Dissecting the Cellular and Genetic Origins of Childhood Leukemias with Single-Cell...

Dissecting the Cellular and Genetic Origins of Childhood Leukemias with

Single-Cell Genomics

Charles Gawad, MD, PhD Assistant Member

St. Jude Children’s Research Hospital

Talk Overview

• Why study genomics at cellular resolution?

• Dissecting cellular states present in complex tissues with single-cell RNA-sequencing

• Measuring clonal diversity using single-cell DNA-sequencing

• Emerging single-cell sequencing technologies

• Conclusions and future directions

Why Single-Cell Sequencing?

Anatomy Identified Gross Tissue Features

Microscope: A Technology that Enabled the Study of Cell Biology

Views of the Cerebral Cortex at Cellular Resolution

“Bulk” Tissue Sequencing

Outputs From Bulk Sequencing

“Microscopes” Bringing the Study of Genomics to the Cellular Level

Microfluidic Platforms

Genome/ Transcriptome Amplification

New Ways of Visualizing Cellular Contributions to Development and Disease within Complex Tissues

Steps For Acquiring High Quality Single-Cell Genomics Data

• Tissue dissociation • Cell isolation • Whole genome/transciptome amplification • Interrogation of amplification product based on

question of interest • Making variant calls/quantification of transcripts

despite amplification biases • Inferring relationships between cells despite missing

data • More to come on this from our review last year in

Nature Reviews Genetics (PMID 26806412)

New Types of Studies Enabled by Single-Cell RNA-seq

• Unbiased classification of cell types within a tissue

• Discovery of new cell types or states

• Unbiased identification of genes or expression modules that are important for specific cells

• Identification of the cellular origins of diseases like cancer

Atlas of Developing Cerebellum As a Scaffold for Identifying the Cells of Origin of Brain Tumors

Atlas of Developing Cerebellum As a Scaffold for Identifying the

Cells of Origin of Brain Tumors

Unbiased Detection of Signaling Difference by Subsets of Anti-tumor Macrophages

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tSNE1

tSN

E2

Cells colored by sample ID

Il1b

Samhd1

Bst2

Irf5

Ccr1

Csf1r

Ifitm2

Ccr2

Psmb8

Flrt2

Flt3

Kit

Il6

Ip6k2

Adar

Oas2

Jak2

Socs1

Socs6

Rnasel

Tyk2

Oas3

Stat2

Ifit2

Mx1

Ifit1

Isg20

Ifit3

Rsad2

Irf1

Irf9

Xaf1

Gbp2

Sp100

Stat1

Hfe

Ifnar1

Stat3

Hck

Myd88

Ifi35

Irf7

Isg15

Ccl2

Irf8

Tnfrsf1a

Ifnar2

Ifitm1

Irf2

1 3 4 6 2 5 7

Column ID

Ge

ne N

am

e

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Arg

1M

gl2

Mrc

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Cluster ID

Lo

g2

exp

Marker gene expression distribution by cluster

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tSNE1

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Cells colored by cluster ID

Identifying New Factors Regulating Fetal and Adult HSC Differentiation

Fetal Liver

Adult Bone

Marrow

preB Cells

HSC

Myeloid-Erythroid Cells HSC

Myeloid Cells

Erythroid Cells

Tumors Also Have Genome Heterogeneity

(Gerlinger et al NEJM 2012)

Bringing Genome Sequencing to Cellular Resolution

Reconstruction of Clonal

Architecture using SNVs,

Deletions, and IgH

Sequence

(Gawad et al PNAS 2014)

(Gawad et al PNAS 2014)

Overview of Experimental Approach

Reconstruction of Clonal

Architecture using SNVs,

Deletions, and IgH

Sequence

(Gawad et al PNAS 2014)

Exome Sequencing 3 Cells from Each

Clone and 3 Normal Cells

Deeper Views of Clonal Structures with

Exome Sequencing

Molecular Barcode Concensus Mutation Call

Clones with Preexisting Treatment Resistance Mutations

Selecting for Drug-Resistant ALL Clones

Whole Genome Amplification The Major Limitation for High Throughput

Single-Cell DNA Sequencing

(De Bourcy et al Plos One 2014)

Current limitations of the field of single-cell DNA sequencing

• Poor whole genome amplification uniformity and/or coverage

• Lack of amplification reproducibility between cells

– Both of these result in high costs

• Calling variants despite noisy/missing data

• Inability to do massively parallel experiments

PTA

Terminator

PTA

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Bulk DNA

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Mean Coverage in 100Kb Windows Across Chromosome 1

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Qiagen MDA

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Mean Coverage in 100Kb Windows Across Chromosome 1

PTA

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BULK G

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LIANTI

MALB

ACPTA

QIA

GEN

RUBIC

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rcen

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Predicted Fraction of Genome Covered

High Throughput Single-Cell DNA Sequencing

(Eastburn et al Bioarchives 2017)

New Opportunities Enabled by High Throughput Single-Cell DNA Seuqencing

• Deep genomic characterization of tumor evolution – Identify prognostic clones that can guide therapy

• Monitoring of response of malignancy to treatment – CTCs or secondary samples of malignancy during

treatment to see how the malignancy is evolving under treatment • New strategy for MRD monitoring?

• Connecting DNA variants and RNA expression in the same cell to study the biology of residual disease

Summary and Conclusions • Single-cell genomics is poised to transform our

understanding of biology and disease – I predict that most sequencing may be single-cell

sequencing in the near future

• Single-cell RNA sequencing provides unbiased views of the cellular compositions of tissues

• Single-cell DNA sequencing provides higher resolution views of the clonal structures of malignancies

• Once we have established protocols for high-throughput single-cell DNA sequencing, there are numerous basic science and clinical applications

Acknowledgements

Veronica Gonzalez

John Easton

Rob Carter

Paul Northcott

Laure Bihannic

Celeste Rosencrance

Siva Natarajan

Doug Green

Stephen Quake

Winston Koh