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Microfluidics
Dave EddingtonAssistant Professor
Department of Bioengineering
July 10, 2008
Outline• Background of BioMEMS
– Microfluidics– Micropatterning– microstructures
• Background of soft lithography• Example projects
– Sickle cell device– Hypoxia– Brain slice device– Cnidocysts
Microtechnology• Photolithography
– Microelectronics• Deposit metals• Diffuse dopants
• Micromachining– Microelectromechanical Systems
• Deposit structural material• Bulk Etch• DLP, Accellerometer
• Soft Lithography– BioMEMS
• Mold PDMS– Microfluidics– Micropatterning– Micromolding
BioMEMS
MEMS
microelectronics
BioMEMS: Micropatterning• Use PDMS network to define surface chemistries
– Advesive– Non-Adhesive
• Control over cells/surfaces• Control cell-matrix interactions• Control cell-cell interactions• Control cell shape
McBaeth et al, Developmental Cell, 05
BioMEMS: Microstructures
• Use PDMS to measure cellular forces• Use PDMS stamp to create wells in gels
Nelson et al, Science, 2006Tan et al, PNAS, 2003
BioMEMS: Microfluidics
• Integrate multiple tasks onto single device• Short diffusion lengths• Laminar flow• Large surface to volume ratio• Similar length-scale as cells• Very small volumes
Lee et al, Science, 05
Why we like microfluidics?
• Leveraging the Microscale– Rapid diffusion– Large surface to volume ratio– Process integration– Microscale systems for microscale needs
Lithography• From Greek, meaning “writing in stone”• A method of patterning a layer of photosensitive material
based on radiation-induced structural degradation• Photosensitive material
– Material that experiences a change in its physical properties when exposed to a radiation source
– Photoresist– Depending on chemical nature, produce either a
positive or a negative image
Lithography
Lithography
SiPR
SiPR
SiSi
“+” “—”
David T. Eddington1*, John M. Higgins2,3, Lakshminarayanan Mahadevan2,4
and Sangeeta N. Bhatia1,5
1Division of Health Sciences and Technology, Harvard-MIT, 2School of Engineering and Applied Sciences, Harvard University,
3Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, 4Department of Systems Biology, Harvard Medical School,
5Department of Electrical Engineering and Computer Science, MIT,*Current Address: Department of Bioengineering, University of Illinois at Chicago
Microfluidic Model of a Sickle Cell CrisisMicrofluidic Model of a Sickle Cell Crisis
What is sickle cell disease?
• The molecular defect was identified over 60 years ago• A single nucleotide change leads to an amino acid
substitution• Sickle hemoglobin molecules polymerize when
deoxygenated• The polymers form long fibers and distort the red blood cell
membrane and “sickle” the cell• Life expectancy = 45 years with one hospitalization/year
http://fig.cox.miami.edu/~cmallery/150/chemistry/hemoglobin.jpghttp://users.rcn.com/jkimball.ma.ultranet/BiologyPages/S/SickleMutation.gif
Vaso-occlusion is a MultiscaleProcess
• Multiscale processes (length & time)– 0.1s,10 nm: polymerization of hemoglobin S– 0.1s, 10 μm: cell sickling– 1000s, 100-μm: vessel jamming
In Vitro Model
• Map out phase space – f(O2, Q, x)– Each occlusion = point
1
2
3
4
5
Experimental Setup
Oxygen Drop
0 20 40 60 80 100 120−2
0
2
4
6
8
10
12
Time (s)
[O2] (
%)
Movies
• Video on method• 7 μm channels• In a 250 μm channel
Occlusion and Relaxation
0 100 200 300 400 500 6000
0.2
0.4
0.6
0.8
1
1.2
Time (s)
Vel
ocity
(no
rmal
ized
to in
itial
)
Occlusionτ = 124 sRelaxationτ = 22 s
0
10
[O2] (
%)
Time (s)
Controls
0 100 200 300 400 500 6000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
Vel
ocity
(no
rmal
ized
to in
itial
)
Normal (0% HbS)Heterozygous (33% HbS)
Phase Space of Occlusion
Medical Intervention Validation
0 200 400 600 800 10000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
Vel
ocity
(no
rmal
ized
to in
itial
)
Untreated (HbS = 78%)τ = −90 sTreated (HbS = 31%)τ = −404 s
0
10
[O2] (
%)
Time (s)
Preventing Occlusion
0 50 100 150 200 250 300 350 4000
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (s)
Vel
ocity
(no
rmal
ized
to in
itial
)
0% O
2; 0% CO
τ = −35 s0% O
2; 0% CO
τ = −31 s0% O
2; 0% CO
τ = −35 s0% O
2; 0.01% CO
τ = −358 s0% O
2; 0.01% CO
τ = −95 s
0
0.01
[CO
] (%
)
0 0
10
[O2] (
%)
Time (s)
Microfluidic Sickle Cell Model
• It is possible to evoke, control and inhibit vaso-occlusion in a minimal microfluidic model
• Oxygen-dependant sickle hemoglobin polymerization and melting are enough to recreate vaso-occlusion
• Clinical interventions can be validated• Test bed for new therapeutics
– Generalizable to other hematological diseases
Characterizing MultiCharacterizing Multi--Modal Tissue Function in a Modal Tissue Function in a Microfluidic DeviceMicrofluidic Device
Javeed Shaikh Mohammed, Wang Yong, Tricia Harvat, Jose Oberholzer,
David Eddington
Islet Quantification: Pre Implantation
• Islet characterization: viability, purity, and sterility
• Transplant η ↔ Islet function • Our goal: high-throughput
platform for use in islet isolation arena
http://transplant.hospital.uic.edu/transplant/islets.html
Function = ?
• Specific aims: – Simple microfluidic device for perfusion and
imaging– Evaluate islet functionality
Cross-sectional view of device
Mouse Islets: Dynamic Perfusion
C57B6 mice islets (25 per perfusion chamber)
Balb/c mice (25 per perfusion chamber), Fura-2: 5 μM
Multimodal Function AnalysisMouse Islets: Ca2+ imaging & insulin
ELISA
Human islets (100 per perfusion chamber) [n=6]
Human Islets: Dynamic Perfusion
Human islets (100 per perfusion chamber), Fura-2: 5 μM
Multimodal Function AnalysisMouse Islets: Ca2+ imaging &
insulin ELISA
Human Islets Transplanted to Mice (Gold Standard to Quantify Islet
Function)
Non potent islets
Potent islets
1000 human islets transplanted into nude mice
Evaluating Human Islets: Highly Variable
Batch # KRB2 14 mM KRB2 KCl KRB2 KRB2 14 mM KRB2 KCl KRB2H249 0.13 1.80 0.42 1.56 0.14 2.26 82.40 5.28 140.60 29.03H251 0.21 0.65 0.12 1.43 0.08 0.05 40.56 2.54 154.50 2.31H256 0.00 0.17 0.03 0.51 0.05 0.71 89.40 9.41 210.00 0.10H259 0.01 0.35 0.11 0.82 0.27 0.88 375.50 58.06 263.20 3.33H261 0.01 0.16 0.05 0.20 0.03 1.43 125.00 2.18 94.36 7.61
AUC-FURA2 AUC-Insulin ELISA
Batch # KRB2 8 mM KRB2 KRB2 12 mM KRB2 KRB2 16.7 mM KRB2H249 0.79 104.10 15.52 38.43 197.20 17.95 1.45 165.20 21.19H251 0.21 188.40 17.07 0.71 201.40 4.71 0.24 193.90 12.65H252 0.13 293.40 1.61 11.42 480.40 35.88 26.70 802.10 24.41H253 0.00 105.00 4.11 1.87 94.17 4.88 0.39 171.60 3.10H256 2.48 101.20 8.83 1.41 360.00 8.07 5.33 420.80 38.14H259 0.17 105.90 7.80 4.70 196.20 14.32 0.32 160.10 23.50
AUC-8 mM AUC-12 mM AUC-16.7 mM
High Throughput High Throughput Oxygenation Oxygenation
Oxygen is a Key Metabolic Variable
• Current Tools: Hypoxic Chambers– Crude, inefficient, and
problematic• Cannot replicate
gradients of oxygen– found across all tissues in
every animal• Need a better too to study
– Development– Angiogenesis– Cancer– Hematopoiesis– Drug Toxicity
System Design: Add-on for Standard Lab Materials
• Modular Platform– Multiwell format – Diffusion through
PDMS
Oxygen Quantification
Microfluidic Brain Slice DeviceMicrofluidic Brain Slice Device
Hugo Caicedo, Javeed S. Mohammed, Chris P. Fall, and David Eddington
Current Approaches for Delivery
• Bathe entire slice – Imprecise– Simple
• Micropipette picospritzer– Precise– Bulky– Separate controller for each pipette
Simple
• Moudular add on for a standard perfusion chamber
Precise
• Microfluidic Stimulation– Simultaneous stimulation of multiple regions
– High spatial and temporal precision
Hans-Ulrich Dodt, Nature Methods 2007
Device Fabrication
http://www.jove.com/index/Details.stp?ID=302
Fluid Delivery: Passive Pumping
• Steady flow without external pumps
Microfluidic Brain Slice Device Design
• Adapt to commonly used materials
Fluorescence Quantification
• “Paint” neuromodulators
Fluorescence Quantification
• Deliver a bolus
Compatible with electrophysiology
3D Modeling
Nematocysts as Part of Drug Nematocysts as Part of Drug Delivery PlatformDelivery Platform
Shawn C. Oppegard, Peter Anderson*, and David Eddington
*University of Florida, Whitney lab for Marine Biology
Overview• Cnidarian biology• Aim of project
– Bioleverage nematocysts• Preliminary results
– Material puncture tests– Lectin binding– Optical Tweezing
• Future directions– Nematocyst patterning
and immobilization
Nematocysts biology• Nematocysts are the venom
delivery system in cnidariananimals
– Phylum cnidaria includes the jellyfish
– Nematocysts are specialized organelles contained within cnidocytes
• Prey contact induces discharge of functional stinger
• Discharge is one of the fastest movements in animal kingdom
– Penetrates hard fish scales– Occurs in less than a microsecond– 5x106 g acceleration– 7 GPa pressure at thread tip
@
http://oceanexplorer.noaa.gov
http://www.reefland.com
http://www.beachhunter.net
Nematocyst discharge in an ex vivotentacle
Nematocysts as Part of a Drug-Delivery Platform
• Ultimate Goal:– Bioleverage nematocysts– Microfabricate containment
wells for nematocysts
• Nematocysts attractive as miniature hypodermic needles– Efficient– Very stable– Can be triggered chemically or
electrically– Very small thread diameter
• Aim is to genetically re-engineer– Dr. Peter Anderson at the
Whitney Marine Biology Lab-University of Florida
DischargeStimulation
Nematocyst
Drug-Delivery“Patch”
Incorporation
Isolated nematocyst discharge studies
• Dry in 25 mM EDTA and then rehydrate in water– EDTA chelates calcium– Discharge is a calcium-dependent process
• Problem: Reorientation of cysts after firing when not anchored– Do not puncture most test materials
Rehydration
Tentacle-contained nematocyst discharge studies
• Best case scenario– Immobilized– Physiological discharge due
to mechanical stimulation of cnidocil
• Tentacles from 3 different animals tested– Chrysaora
• Initial trials– Cladonema
• Stenotele nematocysts– Physalia
• Very long threads (~1 mm vs. ~15 µm capsule diameter)
www.palaeos.org
www.paleobio.org
www.ocean-life.info
Chrysaora
Cladonema
Physalia
5 cm
10cm
Szczepanek, J. Cell Science, 2001www.palaeos.org
Stenotele
Puncture Mechanics Assessment
• Need to assess the puncture mechanics of the thread– Trigger discharge of nematocysts into adjacent material
• Tested materials with gamut of elastic moduli (Eskin=75 kPa)
– Gelatin ~ 20 kPa– Polyacrylamide ~ 60 kPa– Teflon ~ 0.1 MPa– Latex ~ 0.8 MPa– Polydimethyl siloxane (aka silicone, PDMS) ~ 1 MPa Starting Point– Nitrile ~ 2.6 MPa– PVC (Saran wrap) ~ 250 MPa– Polycarbonate ~ 2 GPa– Aluminum foil ~ 70 GPa– Glass ~ 90 GPa
• Elastic modulus as the material characteristic– Not measuring actual puncture stress– E is order of magnitude approximation
Soft
Hard
Physalia: Puncture tests• Went to Florida
– Physalia cultured at Whitney marine biology lab
• Method– Excised tentacles– Nematocysts discharge in
response to:• EGTA solution• Mechanical stimulation
– Tweezer prodding
• Note: A similar protocol was followed for Chrysaora at UIC
Physalia and Chrysaora: Puncture tests (cont.)
• Started with PDMS microchannels because easiest– Clear visualization of cross-sectional penetration– Tentacle pulled inside– Stimulated discharge with EGTA– Observe penetration with dissecting and compound scope
• Used films for all other materials– Place test material films on top of Physalia tentacle– Stimulate discharge with tweezers– Observe penetration with dissecting scope
600 microns
200 microns
Physalia nematocysts elastic modulus puncture threshold is ~1
MPa– Gelatin ~ 20 kPa– Polyacrylamide ~ 60 kPa– Teflon ~ 0.1 MPa– Latex ~ 0.8 MPa– PDMS ~ 1 MPa
----------THRESHOLD-----------– Nitrile ~ 2.6 MPa– PVC ~ 250 MPa– Polycarbonate ~ 2 GPa– Aluminum foil ~ 70 GPa– Glass ~ 90 GPa
Penetration
NoPenetration
Soft
Hard
•Chrysaora could not puncture PDMS
•Should use Physalia for patch
Future Work: Lectin binding as means of nematocyst
immobilization• Isolated nematocysts to be used, not
tentacles– Need to immobilize and possibly orient
• Lectins– Sugar-binding proteins– Sugar moeities present on surface of
nematocysts• Fluorophore-conjugated lectin binding to
nematocyst:– Lectins bind to apical surface of
nematocysts in Cladonema and Physalia• Could bind nematocysts to PDMS
membrane
• Explore other, basal-localized receptors to Physalia
Summary• BioMEMS is an enabling technology• Simple device design streamlines dissemination
– Device complexity limits practicality• We have lots of exciting projects
– Enabling projects• Microfluidic sickle cell model • Islet quantification• Brain slices• High throughput hypoxia
– Bioleveraged• Cnidocyst drug delivery
Acknowledgements• BML lab
– Javeed Mohammed– Hugo Caicedo– Kihwan Nam– Shawn Oppegard
• Collaborators– Jose Oberholzer (UIC)- islets– Yong Wang (UIC) - islets– Chris Fall (UIC) – brain slice– Peter Anderson (UF) – jellyfish
• HST (sickle cell)– Sangeeta Bhatia– John Higgins– Lakshminarayanan Mahadevan
• Funding– NIH - NRSA– DARPA – Alfred P. Sloan Foundation