real time genomics for analyzing dynamic cell and tissue processes:...
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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
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Local InflammationLocal Inflammation
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Systemic InflammationSystemic InflammationChanges in the host’s systemic
• energetic profile or metabolic state
• defensive posture
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• 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?
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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
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• 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.
Why Study Inflammation?Why Study Inflammation?
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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
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• 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.
Why Study Inflammation?Why Study Inflammation?
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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
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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
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Systems for StudySystems for Study
Cell Culture
Perfused organ
Specificity & Experimental ControlSpecificity & Experimental Control
Releva
nce
Releva
nce
Whole Body
Animal
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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
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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
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• 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
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Diverse Dynamic StimuliDiverse Dynamic Stimuli
InflammatoryMediators
Altered Gene andProtein
Expression (PHENOTYPE)
CHANGES INGENE EXPRESSION
gp130
hepatocyte
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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
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Microfluidic Parallel Perfusion Culture SystemsMicrofluidic Parallel Perfusion Culture Systems
Microfluidic Cell CultureMicrofluidic Cell Culture
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Dynamic Intracellular SignalingDynamic Intracellular Signaling
InflammatoryMediators
Altered Gene andProtein
Expression
CHANGES INGENE EXPRESSION
gp130
hepatocyte
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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
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GFP Fluorescent Living Cell Reporters
Time
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Microfluidic Living Cell ArrayMicrofluidic Living Cell Array
1. GFP-based reporter systems
2. Microfluidic Living Cell Array
3. Automated time-lapse microscopy
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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
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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
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IKK
Caspases
JNK
AP-1
RIPIAP
TNF-α
NFκB
HSF1
ISRE
GR
STAT3IκBα
IL-1IL-6
DexIFN
HSP70
Jak
C/EBPERK
LPS
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Cloning StrategyCloning StrategyCloning Strategy
Stimulation
Expansion
Negative Sorting
4.56 %
55.94 %
Stimulation
Stable Transfection
Positive sorting
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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
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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
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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
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Characterization: Cell Cycle DependenceCharacterization: Cell Cycle Dependence
Cell Synchronization Improves Homogeneity of Response
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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
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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
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Summary
Time
• NFκB
• AP-1
• HSE
• GRE
• ISRE
• STAT3/APRF
• C/EBP
• HNF1
Plan: Increase Library to 50-100 different clones
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Microfluidic Living Cell ArrayMicrofluidic Living Cell Array
1. GFP-based reporter systems
2. Microfluidic Living Cell Array
3. Automated time-lapse microscopy
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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
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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)
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Monitor GFP Reporter DynamicsMonitor GFP Reporter Dynamics
Time
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Results (IL-1 Stimulation)
NFκB
ISRE
STAT3
HSE
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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
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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
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SummarySummary
• We can reproducibly perform microfluidiccell culture
• Image dynamic GFP reporting
• Results comparable to traditional methods (FACS and Cell Culture)
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Multiple Inputs/OutputsMultiple Inputs/Outputs
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Microfluidic Living Cell ArrayMicrofluidic Living Cell Array• 50mm deep channels
• 256 cell culture chambers (
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Yellow: Cell Culture Channels
Red and Green: Valve Control Channels
Microfluidic Living Cell ArrayMicrofluidic Living Cell Array
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Microfluidic ValvesMicrofluidic Valves
Valve Closed
PDMS
Valve control channel
PDMS
Apply negative pressure
Valve Open
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Cell SeedingCell Seeding
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Cell Seeding in RowsCell Seeding in Rows
Valves Prevent Cross-talk during Cell Seeding
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Cytokine Stimulation in ColumnsCytokine Stimulation in Columns
Valves Prevent Cross-talk during Cell Stimulation
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Cell Lines Grow to Confluency in the ArrayCell Lines Grow to Confluency in the Array
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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
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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
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TNF-α-Induced DynamicsTNF-α-Induced Dynamics
STAT3HSE
TNF-α
NFκB
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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
?? ?
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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
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Dynamic Inputs
Neural Synapse
Endocrine HormonesR&D Systems Toner, Mitchell ‘03
Inflammation
Therapeutics
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Human LPS Model
Cell
LPS
Gram-negativebacteria
LBP
TLR-4
Cytokineexpression
Cell
LPS
Gram-negativebacteria
LPS
Gram-negativebacteria
LPS
Gram-negativebacteria
Gram-negativebacteria
LBPLBP
TLR-4TLR-4
Cytokineexpression
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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
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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
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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
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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
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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?
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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
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Hepatic Steatosis: “Fatty Liver”Hepatic Steatosis: “Fatty Liver”
Hepatosteatosisw/inflammatory infiltrate
Perivenular fibrosis
Hepatic Inflammation is Complex and Dynamic
Fatty ChangeNormal Liver
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Reporter Cells Become SteatoticReporter Cells Become Steatotic
Control Medium Fatty Acid and Hormone Supplementation
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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
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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
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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
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• 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