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www.sanger.ac.uk
Sarah Teichmann
Genomes and BioData
Mike Stratton
CEO, Wellcome Genome Campus
Director, Wellcome Trust Sanger Institute
Understanding Cellular Heterogeneity
Sarah Teichmann
Wellcome Trust Sanger Institute
Cells: Robert Hooke, 1665
Cell Theory
(Schwann, Schleiden & Virchow, 1838)
1)
Living organisms are composed of 1+ cells.
2)
The cell is the most basic unit of life.
3)
All cells arise from other cells.
The order of things
Reductionist Genomics: Single Cells
Reductionist Genomics: Single Cells
Structural & Functional Units
Single cell RNA-seq: high-res
quantitative descriptor of cells
Single cell transcriptome EQUALS
Single cell image
Transcriptome = all RNA in cell
Transcriptome = all RNA in cell
Human Cell Atlas
 Chart all human cells
 International collaborative effort
 Major hub = Wellcome Genome Campus
111 Epigenomes
NIH Roadmap Epigenomics Consortium,
(Kundaje et al. , Nature 2015)
111 Epigenomes
NIH Roadmap Epigenomics Consortium,
(Kundaje et al. , Nature 2015)
Development
111 Epigenomes
NIH Roadmap Epigenomics Consortium,
(Kundaje et al. , Nature 2015)
iPSC/ESC-derived
111 Epigenomes
NIH Roadmap Epigenomics Consortium,
(Kundaje et al. , Nature 2015)
Cancer & Disease
Interpreting personal genomes
& understanding disease
Human Cell Atlas
www.humancellatlas.org
Understanding Cellular Heterogeneity
Sarah Teichmann
Wellcome Trust Sanger Institute
Distributions across ca. 500 Th2 cells
Hebenstreit, D., et al. . (2011). Mol. Sys. Biol., 7:497.
Heterogeneity - Sequencing
Heterogeneity - Sequencing
bulk RNAseq
•
•
no insight into population diversity
average gene expression in the population
Heterogeneity - Sequencing
bulk RNAseq
•
•
no insight into population diversity
average gene expression in the population
single cell RNAseq
•
•
•
•
how many types
frequency of each cell type
unbiased approach (unlike FISH or FACS)
gene expression in each cell
Extracting order from heterogeneity
Extracting order from heterogeneity
Chip microfluidic single cell capture
scRNA-seq bioinformatics & biology
Bioinformatics:
Sensitivity and specificity of protocols?
Biology:
Resolving cellular decision-making?
scRNA-seq bioinformatics & biology
Bioinformatics:
Sensitivity and specificity of protocols?
Biology:
Resolving cellular decision-making?
Accounting for technical noise with
spike-ins
10
squared coefficient of variation (CV^2)
cell-to-cell variation
100
1
0.1
0.01
0.1
1
10
100
1000
104
105
average normalized read count
gene expression
Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V,
Teichmann SA, Marioni JC, Heisler MG. Nature Methods, 2013
Lower detection limit: 1-1000 mRNA molecules
Accuracy is HIGH!!
Sequencing depth: benefit up to 1 million reads
scRNA-seq bioinformatics & biology
Bioinformatics:
What is the sensitivity and specificity of
diverse protocols?
- Range of sensitivities >1 order magnitude
- Sequencing benefit up to 1 million reads
- Good specificities overall
Acknowledgements
Group members:
Collaborators:
Ana Cvejic, WT Sanger Institute
Valentine Svensson
Ian Macaulay, WT Sanger Institute
scRNA-seq bioinformatics & biology
Bioinformatics:
Sensitivity and specificity of protocols?
Biology:
Resolving T cell fate bifurcation?
Time series modeling
of single cell RNA-sequencing data
resolves bifurcation of T helper cell fates
T helper cell differentiation and function
Viruses &
intracellular
bacteria
Th1
Macrophages, CTLs
Parasites
& venom
Th2
Naïve
CD4+
T cell
Th17
Tfh
(follicular T helper)
Eosinophils
Neutrophils
B cells
Extracellular
bacteria
& fungi
Immunoglobulin
class-switch
& somatic
hypermutation
PcAS
1.59
Th1
Flow cytometry
18
Th1
3
13.6
E x p r e s s io n In d e x ( x 1 0 )
3
Plasmodium chabaudi elicits Th1 and Tfh
a
3
21.3
Naïve
CD4+
T cell
15
T fh
12
9
6
3
0
0
1
2
3
4
5
6
7
Tbet
D a y s p o s t - in f e c t i o n
Bcl6
Tfh
(follicular T helper)
① How do Th1 and Tfh subsets arise in vivo?
② What signals and factors regulate transitions
through intermediate states?
Th1
?
Naïve
?
?
?
Tfh
Single-cell RNA-seq of antigen-specific CD4+ T
cell response
Gaussian Process Latent Variable Model
Valentine Svensson
15 000 genes
Pseudotime
2 latent variables
1 latent variable
=Pseudotime
Overlapping Mixtures of Gaussian Processes
• Unbiased identification of two parallel trends
Overlapping Mixtures of Gaussian Processes
End points match established Th1/Tfh signatures
Fate bifurcation coincides with proliferative peak
Number of expressed genes
Ki67
(proliferation marker)
Fate bifurcation coincides with proliferative peak
Cell cycle phase allocation
70
M
G2
% of cells
in G2, M or S 60
Mitosis
50
Cytokinesis
40
G1
Flow cytometry (n=6)
scRNA-seq (Cyclone*)
30
20
10
S
DNA replication
0
Day 0
Day 4
Day 7
Scialdone A. et al.
Methods. 2015 Sep 1;85:54-61.
1. How do Th1 and Tfh subsets arise in vivo?
Time
408 single-cells resolved by GPLVM/OMGP
Th1
Naïve
Fate
bifurcation
day 4
Th1/Tfh precursor
Highly proliferative
Tfh
2. What signals and factors regulate transitions
through intermediate states?
Th1
Naïve
Fate
bifurcation
day 4
Th1/Tfh precursor
Highly proliferative
?
?
Tfh
Genes associated with Th1 or Tfh trajectories
Th1
Th1
Tfh
Tfh
Tfh
Tfh
Th1
Th1
Genes associated with Th1 or Tfh trajectories
Th1
Th1
Tfh
Tfh
Tfh
Tfh
Th1
Th1
Reciprocal expression of Tcf7 and Id2
underlies Th1 - Tfh bifurcation
Id2
Bifurcation point
Tfh
Th1
Bifurcation point
Tcf7
Th1
Tfh
A single cell gives rise to both Th1 and Tfh progeny
TCR reconstruction by TRACER
Two clonal sibling cells
with identical secondary TCRs
Stubbington M et al.
Nature Methods 2016
1
−1
0
Tfh
−2
github.com/
Teichlab/tracer
Latent variable 2
2
Th1
−2
−1
0
Latent variable 1
1
2
Cellular decision-making by single-cell
RNA-sequencing
Th1
Inflammatory
monocytes
Tfh
B cells
OMGP
Cellular trajectories
Key regulatory factors
Acknowledgements
Tapio Lonnberg
Valentine Svensson
Ashraful Haque
Kylie R. James
Michael Stubbington
Oliver Billker
Ruddy Montandon
Oliver Stegle, EMBL-EBI
William R. Heath
Daniel Fernandez-Ruiz
JOIN: www.teichlab.org