Dissecting the Cellular and Genetic Origins of Childhood Leukemias with Single-Cell...
Transcript of Dissecting the Cellular and Genetic Origins of Childhood Leukemias with Single-Cell...
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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
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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
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Why Single-Cell Sequencing?
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Anatomy Identified Gross Tissue Features
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Microscope: A Technology that Enabled the Study of Cell Biology
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Views of the Cerebral Cortex at Cellular Resolution
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“Bulk” Tissue Sequencing
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Outputs From Bulk Sequencing
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“Microscopes” Bringing the Study of Genomics to the Cellular Level
Microfluidic Platforms
Genome/ Transcriptome Amplification
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New Ways of Visualizing Cellular Contributions to Development and Disease within Complex Tissues
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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)
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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
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Atlas of Developing Cerebellum As a Scaffold for Identifying the Cells of Origin of Brain Tumors
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Atlas of Developing Cerebellum As a Scaffold for Identifying the
Cells of Origin of Brain Tumors
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Unbiased Detection of Signaling Difference by Subsets of Anti-tumor Macrophages
−40
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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
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Tnfrsf1a
Ifnar2
Ifitm1
Irf2
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Column ID
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Marker gene expression distribution by cluster
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Cells colored by cluster ID
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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
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Tumors Also Have Genome Heterogeneity
(Gerlinger et al NEJM 2012)
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Bringing Genome Sequencing to Cellular Resolution
Reconstruction of Clonal
Architecture using SNVs,
Deletions, and IgH
Sequence
(Gawad et al PNAS 2014)
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(Gawad et al PNAS 2014)
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Overview of Experimental Approach
Reconstruction of Clonal
Architecture using SNVs,
Deletions, and IgH
Sequence
(Gawad et al PNAS 2014)
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Exome Sequencing 3 Cells from Each
Clone and 3 Normal Cells
Deeper Views of Clonal Structures with
Exome Sequencing
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Molecular Barcode Concensus Mutation Call
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Clones with Preexisting Treatment Resistance Mutations
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Selecting for Drug-Resistant ALL Clones
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Whole Genome Amplification The Major Limitation for High Throughput
Single-Cell DNA Sequencing
(De Bourcy et al Plos One 2014)
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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
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PTA
Terminator
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PTA
Terminator
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Bulk DNA
0
1
2
3
500 1000 1500 2000
Genome Window (100Kb)
Me
an c
ove
rage
Mean Coverage in 100Kb Windows Across Chromosome 1
0
1
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500 1000 1500 2000
Genome Window (100Kb)
Me
an c
ove
rage
Mean Coverage in 100Kb Windows Across Chromosome 1
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Qiagen MDA
0
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Genome Window (100Kb)
Me
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ove
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Mean Coverage in 100Kb Windows Across Chromosome 1
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Genome Window (100Kb)
Me
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ove
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Mean Coverage in 100Kb Windows Across Chromosome 1
0
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Genome Window (100Kb)
Me
an c
ove
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Mean Coverage in 100Kb Windows Across Chromosome 1
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PTA
0
1
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500 1000 1500 2000
Genome Window (100Kb)
Me
an c
ove
rage
Mean Coverage in 100Kb Windows Across Chromosome 1
0
1
2
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500 1000 1500 2000
Genome Window (100Kb)
Me
an c
ove
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Mean Coverage in 100Kb Windows Across Chromosome 1
0
1
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500 1000 1500 2000
Genome Window (100Kb)
Me
an c
ove
rage
Mean Coverage in 100Kb Windows Across Chromosome 1
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0.00
0.25
0.50
0.75
1.00
1.25
BULK G
E
LIANTI
MALB
ACPTA
QIA
GEN
RUBIC
ON
SIG
MA
Pre
dic
ted
Pe
rcen
t G
en
om
e C
overa
ge
Predicted Fraction of Genome Covered
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High Throughput Single-Cell DNA Sequencing
(Eastburn et al Bioarchives 2017)
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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
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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
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Acknowledgements
Veronica Gonzalez
John Easton
Rob Carter
Paul Northcott
Laure Bihannic
Celeste Rosencrance
Siva Natarajan
Doug Green
Stephen Quake
Winston Koh