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 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

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Clustering versus Ordering

• Most microarray analyses are focus on

what is the difference between A and B?

• We want to know:

Is it possible that A becomes B (or visa versa)?

If so, what are the molecular drivers of this process?

A

B

AB

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Inferring Order

Given high throughput datasets, we want to identify:

(1) the ordering among individual samples

(2) which markers identify the ordering

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Sample Progression Discovery (SPD)

Qiu et al., Discovering Biological ProgressionUnderlying Microarray Samples,PLoS Computational Biology, 2011.

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SPD on B-cell differentiation

Microarray expression data is obtained from the Weissman Lab.

7 HSC 7 proB7 CLP 7 preB 7 IM 5 M(Naïve B, CB,CC, Memory B, CD19+)

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SPD on B-cell differentiation

SPD

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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.

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SPD infers Individual Tumor Plasticity in TCGA Breast Cancer Data

Color coded by PAM50 Score

Enrichment of Mammary Gland Development, Mesenchymal Cells

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SPD Graph Color-Coded by Genes Associated with EMT 

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Visualizing and Ordering High Dimensional Single Cell Data

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Single cell mass cytometry

• CyTOF Data • Sample: normal human bone marrow• 31 Proteins measured on single cells

13 core surface markers 18 function markers

• CyTOF = Cytometer

+ Elemental Mass Spectrometer

Garry Nolan, PhD

Stanford CCSB

Qiu et al., Nature Biotechnology, 2011.

Anchang et al, Nature Protocols, (accepted).

SPADE: Spanning-tree Progression Analysis of Density-normalized Events

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Viewing all the surface markers …

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Manually identifying the populations …

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Pooling samples …

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Analyzing a non-branching process …

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(a)

Nor

mal

B

one

Mar

row

(b)

AL

L

viSNE : Amir et al., Nature Biotechnology 2013.ACCENSE: Shekhar et al., PNAS 2014.

Comparison to other visualization algorithms …

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Integration of SPADE and t-SNE to create “SPADE FOREST”…

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Multi-target drug combinations Multi-target drug combinations derivedderived

from single drug effects from single drug effects measured at the level of single cellsmeasured at the level of single cells

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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 Drug combinations are derived from single drug response each cell cluster individually then combined through a mixture model cell cluster individually then combined through a mixture model

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Intratumoral Heterogeneity

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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

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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

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Perturbation CyTOF Profiling of Functional & Surface Phenotypes

in Healthy Bone Marrow and Pediatric AML

Healthy donor marrow (7)

Diagnosis AML marrow (18)

Matched relapse AML marrow (3)

No inhibitor

Basal (unstim.)AICARFlt3 ligandG-CSFGM-CSFIFNαIFNϒIL-3IL-6IL-10IL-27PMA + ionomycinPVO4

SCFTNFαTPO

Perturbations (19)

PI3K/mTOR inhib.

Inhibitor alonePMA/ionomycinPVO4

15 Functional Markers

16 Surface Markers

Staining panel (31 Abs)

Courtesy of Nolan Lab.

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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

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CCAST: Classification, Clustering and Sorting Tree

Anchang et al., PLoS Computational Biology, 2014.

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CCAST identifies more homogeneous B-cell subpopulations

Anchang et al., PLoS Computational Biology, 2014.

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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

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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

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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.

Objective Function of DRUG NEM

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Multi-drug experiment on HeLa cells under TRAlL stimulation

Stimulation: TRAIL (Base line treatment)

Inhibitors: JNK 1, GDC, GSK, SB

Cell states : Apoptotic-like and survivor-like

Intracellular markers:

Study design

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MNEMs indicate pP38 MAPK (SB) and Mek (GSK) inhibitors are important candidates for

combination therapy

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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

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Acknowledgements

• Benedict Anchang• Peng Qiu• Kara Davis• Harris Feinberg• Sean Bendall• Robert Tibshirani• Garry Nolan

NCI Integrative Cancer Biology Program (ICBP)