tools and approaches for mass cytometry · the cytometrist’s toolbox radar flame pca heatmap...

Erin Simonds, PhD W. A. Weiss Lab, UCSF

May 23, 2013

Tools and approaches for mass cytometry

data analysis

© 2011 J. Simonds

Biaxial plots are not a scalable solution

Parameters: 4 8 14 32

Plots: 6 28 91 496 2010 2000 1989 1983

How can we explore in 30+ dimensions?

The cytometrist’s toolbox

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

Transform raw data

3D plot

Reduce dimensionality

Summarize statistics

Identify clusters

Wanderlust

Box plot

Vetting the CyTOF: In a fair fight, nearly identical data

Experimental design 7 Surface Markers (CD 3, 4, 8, 20, 33, 45RA, 56) 2 Functional Markers (pSTAT3 & 5)

Bendall, Simonds, et al. Science, May 2011

The cytometrist’s toolbox

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

Transform raw data

3D plot

Reduce dimensionality

Summarize statistics

Identify clusters

Wanderlust

Box plot

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

Qiu et al., Nature Biotechnology, Oct. 2011

Marker 1

Mar

ker 1

Marker 2

Marker 2

SPADE

CD45 CD3 CD4 CD8 CD34 CD19 CD20 CD33 CD11b CD123

SPADE projects bone marrow as a continuum of phenotypes

0 100 Intensity (%max)

Bendall, Simonds, et al. Science, May 2011

pSTAT5

0 100 Intensity (%max)

Rediscovery of canonical signaling pathways

Basal IL-7

Bendall, Simonds, et al. Science, May 2011

+ Dasatinib Abl / Src-family kinase inhibitor

Dasatinib differentially affects immune cell subsets

Bendall, Simonds, et al. Science, May 2011

IL7 (pSTAT5)

PVO4 (pSTAT5)

The cytometrist’s toolbox

Transform raw data

Reduce dimensionality

Summarize statistics

Identify clusters

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

3D plot

Wanderlust

Box plot

viSNE preserves high-dimensional, non-linear relationships

Raw data (in 3D) viSNE map (in 2D)

z

y x

viSNE-1

viS

NE

-2

Amir  et  al.,  Nature  Biotechnol.  ePub  19  May  2013  

viSNE maps healthy marrow as discrete populations

Amir  et  al.,  Nature  Biotechnol.  ePub  19  May  2013  

CD4+ T

CD11b-hi monocyte

CD11b-lo monocyte

NK

CD20+ B

CD8+ T

CD20- B

Amir  et  al.,  Nature  Biotechnol.  ePub  19  May  2013  

viSNE can help detect rare subpopulations

ALL cells

Barcode A: Healthy bone marrow cells Barcode B: ALL cells spiked into sample at 0.25% frequency (1/ 400 cells)

Amir  et  al.,  Nature  Biotechnol.  ePub  19  May  2013  

Same data, different dimensionality reduction algorithms

viSNE is still stochastic, but much more reproducible

Amir  et  al.,  Nature  Biotechnol.  ePub  19  May  2013  

SPADE’s stochasticity causes difference between runs

So why should I ever use SPADE? à Clustering allows comparison between samples

The cytometrist’s toolbox

Transform raw data

Reduce dimensionality

Summarize statistics

Identify clusters

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

3D plot

Wanderlust

Box plot

pSTA

T5

pSTAT3

Goal: Characterize the GCSF-responsive subpopulation

G-CSF

pSTAT3 pS

TAT5

Who are these cells?

Are they the same in every

patient?

Surface marker profiling of G-CSF responders in AML

with Faye Hsu, Yury Goltsev

pSTAT3

pSTA

T5

CD14  CD32  CD36  CD86  CD93  CD11a  CD1d  HLA-­‐DR  

CD46  CD69  CD117  CD133  CD155  CD321  

CD13  CD15  CD33  CD34  CD38  CD44  CD45  

Non- responders

+ markers from literature

CD47  CD64  CD114  CD123  CD11b  CXCR4  TIM3  

Responders

Differential gene expression

Screen pediatric AML samples (18 diagnosis, 3 relapse, 7 healthy)

LSCs

Gate G-CSF responders

Identify enriched markers

pSTAT3

pSTA

T5

Basal G-CSF

pSTAT3

pSTA

T5

CD46

Leukemic GCSF responders have distinct surface profiles

with Rob Bruggner & Sean Bendall

Growth factor

receptors

Universal markers

CD34

LSC markers

pSTAT3

pSTA

T5

Healthy

The cytometrist’s toolbox

Transform raw data

Reduce dimensionality

Summarize statistics

Identify clusters

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

3D plot

Wanderlust

Box plot

Human B cell development is less understood than in the mouse

CD34

CD10

CD24 IL7R

CD38

CD179a

Surface Markers

Intracellular Markers

Rag TdT

IgH Genes

CD19

Adapted – Cobaleda, BioEssays 2009

DJH

VDJH

Maturity Trajectory

The predicted trajectory ‘rediscovers’ human B cell development

CD34

TDT

“Wanderlust” algorithm: El-ad Amir, Dana Pe’er (Columbia)

CD24

CD34

CD

38

Following the predicted B cell trajectory

1

•  Fluorescent panel design à Sorting à qPCR

•  VDJ rearrangement confirms the trajectory: (unrearranged) 1 à 2 à 3 à 4 à 5 (rearranged)

Sean Bendall, Kara Davis

CD24

TdT

4

2 5

3

New insights and resolution in human B cell development

CD34  

CD10  

CD24  IL7R    

CD38  

CD179a  

Surface    Markers  

Cytoplasmic    Markers  

Rag  TdT  

IgH  Genes  

CD19  

DJH  VDJH  

I   IIa   IIb   III   IV   Va   Vb  

✓  

✓  

       CD179b  Ki67  

pSTAT5  

Sean Bendall, Kara Davis

Summary

Radar

FLAME

PCA

Heatmap

Biaxial plot Histogram

FLOCK SamSPECTRAL FlowMerge FlowClust

Gemstone SPADE viSNE

Transform raw data

3D plot

Reduce dimensionality

Summarize statistics

Identify clusters

Wanderlust

Box plot

Sean Bendall Kara Davis Harris Fienberg Astraea Jager Rob Bruggner Rachel Finck

stanford.edu/group/nolan

Mount Sinai Michael Linderman

Columbia El-Ad Amir Jacob Levine Dana Pe’er

St. Jude Children’s Amanda Gedman Ina Radtke James Downing

Stanford Peng Qiu (à MD Anderson) Sylvia Plevritis

Prof. Garry P. Nolan Bernd Bodenmiller (à U. Zurich) Eli Zunder Greg Behbehani Wendy Fantl and the entire Nolan lab