupert (a visiting scholar in the Sweedler group on a German Academic

Exchange Service Fellowship) who performed the dissections of the single neurons from

Drosophila melanogaster and Wolf Huetteroth who bred the flies. This chapter is a part

of a larger effort to combine a number of analytical techniques to evaluate neural circuits

within defined phenotypes of Drosophila melanogaster. Funding within the Sweedler

group was from the National Institutes of Health through R01 NS031609 and P30

DA018310.

7.1 Introduction

The goals of this chapter are two-fold, one more analytical and one more

biological. The analytical goals relate to the challenges of selecting, sampling and

assaying well-defined neurons that are literally orders of magnitude smaller in volume

than the Aplysia neurons and mammalian samples described in prior chapters. The

biological goals take advantage of the premier genomics model – the fruit fly – to

understand the neurochemical perturbations caused by well-defined mutations.

As described in Chapter 1, the nervous system regulates internal states along with

monitoring and responding to the outside world using complex neuronal circuitry. This

complexity of neural configuration demands analysis on the single-cell level to

interrogate cell-to-cell communication and cell-specific stimuli responses to understand

132

even the most basic of functions. The size, volume, and trace levels of compounds of

single cells presents significant challenges in measurements as reviewed by Lu et al.1

However, chemical analysis is only as effective as the sampling method. Isolation of

individual cells from tissue has been accomplished using micropipettes and optical traps

as well as hyphenated techniques2 to limit the perturbation to the delicate sample.

Micropipettes allow for more control of cells or even single vesicles3 over the weaker

forces involved in optical trapping, but physical manipulations can easily damage cells,

potentially altering the chemical composition. Optical traps do not require physical

manipulation cells with implements, but damage can occur from absorption of photons

used to create the trap. Obtaining high-quality single cells from tissue is challenging,

particularly when working with neurons which inherently respond to external stimuli.

Developing new and improved techniques for sampling single-cells is critical to the

investigations of cell-to-cell communication.

Methods for single-cell analysis can be broadly categorized into separations and

direct measurements. Within separation based approaches capillary electrophoresis (CE)

has been extensively used due to its compatibility with low-volume samples and

ultrasensitive detection techniques for the analysis of a wide range of target analytes -

from gaseous NO to large proteins.4 Single-cell applications of CE are the subject of

several reviews demonstrating the significant contribution of CE analysis to single cell

analysis.1, 4-8

In particular, laser-induced fluorescence (LIF) and electrochemical (EC)

detection schemes are the widely used due to thier inherent sensitivities.

Drosophila is a commonly used model system as it is genetic tractable and

capable of higher-order functions even with a relatively simple brain.9 The genome of

133

Drosophila was one of the first sequenced and with little genetic redundancy genes can

be linked to behavior and biochemical production.9-10

Specific neurotransmitters have

been linked to behaviors and functions such as octopamine in aggression,11

dopamine in

both olfactory memories12

and arousal,13

and tyramine for addition to cocaine.14

Selectively perturbing molecular or cellular activities and determining changes in

behavior, function, or chemical states is a powerful method for understanding the roles of

specific neurons. A number of molecular genetic strains have been developed to study

neuronal activity in the Drosophila model, the one used in this work selectively controls

neuronal endocytosis.15

While an excellent genetic model for studying the nervous

system, practical challenges given is its small brain (~5 nL) and neurons (~5-15 µm)

require particularly sensitive methods for analysis.16-17

Drosophila with the shibirets1

(shits1

) or shibirets2

(shits2

) mutations are neurogenic

phenotypes resulting in reversible, temperature-gated arrest of endocytosis as a result of a

mutation in the structure of Dynamin, a protein required for endocytosis.18-21

While the

structural abnormalities between these two mutants differ, both result in temperature-

gated endocytosis, disrupting synaptic activity by blocking synaptic recycling. Above

the threshold temperature of 30°C endocytosis arrests, below the threshold endocytosis

occurs normally, providing a temporally-selective control of neuronal activity. The

shibire mutants are a well-established model for the investigation of neural

mechanisms.15, 22-23

The effect of endocytosis/exocytosis arrest has been used to study

development24

and phototransduction25

by selectively subjecting these flies to increased

temperature pulses.

134

From an analytical perspective, we described the interrogation of ~10 µm

individual neurons from Drosophila melanogaster using a novel sampling approach and

genetic strains. Serotonergic neurons were visualized by using genetic variants

expressing GFP within the same neurons expressing the rate limiting enzyme, tryptophan

hydroxylase (Trh), for serotonin synthesis. Additionally, the shibire mutant was used to

stabilize the cell during the dissection process by virtue of the temperature-gated

inhibition of neurosecretory release. While the shits1

and shits2

strains have been used for

the interrogation of neural mechanisms, here we employ it as a method to prevent

neurotransmitter release during cell isolation, thus allowing for a more accurate chemical

inventory of individual neurons.

7.2 Experimental

7.2.1 Chemicals and Solutions

The standards and chemicals, at the highest purity possible, were obtained from

Sigma-Aldrich (St. Louis, MO, USA) unless otherwise noted. Serotonin was purchased

from Alfa Aeser (Ward Hill, MA, USA). Water for preparing the solutions and

standards was obtained from an Elga PureLab Prima water filtration system (Elga LLC;

Woodridge, IL, USA) at purity of 18.2 MΩ.

Extraction solution consisted of 49.5/49.5/1, methanol (LC-MS

grade)/water/glacial acetic acid (99%) by volume. Standards were weighed (1-2 mg) on a

microbalance (Mettler Toledo; Columbus, OH, USA) and dissolved in extraction matrix

for high concentration stock solutions. These stocks were stored at -80°C until use for

dilution to working concentrations of. Borate buffer (50 mM, pH 9.25) for separations

135

was prepared by dissolving 9.2 g of sodium borate with 3.0 grams of boric acid in 1 L of

ultrapure water. Both citric acid and borate buffers were filtered using a 0.22 µm vacuum

filter and degassed by maintaining the vacuum for 20 min while stirring. Dissection

solution consisted of 0.9% saline solution.

7.2.2 Capillary Electrophoresis – Laser-Induced Native Fluorescence Analysis

A 150/50 µm ID/OD fused silica capillary (Polymicro Technologies; Phoenix,

AZ, USA) was used as the separation column. Approximately 20 nL of the sample was

hydrodynamically injected into the capillary by lowering the outlet for 45 s. Separations

were performed in 50 mM borate buffer and potential of +21 kV applied. The sheath-

flow liquid was 25 mM citric acid. Peaks resulting from the analysis of single neurons

were identified by comparison of both migration time and fluorescence emission to that

of standards. Several controls were analyzed to ensure peaks were derived from the

single neuron shown in Fig. 7-2 - extraction solution, dissection solution, and material

from surrounding tissue.

7.2.3 Fly Stocks and Neuron Isolation

Flies were bred by Wolf Huetteroth at the University of Massachusetts Medical

School. Briefly, w UAS-shits1

/FM70-GFO;UAS-mCD8::GFP/CyO;(UAS-shits1

) or (+)/+

were crossed with FM7/Y;UAS-mCD8::GFP/CyO;Trh-gal4/TM3 Sb and wUAS-

shits1

/wUAS-shits1

;+/sp; wUAS-shits1

or (+)/TM6 B were crossed with FM7a/Y;UAS-

mCD8::GFP/UAS-mCD8::GFP;Trh-gal4/TM3 Sb. The genetic strains created and used

here are listed in Table 7-1.

136

The target neuron was dissected from the surrounding tissue under a fluorescence

microscope (Zeiss V12 stereo Lumar; Jena, Germany). Pulled glass capillaries which

had been treated with poly-D-lysine were used to hydrodynamically obtain the cell

without handling. After settling and adhering to the side of the capillary a minimal

volume (~100 nL) of extraction media was drawn into the capillary to cover the cell.

Extraction from the single neuron occurred within the pulled capillary at 4°C until

analysis by CE-LINF 90 min later.


As is the case within most animals, the serotonergic neurons in Drosophila are

few and dispersed throughout the CNS, as can be seen in Fig. 7-1. Even with the limited

number of serotonergic neurons, the impact of the serotonergic system on CNS activity

and organism behavior is profound. Serotonin acts as both a neurotransmitter and

neuromodulator, and is implicated in learning and memory, feeding, and movement

within Drosophila.26

Understanding the metabolic pathways and regulation of serotonin

within these neurons is important to gaining insight into its influence in these diverse

functions and behaviors.

Measurements were made using both GFP expressing strains of Drosophila and

strains expressing both GFP and the temperature-sensitive, shits1

mutation. Control

measurements of dissection solution, extraction solution, and the extracellular fluid

surrounding the neurons determined the signals that were a result of the individual

neuron and those occurring from contamination from tissue surrounding the neuron, Fig.

7-2. While the dissection and extraction solutions did not present unexpected signals, the

137

extracellular fluid contained a number of bands with fluorescence emission in the 400-

600 nm range. These bands are likely due to fluorescent compounds from the eye as the

targeted neuron indicated in Fig. 7-1 is in close proximity to the eye which is highly

pigmented.

7.3.1 GFP Expressing Mutant

Expression of GFP within the targeted neurons provides a guide for identification

and dissection of a particular neuron from a large number of animals. With the rate-

limiting enzyme for serotonin being expressed within these neurons, presence of

serotonin is expected. However, the extent of serotonin production is unknown. Similar

methods using GFP have been used to target specific neurons for peptidome studies.16

Here, the serotonergic neurons are clearly visible with the target neuron indicated with an

arrow at the perimeter of the brain in Fig. 7-1. More than 50 individual neurons were

analyzed using CE-LINF. Tryptophan and tyrosine were detected routinely within these

neurons, suggesting that the isolation and analysis were effective; unexpectedly, only 2 of

these neurons contained detectable levels of serotonin. An example electropherogram for

both serotonin positive and serotonin negative results are given in Fig. 7-3A and B,

respectively.

7.3.2 Shibire Mutant

We hypothesized that during the isolation of the neurons, the readily releasable

pool of vesicles are released due to the mechanical manipulations of the cells. Thus, we

developed an approach to selectively inhibit vesicular release, in essence a form of

138

neuronal stabilization during the dissection and sampling process. Often the limitations

for single neuron analysis are rooted in obtaining a sample with minimal perturbation of

its chemical contents.27

As neurons are inherently sensitive to chemical, electrical and

mechanical stimuli, the potential for altering the chemical compliment during dissection

is high. The shits1

mutant allows for temporal control of neurosecretory activity. Flies

were allowed to develop normally to the adult stage of its life cycle, but an increase in

temperature to 31°C immediately prior to and during dissection reduces vesicular release,

stabilizing the neuronal content, allowing for improved detection of its vesicular content.

Of the strains of the cross bred flies containing the shits1

mutation we found that only one

strain had a higher success rate for detection of enhanced levels of tryptophan

metabolites than the GFP only strain, mutant 4 from Table 7-1.

Not only did this strain facilitate detection of serotonin in neurons for which the

enzymes are expressed, the presence of tryptamine was confirmed along with an intense

signal from an, as of yet, unidentified indole-like compound, Fig. 7-3C. The unidentified

signal has the same emission spectrum as tryptophan, but with a slightly longer migration

time. In addition to detecting previously undetected metabolites, the signal from

serotonin and tryptophan were significantly larger in shits1

than the GFP only mutant,

approximately four-fold higher in intensity for both analyts.

7.4 Conclusions

We have developed and validated several protocols that enable us to isolate

selected cells (here labeled with GFP) and characterize them for their indoles and

catechols. Because the cells are about 10-fold smaller in diameter than the Aplysia

139

neurons, they contain 1000-fold less material. With optimized sampling, we were still

able to characterize selected metabolites in these cells. The techniques developed in this

work for the interrogation of single neurons from Drosophila melanogaster demonstrate

a novel method for the determination of the neuronal contents. Trace levels of serotonin,

tryptophan, and tyrosine could be detected in the flies expressing only GFP. The use of

the shits1

mutant and a temperature controlled dissection process not only substantially

increase the signal from serotonin but revealed new metabolites that were previously

undetectable, presumably as a result of manual manipulation during the dissection

process.

In addition to the positive aspects of this study, the results also demonstrate an

important aspect of single-cell measurement, the perturbation of the cell content caused

during the dissection and sample handling. Such factors need to be considered when

interpreting results. While many techniques are capable of detection at trace levels of

selected compounds, validated sampling protocols are critical to preserving the complex

chemical content of individual cells. This work was one part of a larger set of analytical

methods for the analysis of single neurons from Drosophila which included small-

molecule, metabolomics using CE-ESI-MS28

and peptidomics using MALDI. These

complementary methods for interrogating individual cells can help create a more

comprehensive analysis of the distribution of neuroactive compounds in the CNS at the

cellular level.

140


Only one specific neuronal subtype within the Drosophila brain has been

investigated here. Future work will validate these findings and focus on a larger

coverage of neurons and neurotransmitters. Interrogation of neurons expressing other

neurotransmitters is likely to identify other metabolites not previously measured.

Universal measurement techniques such as CE-MS will provide a platform to discover

other, possibly unidentified metabolites or intermediates. Identifying the unknown

compound detected with the shits1

mutant neuron should be accomplished using multiple,

complimentary techniques. Specifically, using CE-LINF as well as microscale mass

spectrometry techniques would aid in this investigation.

141


Mutant 1 FM7a/UAS-ShiTs1 ; GFP/Sp; TM6B/Trh-gal4

Mutant 2 FM7a/UAS-ShiTs1 ; GFP/Sp; TM6B/UAS-ShiTs1

Mutant 3 Shi/Y; GFP/+ ; Trh-gal4/TM6B

Mutant 4 FM7a/ShiTs1 ; GFP/Sp; Trh-gal4/ShiTs1

Mutant 5 Shi/Y; GFP/+ ; Trh-gal4/ShiTsi

Mutant 6 FM7a/ShiTs1 ; GFP/+; Trh-gal4/TM6B

Mutant 7 ShiTs1/Y; GFP/+; Trh-gal4/TM6B

Mutant 8 Shi/Y; GFP/Sp; Trh-gal4/ShiTs1

Mutant 9 Shi/Y; GFP/CyO; Trh-gal4/+

Mutant 10 ShiTs1/Y; GFP/CyO; Trh-gal4/ShiTs1

Mutant 11 FM7a/ShiTs1 ; GFP/CyO; Trh-gal4/ShiTs1

Table 7-1 – The crossbred Drosophila produced several genetic variants. The strains detailed here were used for single-neuron isolation and were analyzed by CE-LINF for presence of indolamines and catecholamines.

142

Fig. 7-1 Fluorescence image of Drosophila brain with GFP co-expressed with the rate-

limiting enzyme for serotonin synthesis, Trh. This genetic strain allows for serotonergic

neurons to readily be identified for dissection under a fluorescence microscope. The

arrow indicates the targeted neuron, chosen because it is close to the edge of the brain

which is more accessible.

Isolated Neuron

143

Fig. 7-2 The ultratrace level of analytes within single neurons require elimination of any

potential contaminates. The fluids contacting the cell during the dissection process were

analyzed individually to confidently assign signals. (A) Extraction solution, (B)

dissection solution, and (C) extracellular fluid from outside the dissected neuron.

Wa

ve

len

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(n

m)

Time (min)

1 2 3 4 5 6 7 8 9 10

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(n

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B – Dissection Solution

C – Extracellular Fluid

144

Fig. 7-3 Electropherograms from individual neurons identified in Fig. 7-1, representing

both (A) serotonin positive result and (B) serotonin negative result. Identified analytes

bands are: (1) serotonin, (2) tryptophan, (3) tyrosine, and * bands detected from

extracellular fluid in Fig. 7-2. Differences in migration times are a result of a difference

in the length of capillary used; all other conditions were the same.

Wa

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(n

m)

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3 4 5 6 7 8 9 10

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145

Fig. 7.4 – Continued on next page.

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146

Fig. 7-4 Results of analysis of a single neuron from mutant 4 showing significant levels of (1) tryptamine, (2) serotonin, (3)

tryptophan, (4) and an unidentified metabolite with the same fluorescence spectra as tryptophan. Panel B demonstrates the level of

analytes compared to a 1 µM standard. Panel C shows confirmation of the analytes identification for both serotonin and tryptamine by

comparison of normalized fluorescence spectra. Time scale in panel B was aligned for peaks identified by fluorescence spectra.

0

1000

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.)

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147

7.7 References

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