Download SPD on B-cell differentiation - BMI 205

Tools for visualizing high
dimensional single cell data and
inferring cellular hierarchy
Sylvia K. Plevritis, PhD
Professor
Department of Radiology and
(by courtesy) Management Science and Engineering
Stanford University School of Medicine
Stanford CCSB
Clustering versus Ordering
• Most microarray analyses are focus on
what is the difference between A and B?
• We want to know:
B
A
Is it possible that A becomes B (or visa versa)?
If so, what are the molecular drivers of this process?
B
A
Stanford CCSB
Inferring Order
Given high throughput datasets, we
want to identify:
(1) the ordering among individual
samples
(2) which markers identify the
ordering
Stanford CCSB
Sample Progression Discovery (SPD)
Qiu et al., Discovering Biological Progression
Underlying Microarray Samples,
PLoS Computational Biology, 2011.
Stanford CCSB
SPD on B-cell differentiation
7 HSC
7 CLP
7 proB
7 preB
7 IM
5M
(Naïve B,
CB,
CC,
Memory B,
CD19+)
Microarray expression data is obtained from the Weissman Lab.
Stanford CCSB
SPD on B-cell differentiation
SPD
Stanford CCSB
SPD on B-cell differentiation
Genes in selected modules
were specific to B-cell
differentiation and included
CD19, CD20, CD79 as well
as master transcription
factors including PAX5 and
SP140. There was also
enrichment of genes in the
BCR pathway.
Stanford CCSB
SPD infers Individual Tumor Plasticity
in TCGA Breast Cancer Data
Color coded
by PAM50
Score
Enrichment of
Mammary Gland
Development,
Mesenchymal Cells
Stanford CCSB
SPD Graph Color-Coded by
Genes Associated with EMT
Stanford CCSB
Visualizing and Ordering High
Dimensional Single Cell Data
Stanford CCSB
Single cell mass cytometry
Garry Nolan, PhD
• CyTOF = Cytometer
+ Elemental Mass Spectrometer
• CyTOF Data
• Sample: normal human bone marrow
• 31 Proteins measured on single cells
13 core surface markers
18 function markers
Stanford CCSB
SPADE: Spanning-tree Progression Analysis of
Density-normalized Events
Qiu et al., Nature Biotechnology, 2011.
Anchang et al, Nature Protocols, (accepted).
Stanford CCSB
Viewing all the surface markers …
Stanford CCSB
Manually identifying the populations …
gated.combined
Median of La.138.906..Dual
CD45RA
(Used for tree−building)
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Stanford CCSB
Pooling samples …
Stanford CCSB
Analyzing a non-branching process …
Stanford CCSB
SPADE
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(Used for tree−building)
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t-SNE 1
viSNE : Amir et al., Nature Biotechnology 2013.
ACCENSE: Shekhar et al., PNAS 2014.
Stanford CCSB
ACCENSE
t-SNE 2
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(b) ALL
viSNE
gated.combined
Median of La.138.906..Dual
(Used for tree−building)
t-SNE 2
(a) Normal
Bone Marrow
Comparison to other visualization algorithms …
t-SNE 1
3
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Integration of SPADE and t-SNE to create “SPADE FOREST”…
Stanford CCSB
Multi-target drug combinations
derived
from single drug effects
measured at the level of single cells
Stanford CCSB
Overview
Intratumor heterogeneity is modeled at the level of the single cell
Single drug response at the level of single cell is measured by
changes in protein expression using CyToF
Analysis of drug response are performed on clusters of similar cells
Drug combinations are derived from single drug response each
cell cluster individually then combined through a mixture model
Stanford CCSB
Intratumoral Heterogeneity
Stanford CCSB
Outline
• Collect perturbation single data drug response
data using CyTOF
• Identify homogeneous cell types based on
surface markers
• Build a “nested effects” graphical drug model in
each cell type based on intracellular change;
estimate a “mixture nested effects”
• Determine a scoring function of optimizing drug
combinations
Stanford CCSB
Outline
• Collect single data perturbation data using
CyTOF
• Identify homogeneous cell types based on
surface markers
• Build a “nested effects” graphical drug model
in each cell type based on intracellular
change; estimate a “mixture nested effects”
• Determine a scoring function of optimizing drug
combinations
Stanford CCSB
Perturbation CyTOF Profiling of
Functional & Surface Phenotypes
in Healthy Bone Marrow and Pediatric AML
Staining panel (31 Abs)
Perturbations (19)
Healthy donor
marrow (7)
Diagnosis AML
marrow (18)
Matched relapse
AML marrow (3)
No inhibitor
Basal (unstim.)
AICAR
Flt3 ligand
G-CSF
GM-CSF
IFNα
IFNϒ
IL-3
IL-6
IL-10
IL-27
PMA + ionomycin
PVO4
SCF
TNFα
TPO
PI3K/mTOR inhib.
Inhibitor alone
PMA/ionomycin
PVO4
15 Functional Markers
p4EBP1
pAkt
pAMPK
pCbl
pCREB
pERK1/2
p-p38 MAPK
cleaved Casp3
pPLCgamma2
pRb
pS6
pSTAT1
pSTAT3
pSTAT5
pSyk
16 Surface Markers
CD3
CD7
CD11b
CD15
CD19
CD33
CD34
CD38
CD41
CD44
CD45
CD47
CD64
CD117
CD123
HLADR
Courtesy of Nolan Lab.
Stanford CCSB
Outline
• Collect single data perturbation data using
CyTOF
• Identify homogeneous cell types based on
surface markers
• Build a “nested effects” graphical drug model
in each cell type based on intracellular
change; estimate a “mixture nested effects”
• Determine a scoring function of optimizing drug
combinations
Stanford CCSB
CCAST: Classification, Clustering and Sorting Tree
Anchang et al., PLoS Computational Biology, 2014.
Stanford CCSB
CCAST identifies more homogeneous B-cell subpopulations
Anchang et al., PLoS Computational Biology, 2014.
Stanford CCSB
Outline
• Collect single data drug response data using
CyTOF
• Identify homogeneous cell types based on
surface markers
• Build a “nested effects” graphical drug model
in each cell type based on intracellular
change; estimate a “mixture nested effects”
• Determine a scoring function of optimizing drug
combinations
Stanford CCSB
Graphical Models of Nested Drug Effects
An effect represents a change
in protein marker following
drug intervention
Different drug effect subsets
can be observed
In a graphical model of the
drugs D1 and D2, an edge
from D1 to D2 indicaes that
the effects of D2 are nested
in the effects of D1
Stanford CCSB
Objective Function of DRUG NEM
Given a drug response single
cell data from n drugs and m
targets, identify an optimal
drug regimen as minimum
number of drugs that
maximum number of markers
effected.
Stanford CCSB
Multi-drug experiment on HeLa cells
under TRAlL stimulation
Study design
Stimulation: TRAIL (Base line treatment)
Inhibitors: JNK 1, GDC, GSK, SB
Cell states : Apoptotic-like and survivor-like
Intracellular markers:
Stanford CCSB
Stanford CCSB
L
GDC
JNK I
SB
1
GSK
0
.8
0
.5
0
.2
0
80.00%
60.00%
40.00%
20.00%
0.00%
GSK SB
GSK GDC
Drug regimens
SB
TRAIL
G
D
C
JN
K
I
G
SK Ku
G
SK +K
G +G u
SK D
+ C
J
G NK
SK I
Ku +S
+ B
G
SB SB+ DC
+
G
G
SK Ku DC
+
+
S B GD
+ C
G
D
C
G
D
C
SB
J
N
K
I
ul
)
G
SK
pHistoneH3
Ki67
pS6
pErk
pNFkB
pAkt
cCaspace7
cCaspase3
pP90RSK
cPARP
G
S
K
180.00%
160.00%
140.00%
120.00%
cPARP
pNFkB
pP38
cCaspase3
cCaspace7
pHistoneH3
pP90RSK
0
pAkt
IKBalpha
pErk
pAMPK
Bid
Ki67
pS6
pRb
S6
0
.2
(2
D
DRUGMNEM prediction
DRUGMNEM Network
SB
Ovarian (HeLa) cancer cells
S
B
GSK SB
MNEMs indicate pP38 MAPK (SB) and Mek
(GSK) inhibitors are important candidates for
combination therapy
D
Dr u
0
.5
% Survival
pP38
S6
Bid
pRb
IKBalpha
SB
TR
AI
pHistoneH3 S
pP90RSK S
IKBalpha S
pAkt S
cCaspace7 S
cCaspase3 S
Ki67 A
pS6 A
pNFkB A
pAkt A
pP38 S
cCaspace7 A
pNFkB S
pErk A
pS6 S
Ki67 S
Bid S
pP38 A
cPARP A
pP90RSK A
cCaspase3 A
C
pRb S
pErk S
pAMPK S
pAMPK A
IKBalpha A
pRb A
Bid A
S6 A
S6 S
Drugs
pAMPK
GSK
S
G
GDC SB
JNK I SB
GSK GDC SB
GSK JNK I SB
GDC JNK I SB
100.00%
Clonogenic prediction
Drug regimens
GSK GDC SB
Summary
• SPD infers hierarchical ordering of tumors based on
gene experssion
• SPADE infers hierarchical ordering among single cells
based on CyTOF data
• DRUGMNEM generates drug combination hypotheses
using single drug information based on measurements of
intratumor heterogeneity
Stanford CCSB
Acknowledgements
•
•
•
•
•
•
•
Benedict Anchang
Peng Qiu
Kara Davis
Harris Feinberg
Sean Bendall
Robert Tibshirani
Garry Nolan
NCI Integrative Cancer Biology Program (ICBP)
Stanford CCSB