Mark A. Hayes

1. 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

2. What would you need to provide the earliest possible detection of disease? We try to break things down into their simplest components, understand those and build back towards complexity. We are all chemists here, and therefore are reductionists-

3. 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

4. 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…

5. Analytics and Medical Science Three problems: 1) we don’t even know all the molecules & cells 2) we don’t have tools to measure these at the right timescales, cost and sensitivities 3) we don’t know how useful this would be (and can’t until we do it!)

6. Analytics and Medical Science Here’s where we come in: Building the best tools to Independently identify biomolecules in a short timeframe, in a cost efficient manner that is relevant to medical science (and fundamental biological studies) To augment other analytics (mass spectrometry, molecular recognition, spectroscopy, electrochemistry) to accomplish the same goals To learn exactly how sensitive and precise these measurements need to be (more later on this topic)

7. 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)

8. 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.)

9. Overall Technical Paradigm Flow stream shift or valve Flow Lysing or disruption chamber Fraction Collection from Dielectrophoresis

10. Overall Technical Paradigm Lysis and Pattern Generation (separation or array) Linear or multi-dimensional separations or and (some circumstances) Lysing or disruption chamber Array readout: molecular recognition & spectroscopy

11. and (some circumstances) Overall Technical Paradigm: Today’s Presentation Gradient Dielectrophoresis Electrophoretic Capture (Array)

12. Electrokinetic Forces Electro-osmosis (EO) Dielectrophoresis (DEP) Electrophoresis (EP)

13. Dielectrophoresis • DEP force depends on: • Particle size (r) • Medium permittivity (εm) • Clausius-Mossotti factor (fCM) • Electric field gradient (∇|E|2)

14. Generating Non-uniform Fields • Shaped electrodes • Expensive, complicated to fabricate • Electrochemical reactions at capture zones • Shaped insulators • Inexpensive, simple fabrication • Electrodes in remote reservoirs

15. Current Design FEK FDEP • EK Forces • FEK E, FDEP E2 • Opposing directions • Sawtooth insulators • Polydimethylsiloxane (PDMS) teeth shape E field • Sharp features yield intense gradients • Varied spacing forms distinct local gradients

16. Current Design A new dual-force gradient focusing technique similar in form to IEF: D IEF: f( d +z/d pH * E) f( d -z/d pH * E) R = f (dpH/dx, dz/dpH, E, D) FDEP D IGDEP: f( mDEP * DE2) f(mEK * E) R = f (dE/dx, dDE2 /dx, D)* *other factors also… FEK FDEP • EK Forces • FEK E, FDEP E2 • Opposing directions • Sawtooth insulators • Polydimethylsiloxane (PDMS) teeth shape E field • Sharp features yield intense gradients • Varied spacing forms distinct local gradients

17. DC-iGDEP: Particle Separations FEOF FDEP Chen et al. 2009 EK Linear separation device, similar to isoelectric focusing or other gradient techniques.

18. Predicted Capture Assuming nDEP Buffer solution PDMS FEOF FDEP

19. COMSOL Modeling of Field Properties COMSOL Calculation DEP Force (Centerline, Log Scale) Seven ‘teeth’ narrowest on right ∆ • Surface plot: local electrical potential, V • Contour lines: magnitude of electric field, |E| • Normalized arrows: direction of DEP force, proportional to |E|2

20. 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)‏ DC-iGDEP Particle Separations: Bacteria Pysher 2005: Hayes 2007

21. Blood diluted in phosphate buffer Cells located in specific zones Cell debris trapped separately DC-iGDEP : Red Blood Cells Jones, 2010

22. DC-iGDEP: Particle Separations A-beta Amyloid Fibrils 1 x 20 nm courtesy Gilman/Kheterpal Staton, 2010, in press Electrophoresis 1 micron and 200 nm polystyrene

23. Two New Designs Δh (µm) = 50 25 10 5 3 2 • Cells • Narrowest Gate: 20 µm • Widest Gate: 500 µm • Change in gate height (Δh) varies along channel Δh (µm) = 2 1 • Proteins/Virions • Narrowest Gate: 1 µm • Widest Gate: 30 µm

24. 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 ∆

25. 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)

26. 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