Convergenceof single cell assays, bioinformatics and simulation...

Convergence ofsingle cell assays, bioinformatics and simulationto studytissue injury and regeneration

Rajanikanth VadigepalliDaniel Baugh Institute

for Functional Genomics/Computational BiologyDepartment of Pathology, Anatomy, and Cell Biology

Thomas Jefferson University, Philadelphia PA, USA

Disclosures

None

Developmental DefectsJeffersonProf. Raj VadigepalliMadhur Parihar

Tel Aviv UnivProf. Iftach Nachman

Hebrew UnivProf. Abraham Fainsod

Temple UnivProf. Eleni AnniTexas A&MProf. Rajesh Miranda

Autonomic Nervous Systemand Heart Failure

JeffersonProf. Raj VadigepalliProf. James SchwaberDr. Marina BalychevaAlison MossSean NievesShaina RobinsonSirisha Achanta

Central FloridaProf. Zixi Cheng

JeffersonProf. Raj VadigepalliProf. Jan HoekDr. Ankita SrivastavaDr. Egle JuskeviciuteDr. Jiayi HeDr. Justin MelunisDr. David SmithAalap VermaAnil NoronhaAustin ParrishBenjamin BarnhartManan DamaniUniv PittsburghProf. Ramon BatallerIfADO, GermanyProf. Jan Hengstler

Indian Inst Tech-MadrasProf. Push SubramaniamBabita Verma

Liver Injuryand Regeneration

Team of Teams

Extramural Funding:NIH R01 AA018873, R01 HL111621, OT2 OD023848 (SPARC)U01 HL133360, U01 EB023224, U01 AA021908 (UPMC), T32 AA007463F31 AA023445, F31 AA024969, F31 AA023143, Gift of Life Foundation

Tissue Repair and RegenerationJeffersonProf. Raj VadigepalliProf. Jan HoekProf. Theresa Freeman

Prof. Ross SummerProf. Edita Askamitiene

Prof. My MahoneyProf. Nancy PhilpProf. John Eisenbrey

Biology as a way to DiscoverReason, Apply,

Integrate, CreateLabel, Tabulate,

Classify, Categorize

Single Cells

Gene

s/Tr

ansc

ripts

High Throughput View of Molecular States of Single Cells

Pancreatic isletsSegerstolpe et al., Cell Metabolism 2016

Hippocampal cellsArtegiani et al., Cell Reports 2017

Retinal cellsMacosko et al., Cell 2015

in vivo

in simulo

Convergent Systems Pathology

0 0 05 0 1 0 15

in statistico

in vivo

in simulo


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in statistico

What Is

What May Be What If

Single Neuron Transcriptomic BiologyPark et al., Genome Res 2014Sustained hypertension

(Phenylephrine Stimulus)

Time (hours)

MAP

(mm

Hg)

Cryosection brainstem

Caudal

Rostral

Laser capture microdissection of single neurons

On Cap

192 single cells

81 g

enes

Sam

ples

Assays

High-throughput qRT-PCR (BioMarkTM)

Multivariate analysis

Principal Component 1 (17.49%)

Prin

cipa

l Com

pone

nt 2

(12.

55%

)

Scores Loadings

Principal Component 1

Prin

cipa

l Com

pone

nt 2

1+

1511

151

Cel

l Ind

ex

0 19

Gradient of transcriptional phenotypes of single neurons in the brainstem

Laser Capture Microdissected Neurons from the Nucleus Tractus SolitariusCategorized based on gene expression of TH and Fos

2 1

Thhigh/FoslowThhigh

FoshighThlow

FoslowThlow

FoshighTh+ Fos– Th – Fos+

Mod

ule

1M

odul

e 2 Gen

es

Park et al., Genome Res 2014

Structured Variability ofSingle Cell Gene Expression

Makadia et al., PLoS Comp Bio 2015

Network Model ofGene Regulation Dynamics

Makadia et al., PLoS Comp Bio 2015

cFos

gene

exp

ress

ion

cJun

gene

exp

ress

ion

Simulated Distribution of Gene Regulation Dynamics

Model-based inference of the duration ofSignaling pathway activity in single cells

Convergent Systems Pathology Makadia et al., PLoS Comp Bio 2015

Redefining the circuit ofthe Central Circadian ClockPark et al. Front Neurosci 2016

in vivo

in simulo


0 0 05 0 1 0 15

in statistico

Higgins and Anderson, Arch Pathol (1931) Koniaris et al., J Am Coll Surg (2003)

Liver Repair and Regeneration

https://prometheuscomic.wordpress.com

Liver Repair and Regeneration

Network modelingKuttippurathu et al., 2014; Correnti et al., 2015Cook et al., 2015a, 2015b, 2018; Verma A et al., 2016Verma B et al., 2018a, 2018b; Verma A et al., 2018

Single cell gene expressionCook et al., 2018; Achanta et al., 2019

in vivo manipulation of microRNAJuskeviciute et al., 2016

Transcriptomics and miRnomicsJuskeviciute et al., 2008; Kuttippurathu et al., 2016Dippold et al., 2012a; 2012b

Genome-wide transcription factor bindingCook et al., 2015; Kuttippurathu et al., 2015, 2016, 2017Nilakantan et al., 2015

Systems Pathology of Liver Regeneration/Function

Computational Modeling of Liver Regeneration:Multiscale Network of Cell Functional States

Cook et al., BMC Systems Biology, 2015Correnti, Cook et al., J Physiol, 2015Cook and Vadigepalli, Chapter 13 in Liver Regeneration, 2015Cook et al., BMC Systems Biology 2018Verma et al., Processes 2018; BMC Systems Biology 2018

Hepatocyte Functional States Multicellular Functional States

80 hrs

Simulating Dynamic Hepatocyte Populations

Q

P

R0 50 100 150 2000

0.2

0.4

0.6

0.8

1

Frac

tion

Rec

over

y

Time post-PHx (hrs)

Q

P

R

Q

R

P

Q

8 hrs 200 hrsHepatocyte

States

Liver Regeneration Profile

Total

ReplicatingPrimed

Q

A Q

AR

PRIL6

IL10

TNFa

TGFβ

Q

P

RECM

STAT3Pathway

(hepatocytes)

IE

GF

StimulateSECs

MetabolicDemand

Kupffer Cells

MetabolicDemand

Stellate Cells Hepatocytes

Modeling the Multiscale Control of Liver Repairintegrating molecular regulation, cell phenotypes and physiological response

MMPProduction

Cook et al. BMC Systems Biology 2018

Q

A Q

AR

PRIL6

IL10

TNFa

TGFβ

Q

P

RECM

STAT3Pathway

(hepatocytes)

IE

GF

StimulateSECs

MetabolicDemand

Kupffer Cells

MetabolicDemand



MMPProduction

ControlPoints


Shifting the dynamics ofhepatic stellate cell state transitionscontrols the overall mass recovery

0 0.05 0.1 0.15 0.2

Pro-Regenerative

0

0.05

0.1

0.15

0.2

0.25

Ant

i-Reg

ener

ativ

e

Pro-Regenerative FractionAnti-

Reg

ener

ativ

e Fr

actio

n

Time post-PHx (hrs)

0 100 200 300M

ass

Rec

over

y

0

0.2

0.4

0.6

0.8

1

1.2

Time post PHx (hours)Frac

tiona

l Mas

s R

ecov

ery


AR

PR

kQARkQPR

Cellular state distributions from single cell analysis

Prior to LCM Post LCM Cells on Cap

DAPIPhalloidin

GFAP

Staining and capture of single HSCs and hepatocytes

PC1

PC3PC2

Hepatocytes Stellate cells

Separation of cell types using PCA

Espina, Virginia, et al. Nature protocols 1.2 (2006)

Hepatic stellate cell states are characterized by correlated modules of gene expression

Hepatic stellate cell states are characterized by correlated modules of gene expression

low low

highlow

high low

high high

Four molecular states of hepatic stellate cells

Cyclic transitions

Star-type transitions

Fate commitment transitions

MixedQuiescent Anti-ProlifPro-Prolif

Pre-fibrotic

?

Q

A Q

AR

PRIL6

IL10

TNFa

TGFβ

Q

P

RECM

STAT3Pathway

(hepatocytes)

IE

GF

StimulateSECs

MetabolicDemand

Kupffer Cells

MetabolicDemand



MMPProduction

Q

A Q

S3

S1IL6

IL10

TNFa

TGFβ

Q

P

RECM

STAT3Pathway

(hepatocytes)

IE

GF

StimulateSECs

MetabolicDemand

Kupffer Cells

MetabolicDemand



MMPProduction

S2

Baseline, t=0

PHx, t=24h

Single hepatocyte phenotypesacross the molecular state landscape

Achanta et al., Gene Expression 2019

Baseline, t=0

PHx, t=24h

Quiescent

Primed

Proliferating

Single hepatocyte phenotypesacross the molecular state landscape


Baseline, t=0

PHx, t=24h

Alcoholic Liver Disease

Quiescent

Primed

Proliferating

Alcoholic liver injury shifts thesingle cell states across the landscape


Cell Phenotypes and Functional StatesEvolving LandscapesShaped by Regulatory Networks

Waddington (1957) Park et al., Genome Research 2014

ChallengeIncorporate ‘analog’ regulation of cell phenotypes

into a multiscale network model

Discrete Analog

Cell State Transitions as Critical Control Pointsof Liver Regeneration


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in vivo

in simulo


0 0 05 0 1 0 15

in statistico

Time (s)864 944 1024 1104 1184

Cyt

osol

ic C

a2+

(a.u

.)

CV

CVCV

CV

Vasopressin induced Ca2+ signals

in vivo dynamic imaging ofspatial calcium patterns at single cell resolution

Verma et al., Frontiers in Physiology, 2018

( ) ( )1

1 1 1, ,, , .log | ,n m

t t t

n m n mt t t t t ty Y X

h p y Y X p y Y X+

+ += −∑

( ) ( )1

2 1 1, ,, , .log |n m

t t t

n m nt t t t ty Y X

h p y Y X p y Y+

+ += −∑2 1X YTE h h→ = −

Transfer entropy based Causality Analysisof cell-cell interactions

Waves of calcium propagating frompericentral vein to periportal vein

Pericentral to Periportal

Periportal to Pericentral

Causal influence edges are not aligned unidirectionally from pericentral to periportal regions

Central Vein


Single cell gene expression dataindicates zonation of hormonal signaling components

0

1.4E-4 Avpr1a(vasopressin receptor)

0

4.0E-5

1 2 3 4 5 6 7 8 9

Plcb1(IP3 synthesis)

Frac

tion

of to

tal c

ellu

lar m

RN

A

Hepatocyte LayerPericentral Periportal

Halpern et al., Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature (2017)

Randomspatial patternof cell signaling

components

Cell-cell connectivity from causality analysis

Mod

el S

imul

atio

nsRandom

spatial patternof cell signaling

components

Cell-cell connectivity from causality analysis

Spatial gradientof cell signaling

Mod

el S

imul

atio

nsCell-cell connectivity from causality analysis

in vivo connectivity pattern yieldsrobustness of spatial patterns of calciumto gap junction disruption

20% of gap junctions OFF

50% of gap junctions OFF


High-resolution 3D reconstruction of liver tissue

Simulating spatial dynamics of Ca2+ signalin an exact 3D reconstruction of liver tissue

0

Avpr1a

01 2 3 4 5 6 7 8 9

Plcb1

Spatial gradients ofsignaling components across tissue

Heterogeneousgap junction connectivity

Robust spatial patterns of calcium organized in lobular microdomains

Convergence ofsingle cell gene expression, imaging, dynamic modeling

Verma et al. Frontiers in Physiology 2018

in vivo cell-cellconnectivity profile and

spatial gradients of signalingrequired for robustness of

tissue-scale calcium dynamics

in vivo

in simulo


0 0 05 0 1 0 15

in statistico

What Is

What May Be

What If

Convergenceof single cell assays, bioinformatics and simulation...

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