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