mark a. hayes
DESCRIPTION
Mark A. Hayes. Humboldt State University (CA) B.A. 1985 Industry for 4 years Penn State Ph.D. 1993 University of California, Riverside Postdoc 1996 Professor ASU 1996-current. What would you need to provide the earliest possible detection of disease? . - PowerPoint PPT PresentationTRANSCRIPT
HUMBOLDT STATE UNIVERSITY (CA) B.A . 1985INDUSTRY FOR 4 YEARSPENN STATE PH.D. 1993
UNIVERSITY OF CALIFORNIA, RIVERSIDE POSTDOC 1996
PROFESSOR ASU 1996-CURRENT
Mark A. Hayes
WE TRY TO BREAK THINGS DOWN INTO THEIR SIMPLEST COMPONENTS,
UNDERSTAND THOSE AND BUILD BACK TOWARDS COMPLEXITY.
What would you need to provide the earliest possible detection of disease?
We are all chemists here, and therefore are reductionists-
Differential Diagnostics
What are typical diagnostic strategies? ‘black box’ guesses
Symptoms (T, BP, visual cues) , some chemical/biological measurements
Fit to a model Educated but—by definition—guesses Patient used as test bed – treatments attempted, when
fail—move to next treatment We simply do not yet understand ‘normal’ biology,
much less ‘abnormal’ or ‘disease’ biology
Analytics and Medical Science
Premise: if we can measure all the cells and molecules (and tissue?) in the ‘system’ we could predict (and diagnosis precisely) disease state. [and a lot of other things: pathways ID, enzymatic quantification, PTMs, etc.]
Works pretty well for 747s (Hartwell quote)- 10000 sensors, early warning systems in place. None have fallen out of the sky.
But…
Analytics and Medical Science
Three problems:
1) we don’t even know all the molecules & cells2) we don’t have tools to measure these at the right timescales, cost and sensitivities3) we don’t know how useful this would be (and can’t until we do it!)
Analytics and Medical Science
Here’s where we come in:
Building the best tools to 1) Independently identify biomolecules in a short
timeframe, in a cost efficient manner that is relevant to medical science (and fundamental biological studies)
2) To augment other analytics (mass spectrometry, molecular recognition, spectroscopy, electrochemistry) to accomplish the same goals
3) To learn exactly how sensitive and precise these measurements need to be (more later on this topic)
Our work
We focus on microfluidics, separations science and immunoassay (and other fundamental physical processes – not discussed today)
Because biomolecules all look the same (spectroscopically speaking) or will compromise the operation of instruments, they must be purified or isolated prior to analysis
The separation itself can be an identifier (retention time, location on an array, signal from an immunoassay)
Our work
What’s different or new compared to all the other microfluidics out there? 1) we are generating unprecedented resolution (the
ability to quickly or efficiently (space) separation wanted from unwanted)
2) broad range of targets (10 microns to small molecules) – bacteria, cells, viruses, proteins, metabolites
3) building a format for programmable parallel array-base separations
4) all can be coupled to traditional bio-detection systems (immuno./molec. rec., MS, EC, spectrscp.)
Overall Technical Paradigm
Lysing or disruption chamber
Flow
Flow stream shift or valve
Fraction Collection from Dielectrophoresis
Overall Technical Paradigm
Lysis and Pattern Generation (separation or array)
Lysing or disruption chamber
orand (somecircumstances)
Array readout: molecular recognition & spectroscopy
Linear or multi-dimensional separations
Overall Technical Paradigm: Today’s Presentation
Gradient Dielectrophoresis
Electrophoretic Capture (Array)
and (some
circumstances)
Electrokinetic Forces
Electrophoresis (EP)Dielectrophoresis (DEP)
Electro-osmosis (EO)
Dielectrophoresis• DEP force depends on:
• Particle size (r)• Medium permittivity (εm)• Clausius-Mossotti factor
(fCM)• Electric field gradient (∇|E|2)
Generating Non-uniform Fields• Shaped electrodes
• Expensive, complicated to fabricate
• Electrochemical reactions at capture zones
• Shaped insulators• Inexpensive, simple
fabrication• Electrodes in remote
reservoirs
Current Design• Sawtooth insulators
• Polydimethylsiloxane (PDMS) teeth shape E field
• Sharp features yield intense gradients
• Varied spacing forms distinct local gradients
FEK
FDEP
• EK Forces• FEK E, FDEP E2 • Opposing directions
Current Design• Sawtooth insulators
• Polydimethylsiloxane (PDMS) teeth shape E field
• Sharp features yield intense gradients
• Varied spacing forms distinct local gradients
FEK
FDEP
• EK Forces• FEK E, FDEP E2 • Opposing directions
FDEP
A new dual-force gradient focusing technique similar in form to IEF:
IEF: f( d +z/d pH * E)f( d -z/d pH * E)
D
R = f (dpH/dx, dz/dpH, E, D)
IGDEP: f( mDEP * DE2)f(mEK * E)
D
R = f (dE/dx, dDE2 /dx, D)*
*other factors also…
Chen et al. 2009
DC-iGDEP: Particle Separations
FEOF
FDEP
EK
Linear separation device, similar to isoelectric focusing or other gradient techniques.
Predicted Capture
FEOF
FDEP
PDMS
Buffer solution
Assuming nDEP
COMSOL Modeling of Field Properties
Surface plot: local electrical potential, V Contour lines: magnitude of electric field, |E| Normalized arrows: direction of DEP force, proportional to |
E|2
∆
DE
P Fo
rce
(Cen
terl
ine,
Log
Sc
ale)
COMSOL Calculation
Seven ‘teeth’ narrowest on right
Live & dead Bacillus subtilis, Escherichia coli, & Staphylococcus epidermidis
Three different design, based on sawtooth theme
Consistent separation between physiologic states
Suggests ability to resolve both species, sub-species and metabolic state (see ‘C’, top left)
Pysher 2005: Hayes 2007
DC-iGDEP Particle Separations: Bacteria
Blood diluted in phosphate buffer
Cells located in specific zones
Cell debris trapped separately
Jones, 2010
DC-iGDEP : Red Blood Cells
Staton, 2010, in press Electrophoresis
DC-iGDEP: Particle Separations
A-beta Amyloid Fibrils 1 x 20 nm courtesy Gilman/Kheterpal
1 micron and 200 nm polystyrene
Two New Designs
➡ Cells➡ Narrowest Gate: 20 µm➡ Widest Gate: 500 µm➡ Change in gate height (Δh) varies along channel
Δh (µm) = 50 25 10 5 3 2
➡ Proteins/Virions➡ Narrowest Gate: 1 µm➡ Widest Gate: 30 µm
Δh (µm) = 2 1
Modeling Results – Cells➡ Max value |E|2 :
➡ 2.1x1015 V2/m3 – 20 µm gate
➡ Min value :➡ 1.9x1013 V2/m3 – 500 µm gate
➡ Rate of change :➡ 1.3x – narrow gates➡ 1.1x – wide gates
∆
What does this all mean?
We develop revolutionary tools, tightly coupled to needs in the medical sciences Collaborations with pathologists, surgeons, instrument
companies, defense industry (along with physicists, mathematicians, engineers, biologist, other chemists)
Need to get to the biologically fluctuations in concentration to extract interpretable data
Sometimes that means pushing the detection limit or temporal resolution (cost)
Other times that means monitoring a large number of targets looking or patterns (meadow/ecology model)
What is it like to work with Dr. Hayes?
Good question! Please ask my current students. My students average 5 years to graduation
Earliest is 2.7 years, latest is 6.5 Work hard, play hard
Not much in the way of micromanaging Expect a lot, gentle corrections You will know more about your project than I by the time you graduate. Most
students tell me when they are ready. Social group Attend Conferences 3-5 first-author publications Highly supported: NSF, Fulbright & NIH fellows earned while in
group Looking for 1-2 students