real time genomics for analyzing dynamic cell and tissue processes:...

Real Time Genomics for Analyzing Dynamic Cell and

Tissue Processes: Inflammation

Real Time Genomics for Analyzing Dynamic Cell and

Tissue Processes: Inflammation

Center for Engineering in MedicineMassachusetts General Hospital

Harvard Medical School Boston, MA

Center for Engineering in MedicineMassachusetts General Hospital

Harvard Medical School Boston, MA

Martin L. YarmushMartin L. Yarmush

Local InflammationLocal Inflammation

Systemic InflammationSystemic InflammationChanges in the host’s systemic

• energetic profile or metabolic state

• defensive posture

• We have known for over a century that inflammation is present in many disorders: anaphylaxis; environmental diseases (smoke inhalation, asbestos exposure, etc.); rheumatoid arthritis; gout; intestinal diseases; and autoimmune diseases; infections; burns; trauma.

• We have known for over a century that inflammation is present in many disorders: anaphylaxis; environmental diseases (smoke inhalation, asbestos exposure, etc.); rheumatoid arthritis; gout; intestinal diseases; and autoimmune diseases; infections; burns; trauma.

Why Study Inflammation?Why Study Inflammation?

Traumatic InjuryTraumatic Injury• Trauma, which is defined as an injury caused by a

physical force, includes the consequences of motor vehicle accidents, falls, drowning, gunshots, fires and burns, and stabbing or other physical assaults.

• Trauma kills more people between the ages of 1 and 44 than any other disease or illness: – 41 percent of all deaths from ages 1-4 – 46 percent of all deaths from ages 5-14 – 73 percent of all deaths from ages 15-24

• ~200,000 Americans of all ages die from trauma each year.

• >2.6 million are hospitalized from traumatic injury at a societal cost estimated at over $260 billion

• Trauma, which is defined as an injury caused by a physical force, includes the consequences of motor vehicle accidents, falls, drowning, gunshots, fires and burns, and stabbing or other physical assaults.

• Trauma kills more people between the ages of 1 and 44 than any other disease or illness: – 41 percent of all deaths from ages 1-4 – 46 percent of all deaths from ages 5-14 – 73 percent of all deaths from ages 15-24

• ~200,000 Americans of all ages die from trauma each year.

•• >2.6 million are hospitalized from traumatic injury at >2.6 million are hospitalized from traumatic injury at a societal cost estimated at over $260 billiona societal cost estimated at over $260 billion

• We have known for over a century that inflammation is present in many disorders: infections; burns; trauma; anaphylaxis; environmental diseases (smoke inhalation, asbestos exposure, etc.); rheumatoid arthritis; gout; intestinal diseases; and autoimmune diseases.

• In addition, over the past twenty years, many other diseases have been added to this list.

• We have known for over a century that inflammation is present in many disorders: infections; burns; trauma; anaphylaxis; environmental diseases (smoke inhalation, asbestos exposure, etc.); rheumatoid arthritis; gout; intestinal diseases; and autoimmune diseases.

• In addition, over the past twenty years, many other diseases have been added to this list.


Model of Chronic Inflammationin the Etiology of Cancer

Model of Chronic Inflammationin the Etiology of Cancer

Chronic inflammation

Replacementhyperproliferation

Mutation TumorROS/RNS

ROS/RNS

Cell damage Promotion

Initiation

↓ apoptosis↑ angiogenesis

Growthadvantage

• We have known for over a century that inflammation is present ininfections; in anaphylaxis; in environmental diseases (smoke inhalation, asbestos exposure, etc.); in rheumatoid arthritis; gout; autoimmune diseases; intestinal diseases; and autoimmune diseases.

• Over the past twenty years, other diseases have been added to this list.

• Thus, inflammation is really at the heart of health and disease not only in terms of symptomatology and sequelae, but also in terms of disease etiology.

• Although selected aspects of inflammation have been studied, no comprehensive quantitative model of the inflammatory process is currently available.

• Classical engineering analysis which comprises well-defined field equations and boundary value analyses, their prediction and experimental verification, is as yet premature. Identification of the players (i.e. different cell types, genes, proteins) and their dynamics must become an integral and early part of the engineering analysis.

• We have known for over a century that inflammation is present ininfections; in anaphylaxis; in environmental diseases (smoke inhalation, asbestos exposure, etc.); in rheumatoid arthritis; gout; autoimmune diseases; intestinal diseases; and autoimmune diseases.

• Over the past twenty years, other diseases have been added to this list.

• Thus, inflammation is really at the heart of health and disease not only in terms of symptomatology and sequelae, but also in terms of disease etiology.

• Although selected aspects of inflammation have been studied, no comprehensive quantitative model of the inflammatory process is currently available.

• Classical engineering analysis which comprises well-defined field equations and boundary value analyses, their prediction and experimental verification, is as yet premature. Identification of the players (i.e. different cell types, genes, proteins) and their dynamics must become an integral and early part of the engineering analysis.


WOUND, INFECTION,

TUMORglucose, APR

INFLAMMATORY CELLS

IL-1β, IL-6, TNFα

catecholamines, glucocorticoids, glucagon, insulin

amino acids

SKELETAL MUSCLE

IGFBP-1

NERVOUS SYSTEM

ENDOCRINE SYSTEM

Liver Plays A Central RoleLiver Plays A Central Role• A principal target of systemic inflammatory mediators• Supplies necessary components for immediate defense at site of injury• A principal target of systemic inflammatory mediators• Supplies necessary components for immediate defense at site of injury

New Physiologic StateNew Physiologic State

Glucose

PYR

Ac-CoAAmino Acids

Lactate

Urea

FA

UreaCycle

TCACycle

KetoneBodies

Glycogen

PPP

Glucose

PYR

Ac-CoAAmino Acids

Lactate

Urea

FA

UreaCycle

TCACycle

KetoneBodies

Glycogen

PPP

Hypermetabolism Defensive Posture

Systems for StudySystems for Study

Cell Culture

Perfused organ

Specificity & Experimental ControlSpecificity & Experimental Control

Releva

nce

Releva

nce

Whole Body

Animal

Hepatocytes in InflammationHepatocytes in Inflammation

InflammatoryMediators

Altered Gene andProtein

Expression

CHANGES INGENE EXPRESSION

gp130

hepatocyte

Describe the new physiologic state or phenotypeDescribe the new physiologic state or phenotype

Multiple Organ Dysfunction SyndromeMultiple Organ Dysfunction Syndrome

InjuryDays After Injury

Met

abol

ic R

ate

Multiple Organ Dysfunction

Syndrome and Death

200,000 patients/yr

1 3 7 14 21

Sho

ck

Res

usci

tatio

n

200,000 patients/yr

Systemic Inflammatory Response Syndrome (SIRS)

400,000 patients/yr

Basal Metabolic Rate

400,000 patients/yr

Infections and other “insults”

The systemic response to injury has an ebb phase of reduced metabolism for ~1 day followed by a flow phase of hypermetabolism that can last wks-mnthsThe systemic response to injury has an ebb phase of reduced metabolism for ~1 day followed by a flow phase of hypermetabolism that can last wks-mnths

• Understand why certain hypermetabolic physiologic states reverse and why some do not

• Understand how diverse dynamic stimuli are integrated by the cell’s intracellular signaling pathways to establish new physiologic states

• Develop techniques to characterize the dynamics of the cell response to diverse dynamic stimuli

• Understand why certain hypermetabolic physiologic states reverse and why some do not

• Understand how diverse dynamic stimuli are integrated by the cell’s intracellular signaling pathways to establish new physiologic states

• Develop techniques to characterize the dynamics of the cell response to diverse dynamic stimuli

Long Term GoalsLong Term Goals

Diverse Dynamic StimuliDiverse Dynamic Stimuli



Expression (PHENOTYPE)


gp130

hepatocyte

Pipetting – Many Inputs• multiple wash steps to remove stimuli• Not well suited for dynamic inputs

Pipetting – Many Inputs• multiple wash steps to remove stimuli• Not well suited for dynamic inputs

Classical Methods to Control Extracellular Inputs

Classical Methods to Control Extracellular Inputs

Needed: A Parallel Perfusion Culture System

Many Inputs and Dynamic Inputs

Perfusion Systems – Dynamic Inputs• Serial and low-throughput• Not well suited for many inputs

Perfusion Systems – Dynamic Inputs• Serial and low-throughput• Not well suited for many inputs

Microfluidic Parallel Perfusion Culture SystemsMicrofluidic Parallel Perfusion Culture Systems

Microfluidic Cell CultureMicrofluidic Cell Culture

Dynamic Intracellular SignalingDynamic Intracellular Signaling



Expression


gp130

hepatocyte

Calvano, Lowry Nature ‘05

DNA microarrays

Microarrays: Output-focused

Many Genes but Few Conditions

RT- PCR

Needed: A Tool to Study 10’s of Genes

Many Conditions and Time Points

1000’s of Plates

Gaudet, Sorger ‘05

Measuring Gene Expression DynamicsMeasuring Gene Expression Dynamics

GFP Fluorescent Living Cell Reporters

Time

Microfluidic Living Cell ArrayMicrofluidic Living Cell Array

1. GFP-based reporter systems

2. Microfluidic Living Cell Array

3. Automated time-lapse microscopy

GFP Reporter Cell LinesGFP Reporter Cell LinesGFP Reporter Cell Lines

• H35, a rat hepatoma cell line• Short half-life (2 h) EGFP as a reporter • A library of plasmids of regulatory

genes of inflammation

• H35, a rat hepatoma cell line• Short half-life (2 h) EGFP as a reporter • A library of plasmids of regulatory

genes of inflammation

RE RE RE CMVmin EGFP

Transcription factor binding sequence

enhanced d2EGFPCMV minimal promoter

RE

pd2EGFP

Response ElementsResponse ElementsResponse Elements

• NFκB: nuclear factor κB

• AP-1: activator protein 1

• HSE: Heat shock response element

• GRE: Glucocorticoid response element

• ISRE: interferon-stimulated response element

• STAT3/APRF: signal transducer and activator of transcription 3/acute phase response factor

• C/EBP: CCAAT enhancer binding protein

• HNF1: hepatocyte nuclear factor 1

• NFκB: nuclear factor κB

• AP-1: activator protein 1

• HSE: Heat shock response element

• GRE: Glucocorticoid response element

• ISRE: interferon-stimulated response element

• STAT3/APRF: signal transducer and activator of transcription 3/acute phase response factor

• C/EBP: CCAAT enhancer binding protein

• HNF1: hepatocyte nuclear factor 1

IKK

Caspases

JNK

AP-1

RIPIAP

TNF-α

NFκB

HSF1

ISRE

GR

STAT3IκBα

IL-1IL-6

DexIFN

HSP70

Jak

C/EBPERK

LPS

Cloning StrategyCloning StrategyCloning Strategy

Stimulation

Expansion

Negative Sorting

4.56 %

55.94 %

Stimulation

Stable Transfection

Positive sorting

Cloning StrategyCloning StrategyCloning Strategy

• Limited dilution for subcloning• Monoclonal screening by FACS & microscopy• Limited dilution for subcloning• Monoclonal screening by FACS & microscopy

NFκB AP-1

0

0.05

0.1

0.15

0 5 10 15 20 25

p65 TransFactor AssayRelative Difference in p65 binding

treated with +/- 10 ng/mL TNFαHeLad4NFkB_EGFP

time (hr.)

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15 20 25

p50 TransFactor AssayRelative Difference in p65 binding

treated with +/- 10 ng/mL TNFαHeLa_p50d4NFkB_p50

time (hr.)

Characterization: DNA Binding DynamicsCharacterization: DNA Binding Dynamics

Characterization: GFP Level DynamicsCharacterization: GFP Level Dynamics

0

10

20

30

40

50

1

1.5

2

2.5

3

3.5

4

4.5

5

0 5 10 15 20 25

d4EGFP Fluorescence and Protein following stimulation with 10 ng/mL TNFα

relative difference M2%

10 ng/mL0 ng/mL

time (hr.)

FAC

S EG

FP F

luor

esce

nce

(rel

ativ

e di

ffere

nce

in m

2%)

Western B

lot Analysis

(relative difference in EG

FP protein)TNFα10 ng/mL

Characterization: Cell Cycle DependenceCharacterization: Cell Cycle Dependence

Cell Synchronization Improves Homogeneity of Response

Characterization: Response to PulsesCharacterization: Response to PulsesNFκB Reporter Response to 2hr Pulse of 10ng/mL TNF-α

Time (hr)

0 10 20 30 40

Nor

mal

ized

Flu

ores

cenc

e In

tens

ity

0.0

0.5

1.0

1.5

2.0

2.5

TNF-α

Reporter Fluorescence

Destabilized GFP enables transient response monitoring

Reporter Responses have Different KineticsReporter Responses have Different Kinetics

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 2 4 6 8 10 12 14 16 18

Stimulation Time (hour)

Nor

mal

ized

GFP

Exp

ress

ion

NFκBAP-1HSEGREISRESTAT3

Characterization: GFP in Different ClonesCharacterization: GFP in Different Clones

Summary

Time

• NFκB

• AP-1

• HSE

• GRE

• ISRE

• STAT3/APRF

• C/EBP

• HNF1

Plan: Increase Library to 50-100 different clones


1. GFP-based reporter systems

2. Microfluidic Living Cell Array

3. Automated time-lapse microscopy

Microfluidic Bioreactor FabricationMicrofluidic Bioreactor Fabrication

Silicon Master Mold:Photoresist is spin coated and exposed through high-resolution photomask

Polymer Replicas:Cast, cure, and peel PDMS silicone elastomer

Fluidic Assembly: Drill holes, bond to glass substrate, and autoclave sterilize

Microfluidic Cell CultureMicrofluidic Cell Culture

Surface Modification:Fibronectin is physisorbedand bubbles purged

Cell Seeding:Inject high concentration cell suspensions

Long-term Culture:Maintain cells by continuous-flow (gravity or syringe pump) or discrete medium changes (using syringe)

Monitor GFP Reporter DynamicsMonitor GFP Reporter Dynamics

Time

Results (IL-1 Stimulation)

NFκB

ISRE

STAT3

HSE

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Time (hrs)

Nor

mal

ized

Inte

nsity

NFkB MicroscopyGRE MicroscopyGRE FACSNFkB FACS

0

10

20

30

40

50

60

70

80

0 200 400 600 800 1000

Intensity of Green Fluoresence

Num

ber o

f Cel

ls

Control2 hr4 hr6 hr8 hr13.5 hr18 hr25 hr

0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200

Intensity of Green Flourescence

Num

ber o

f Cel

ls

control

5 hr

8 hr

18 hr

GRE

NFκB

Characterization: FACS and LCACharacterization: FACS and LCA

Characterization: LCA vs CultureCharacterization: LCA vs Culture

Comparison of Microfluidic and Culture Plastic Responses

Time (h)

0 2 4 6 8 10 12

Nor

mal

ized

Flu

ores

cenc

e

0.0

0.2

0.4

0.6

0.8

1.0Dynamic LCAStatic Dish

SummarySummary

• We can reproducibly perform microfluidiccell culture

• Image dynamic GFP reporting

• Results comparable to traditional methods (FACS and Cell Culture)

Multiple Inputs/OutputsMultiple Inputs/Outputs

Microfluidic Living Cell ArrayMicrofluidic Living Cell Array• 50mm deep channels

• 256 cell culture chambers (

Yellow: Cell Culture Channels

Red and Green: Valve Control Channels


Microfluidic ValvesMicrofluidic Valves

Valve Closed

PDMS

Valve control channel

PDMS

Apply negative pressure

Valve Open

Cell SeedingCell Seeding

Cell Seeding in RowsCell Seeding in Rows

Valves Prevent Cross-talk during Cell Seeding

Cytokine Stimulation in ColumnsCytokine Stimulation in Columns

Valves Prevent Cross-talk during Cell Stimulation

Cell Lines Grow to Confluency in the ArrayCell Lines Grow to Confluency in the Array

Seeding Outlet

Stimulus Outlet

8 Clones8 Stimuli4 Replicates24 Time Points>5000 stpm/day

Valve Control 1

Valve Control 2

Multiple Cell Types

Multiple Soluble Stimuli

Nontransfected

NFκB

AP-1

STAT3

ISRE

GRE

HSE

Constitutive GFP

MediumTNF-α

IL-1β IL-6 INFγ Dex

TNF+IL1+IL6

TNF+IL1+IL6+Dex

Response to Inflammatory MediatorsResponse to Inflammatory Mediators

Dynamic Gene ExpressionDynamic Gene Expression

NF-kappaB Response to TNF-a

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 5 10 15 20 25 30 35

Tim e (hour)

Nor

mal

ized

Flu

ores

cenc

e (R

FU)

IL-1 Stimulation

NFκB

ISRE

STAT3

HSE

TNF-α-Induced DynamicsTNF-α-Induced Dynamics

STAT3HSE

TNF-α

NFκB

Time (hours)

0 10 20 30

Nor

mal

ized

Inte

nsity

0.0

0.2

0.4

0.6

0.8

1.0

NFκBSTAT3HSE

TNF-α-Induced DynamicsTNF-α-Induced DynamicsNFκB

STAT3

HSE

Time

STAT3HSE

TNF-α

IKK JAKJNK

ERK

IL-6

NFκB

?? ?

HSE Reporter Dynamics are Stimulus-dependentHSE Reporter Dynamics are Stimulus-dependent

HSE

TNF-α IL-6IL-1

Time (hours)

0 10 20 30N

orm

aliz

ed In

tens

ities

0.0

0.2

0.4

0.6

0.8

1.0

TNF-αIL-1IFNγTNF-α/IL-1/IL-6TNF-α/IL-1/IL-6/Dex

HSE Reporter Dynamics are Stimulus-DependentHSE Reporter Dynamics are Stimulus-Dependent

TNF-α

IL-1

IFNγ

TNF-αIL-1 IL-6

TNF-αIL-1 IL-6 Dex HSE

TNF-α IL-6IL-1

Dynamic Inputs

Neural Synapse

Endocrine HormonesR&D Systems Toner, Mitchell ‘03

Inflammation

Therapeutics

Human LPS Model

Cell

LPS

Gram-negativebacteria

LBP

TLR-4

Cytokineexpression

Cell

LPS


LPS


LPS



LBPLBP

TLR-4TLR-4

Cytokineexpression

Concentration ControlConcentration ControlConcentration Control

Time

Con

cent

ratio

n

Channel Number

1 2 3 4 5 6 7 8

[TN

F-α

] (ng

/ml)

0

2

4

6

8

10

12

NFκB Reporter Response to TNF-α Concentration

Time (h)0 2 4 6 8 10 12

Ave

rage

Inte

nsity

per

Cel

l (A

FU)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.010.00 ng/ml9.04 ng/ml7.24 ng/ml5.06 ng/ml2.85 ng/ml1.39 ng/ml0.36 ng/ml0 ng/ml

Dose Response of NFκB ActivationDose Response of Dose Response of NFNFκκBB ActivationActivation

Control PatternsControl PatternsControl Patterns

α

time

θ1

θ2

θ3

θ4

Duration Control

Transient Cytokine StressTransient Cytokine Stress

Recovery Period Control

α

time

θ1

θ2

θ3

θ4

Heat Shock ProtectionHeat Shock Protection

α

time

Period

f

2f

4f

8f

θ1

θ2

θ3

θ4

Frequency Control

Hormonal ControlHormonal Control

QMedium

QMediumQStimulus

Waste

ΔP

Time

ΔTθi

Time

Time

Cell Visualization Chamber

Stimulus

Stimulus Medium

QQ Q

χ ≡+

ii

Stimulus

cc

θ ≡

Increasing χ

Duration Control: Flow-encoded SwitchingDuration Control: Duration Control: Flow-encoded Switching

Weeks vs Hours

Transient Cytokine StressTransient Cytokine StressQMediumQStimulus

Waste

χ

Time

θ1

θ20Time (hr)

0 2 4 6 8 10 12 14 16-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.300 min15 min20 min25 min40 min50 min55 min

2 hr 5 hr 8 hr 11 hr 20 hr Time

Duration of TNF-α Exposure (min)

0 10 20 30 40 50 60 70N

orm

aliz

ed N

F κB

Act

ivat

ion

Leve

l0.0

0.2

0.4

0.6

0.8

1.0

1.2

MicrofluidicHoffman

Simultaneous Concentration & Duration ControlSimultaneous Concentration & Duration ControlSimultaneous Concentration & Duration Control

Duration

Concentration

M

M

MICROFLUIDICS TO CONTROL STIMULI

DYNAMIC GENE EXPRESSION VIA CELL REPORTERS

CELLULAR RESPONSES DURING INFLAMMATION

Can we explore this in the context of disease?

Hepatic Steatosis: “Fatty Liver”Hepatic Steatosis: “Fatty Liver”

• Nonalcoholic Fatty Liver Disease or NAFLD: 20% – association w/Metabolic Syndrome and Obesity

• Steatosis ↑ susceptibility to subsequent injury

• “Second Hits” (e.g. ischemia) can lead to chronic inflammation and nonalcoholic steatohepatitis or NASH: 2-3%

• Chronic inflammation in the liver can progress to cirrhosis and hepatocellular carcinoma

Hepatic Steatosis: “Fatty Liver”Hepatic Steatosis: “Fatty Liver”

Hepatosteatosisw/inflammatory infiltrate

Perivenular fibrosis

Hepatic Inflammation is Complex and Dynamic

Fatty ChangeNormal Liver

Reporter Cells Become SteatoticReporter Cells Become Steatotic

Control Medium Fatty Acid and Hormone Supplementation

Fatty Acid Uptake in Reporter CellsFatty Acid Uptake in Reporter Cells

0.0

0.4

0.8

1.2

1.6

2.0

Control Oleic Acid (2 mM) Oleic Acid (4 mM)

FFA

(mM

)

FFA Decrease

Nile Red Staining 0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Control Oleic Acid (2 mM) Oleic Acid (4 mM)

Trig

lyce

rides

(mg/

ml)

Triglycerides

FFA Uptake

Triglyceride Content

Responses in Steatotic NFκB Reporters

Decreased Basal Activity Decreased TNF-a ResponseDecreased IL-6 Response

• Infected cells, cells metabolizing toxins or drugs

• Cells in different cell cycle positions, cells fed different diets

Hepatocytes Are Not Alone

Dynamic Tissue MicrosystemsMicroscale Models of Disease

Automated Stage 37°C & CO2 Control

rsGFP YFP CFP PhaseMulti-parameter Detection High Content Screening (HCS)

Dynamic Tissue Microsystems

Disease Process Fingerprinting

Dynamic Clustering

Mathematical Modeling

time

Experiment

Valve and Pump Control

Complex System Identification

•Integrated Microfluidics•Gene Expression Monitoring•100’s of Parallel Experiments•Study Dynamics of Disease Processes•Drug and Toxin Testing

Stimuli ResponseFunctional Tissue UnitSi(t) Rj(t)

Steroid/Drug metabolismCholesterol breakdownTriglyceride breakdownCholesterol biosynthesisFatty acid biosynthesisGlycolysisFatty acid biosynthesisBile acid synthesis

Fatty acid transportFatty acid beta oxidation

Heme biosynthesisCholesterol importFatty acid import

12

3

4

5

6

7

•Control Response with Therapeutics

Drug or Nutrition

fluor

esce

nt s

igna

l

Genes

Inducers

time

kCCDdtdC

−∇= 2

Applicable to Various TissuesInputs Functional

Tissue UnitOutputs

Mechanical – Pressure, Shear Stress, Tensile Resistance

Chemical – Neurohormonal, Cytokines, Oxygenation

General - Morphology, Cell Shape, Proliferation, Migration, Apoptosis

Cell-Specific Function - Gene Expression, Protein Interactions, Protein Secretion

Bone Neurons

Skin Pancreas

Muscle Fibers

Liver

Linearized Pancreatic IsletsLinearized Pancreatic Islets

δ Cells

Islet

β CellsArteriole Venule

Microfabricated bioreactor

α Cells

Tissue AssemblyTissue AssemblyInputs Functional

Tissue UnitOutputs

Mechanical – Pressure, Shear, Tensile Resistance

Chemical – Neurohormonal, Cytokines, Oxygenation

General - Morphology, Cell Shape, Proliferation, Migration, Apoptosis

Cell-Specific Function - Gene Expression, Protein Interactions, Protein SecretionCellular – Neutrophils,T Cells

Layered Structure

+Collagen

+

Day 4; separation distance: ~ 150 µm

VEGF

HIF1α

HIF1β

Hypoxia

HIF1

HRE

5’

3’

VEGF R

ICAM-1

VCAM-1E-Selectin

VEGF

NFkBNFkB

Hepatocytes-Endothelial Cell Signaling

1. Ischemia

2. Hepatocyte HIF-1α activation

3. VEGF expression and secretion

4. Endothelial Cell NFkB activation

5. ICAM expression

6. Reperfusion

7. Neutrophil Capture

ISCHEMIA REPERFUSION

Time

MICROFLUIDIC ARRAYS

STIMULUS CONTROL

GFP REPORTER LIBRARY

DYNAMIC GENE EXPRESSION

DYNAMIC RESPONSES TO INFLAMMATORY CYTOKINES

DISEASE DYNAMICS

STEATOSIS AND I/R

• Use the LCA and DTM to understand the dynamics of the cell signaling response to diverse dynamic stimuli

• Understand how diverse dynamic stimuli are integrated by the cell’s intracellular signaling pathways to establish a new physiologic state

• Make headway in understanding the nature of reversible versus non-reversible physiologic states

• Find collaborators with interesting problems

• Use the LCA and DTM to understand the dynamics of the cell signaling response to diverse dynamic stimuli

• Understand how diverse dynamic stimuli are integrated by the cell’s intracellular signaling pathways to establish a new physiologic state

• Make headway in understanding the nature of reversible versus non-reversible physiologic states

• Find collaborators with interesting problems

Future DirectionsFuture Directions

AcknowledgementsAcknowledgements

FUNDING

NIH BRP AI063795 (Real Time Genomics) NIH P41 EB002503 (Dynamic Tissue Microsystems)

INVESTIGATORS

Kevin King, Sihong Wang, Deanna Thompson, Arul Jayaraman, Ken Weider, Daniel Irimia, Octavio Hurtado, Amol Janorkar, Rohit Jindal, Koby Nahmias, Zak Megeed, Mehmet Toner, Jeff Morgan

Real Time Genomics for Analyzing Dynamic Cell and Tissue Processes: InflammationLocal InflammationSystemic InflammationTraumatic Injury �Model of Chronic Inflammation�in the Etiology of CancerNew Physiologic StateSystems for StudyHepatocytes in InflammationMultiple Organ Dysfunction SyndromeLong Term GoalsDiverse Dynamic StimuliDynamic Intracellular SignalingGFP Reporter Cell LinesResponse ElementsCloning StrategyCloning Strategy Characterization: Cell Cycle DependenceCharacterization: Response to PulsesReporter Responses have Different KineticsMicrofluidic Bioreactor FabricationMicrofluidic Cell CultureMonitor GFP Reporter DynamicsResults (IL-1 Stimulation)Characterization: LCA vs Culture Microfluidic Living Cell ArrayMicrofluidic ValvesCell SeedingCell Seeding in RowsCytokine Stimulation in ColumnsIL-1 StimulationDynamic InputsHuman LPS ModelHepatic Steatosis: “Fatty Liver”Hepatic Steatosis: “Fatty Liver”Reporter Cells Become SteatoticFatty Acid Uptake in Reporter CellsResponses in Steatotic NFkB ReportersHepatocytes Are Not AloneApplicable to Various TissuesLayered Structure Hepatocytes-Endothelial Cell SignalingFuture Directions

real time genomics for analyzing dynamic cell and tissue processes:...

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