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Fernandez Group 2011

Advances in DART Instrumentation: Transmission

Mode, Laser Ablation and Mobility Separations

Facundo M. Fernández

School of Chemistry and Biochemistry. Georgia Institute of Technology. Atlanta,

Georgia, USA.


Ambient MS

• Ambient MS refers to ionization techniques performed:

– Atmospheric pressure

– In the open-air, no enclosures

– No restrictions on sample size and shape

– Sample in its native state

– Relatively soft ionization conditions


Surface Sampling “Ambient” MS

Biannual Anal. Chem. Review-Harris/Fernández

Acronym Name

AP-TD/SI Atmospheric Pressure Thermal Desorption-Secondary Ionization

BADCI Beta electron-assisted Direct Chemical Ionization

DAPCI Desorption Atmospheric Pressure Chemical Ionization

DAPPI Desorption Atmospheric Pressure Photo-Ionization

DART Direct Analysis in Real-Time

DBDI Dielectric Barrier Discharge Ionization

DCBI Desorption Corona Beam Ionization

DEMI Desorption Electrospray/Metastable-Induced Ionization

DESI Desorption Electrospray Ionization

DICE Desorption Ionization by Charge Exchange

EASI Easy Ambient Sonic-spray Ionization

ELDI Electrospray-assisted Laser Desorption Ionization

FAPA Flowing Atmospheric Pressure Afterglow

IR-LAMICI Infrared Laser Ablation Metastable-induced Chemical Ionization

LADESI Laser-Assisted Desorption Electrospray Ionization

LAESI Laser Ablation Electrospray Ionization Mass Spectrometry

LDESI Laser Desorption Electrospray Ionization

LESA Liquid Extraction Surface Analysis

LIAD-ESI Laser-Induced Acoustic Desorption-Electrospray Ionization

LMJ-SSP Liquid Micro Junction-Surface Sampling Probe

LTP Low-Temperature Plasma probe

MALDESI Matrix-Assisted Laser Desorption Electrospray Ionization

ND-EESI Neutral Desorption Extractive Electrospray Ionization

PESI Probe Electrospray Ionization

RADIO Radio-frequency Acoustic Desorption and Ionization

REIMS Rapid Evaporative Ionization Mass Spectrometry

SwiFerr Switched Ferroelectric Plasma Ionizer


Surface Sampling “Ambient” MS

Volatilization Principle

Liquid Extraction

Chemical Sputtering

Thermal Mechanical Laser Desorption/Ablation

Acoustic Neutral Headspace smapling

Ionization Principle

ESI DESI, DICE,

DEMI, LMJ-

SSP, DEMI

AP-TD/SI PESI ELDI, LAESI, MALDESI

LIAD, RADIO

ND-EESI

Sonic spray EASI

APPI DAPPI DAPPI

APCI DAPCI, DBDI

ASAP, BADCI, DART, DCBI, DEMI, FAPA, LTP, REIMS, DEMI

LD/APCI LIAD

Reviews by Cooks, Van Berkel, Eberlin, Fernández


Outline

•Transmission Mode DART (TM-DART)

•Imaging DART

•DART-Ion Mobility


Ambient MS in the Real World: DART

Advantages include:

•No sample preparation required.•Sample is not placed in vacuum.•Sizeable objects can be directly examined in the open air.•Very high sample throughput.•Can be coupled to any mass spectrometer with an atmospheric pressure interface for accurate mass or MS/MS experiments.•No memory effects

•No sample preparation required.•Sample is not placed in vacuum.•Sizeable objects can be directly examined in the open air.•Very high sample throughput.•Can be coupled to any mass spectrometer with an atmospheric pressure interface for accurate mass or MS/MS experiments.•No memory effects


He or N2

1-5 L min-1

Direct Analysis in Real Time

(DART)

3-5 kV Needle

Glow Discharge

Heater

Grid Electrode

H2O

H3O+

He*

He*

He*

He*

He*

He*

He*

He*

He*

He*

H3O+ MH+

MH+

To TOF

MS

Analyte Ions

Anal. Chem. 2005, 77, 2297

M(analyte)

Heated metastables and Penning ionization

Thermal desorption and declustering

Chemical sputtering and surface collisions

Direct Penning ionization and charge exchange reactions

)()(12)()(2)(* )( ggnggg OHHOHHeOnHHe −+

− ++→+

)()( gheat

s ABAB →

)()1(

)(2)(12)()(22)()( gOHm

ggmsgm ABHOHHOHABABHOH +−−+−

+ →+→+

)()1(

)(2)(12)()(22)()( gOHm

ggmggm ABHOHHOHABABHOH +−−+−

+ →+→+

−+ ++→+ eABHeABHe gggg )(.

)()()(*

−+ +++→+ eHeOHeOHe ggg.

2)()(2)(*

.

2)()(.

2

++ +→+ ABOABO gg


Metabolomic Discovery Workflow

Protein Precipitation,

TMS Derivatization

Protein Precipitation,

TMS Derivatization

DART TOF MS (+)

DART TOF MS (+)

Raw Data

Sets for OC and Controls

Raw Data

Sets for OC and Controls

Internal Mass Drift

Compensation

Internal Mass Drift

Compensation

Export as

ASCII

Export as

ASCII

Multivariate Modeling

Multivariate Modeling

Functional Support Vector Machine Classification(Linear, non-

linear)

Functional Support Vector Machine Classification(Linear, non-

linear)

Feature Selection:L1-norm SVM

RFEWeston’s Method

Feature Selection:L1-norm SVM

RFEWeston’s Method

Accuracy, Sensitivity, Selectivity prediction

Accuracy, Sensitivity, Selectivity prediction

Leave-one-out Cross-validation

Leave-one-out Cross-validation

64-30 Test Set validation (x50)

64-30 Test Set validation (x50)

Spectral features in Best Model

Spectral features in Best Model

Accurate mass and Isotope Pattern Matching

Elemental Formulae of Discriminating Features

Metabolite Database Searches:HMDB

Putative Metabolite IDs

Serum samples were obtained from 44 women

diagnosed with serous papillary ovarian cancer

(stages I-IV) and 50 healthy women or women

with benign conditions (e.g., serous, simple, or

follicular cysts; Table 1) and run in triplicate.

Fernandez Group 2011Zhou et al., J. Am. Soc. Mass Spectrom., 2010, 21, 68-75

DART Metabolomic Discovery Workflow

Effect of helium gas temperature on DART-TOF MS sensitivity

for metabolomic profiling of derivatized serum: (a) background

corrected mass spectra at various helium temperatures, (b)

number of metabolites matched to HMDB database, and (c)

change in S/N of three mass spectrometric signals at m/z

205.12, 467.22, and 762.25 versus helium temperature.


DART Diagnostics Results

Classifier typeFeature selection

method

Number of

FeaturesSENS(%) SPEC(%) ACC(%)

fSVM 1:7:20,000 sub-

sampling2,858

100.0 93.8 96.7

fSVM_NL 100.0 93.8 96.7

fSVMOne-way ANOVA

(p=0.05)3,017

100.0 100.0 100.0

fSVM_NL 100.0 93.8 96.7

fSVM One-way ANOVA

(p=0.01)1,320

92.9 93.8 93.3

fSVM_NL 92.9 100.0 96.7

64:30 Split validation


Pathway Enrichment Analysis

• 153 elemental formulae were assigned to 299 unique endogenous metabolites or xenobiotic compounds.

• These compounds mapped onto 25 pathways, suggesting differences between cancer and noncancer groups in amine, amino acid, eicosanoid, and TTP metabolisms.

• Differences in the

metabolisms of

carbohydrates and

estrogens have

lower confidence due

to ambiguity in

elemental formulas.


More to DART Than Gas-Phase Chemistry

VD

VGridTG

VMSD1 D2

D3QG

G% DART MSGIST

Sample

θDART

A

VD

VGridTG

VMSD1 D2

D3QG

G% DART MSGIST

Sample

θDART

A

P1 P2

H1

H2

MSSample


DART with GIST interface


TM-DART

0

0.1

0.2

0.3

0.4

0.5

0.6

DART

Sample Holder

Orifice

0.2

0.1

0

0.3

0.4

0.5

0.6

0.7

DART

Sample

Holder

Orifice

Deltamethrin

MW: 505.21


TM-DART

TP 039

Christina Jones

Manshui Zhou

Facundo Fernandez


Outline


•Imaging DART



Advantages

Relative Weaknesses

Ionization Methods Used for Imaging

Odd-shaped samples

In vivo

Native state (no matrix, no drying) Better Ion Transmission

No surface damage

Soft as ESI

Can perform cationization etc. in

reactive mode

Higher lateral

resolution than DESI

Unsurpassed for large

Biomolecules

Highest lateral

resolution

Limited Spatial

Resolution

Limited ionization

efficiency

Matrix interference in

Low Mw range

Matrix deposition

Fragmentation


IR-DART


Outline


•Imaging DART



Reaction

regionDrift region

DetectorGate

2/1

2

16

3−

Ω=

kTN

qK

D µπ

Portable IM Instrumentation


DART-IMS

v.

vi.

iv.iii.

ii.i.

vii.

0.1 1 10

0.0

0.2

0.4

0.6

0.8

1.0

Likelihood of Detection

Concentration (%, log10 scale)

( )1

12.1/1

125.1+

+=

−

xy

DMMP TPD: 11.81%

0/8

2/8

4/8

7/8

8/8DMMP

0/8

2/8

4/8

7/8

8/8

0/8

2/8

5/8

6/8

8/8 TM DART-

IMS DMMP

0/8

2/8

6/8

5/8

8/8

11.81%

0.28%


Multiplexed DART-IMS

200 us Multiplexing

SNR Gains vs. Conventional Mode

1.0

1.5

2.0

2.5

3.0

3.5

20 sweeps

100 sweeps

400 sweeps

400 us Multiplexing

Sequence Duty Cycle

2% 5% 10% 30% 50%

SNR Gains vs. Conventional Mode

0

1

2

3

4

5

20 sweeps

100 sweeps

400 sweeps

r = 1, ~0.6%, 16 seqs

r = 2, 12.5%, 120 seqs

r = 4, 25%, 1820 seqs

r = 8, 50%, 12870 seqs

)!(!

!nsCombinatio

rnr

n

−=

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