ABRF meeting, single-cell session 2017

1. Cells that fell into wells with stories to tell
Christopher E. Mason
Associate Professor
Department of Physiology and Biophysics,
The Feil Family Brain and Mind Research Institute (BMRI) &
The Institute for Computational Biomedicine (ICB) &
the Meyer Cancer Center of Weill Cornell Medicine (WCM)
Fellow of the Information Society Project, Yale Law School
March 28th, 2017
.@mason_lab

2. (0)
Background

3. Genetic alterations can be selected for
and potentially drive a tumor’s progression.
Alizadeh et al., “Toward understanding and exploiting tumor heterogeneity.” Nature Medicine, 2015
What else?

5. Li S, Garrett-Bakelman F, et al., Distinct Evolution and dynamics of epigenetic and genetic heterogeneity in AML. Nature Medicine, 2016.
Li S, et al., “Dynamic Evolution of Clonal Epialleles Revealed by Methclone.” Genome Biology, 2014.

6. Mosaicism increases with age
Wang Y et al. Maternal mosaicism is a significant contributor to discordant sex chromosomal aneuploidies associated with
noninvasive prenatal testing. Clinical Chemistry 2014; 60(1):251-9.
%ofcellswith
XChromosomeLoss(XCL)

7. http://2014hs.igem.org/Team:TAS_Taipei/project/abstract

8. Epigenetic Drift in Twins
Mario F. Fraga et al. PNAS 2005;102:10604-10609

10. Horvath S. “DNA methylation age of human tissues and cell types.” Genome Biology. 2013;14(10):R115.

12. Prediction and Precision
Predictive
Medicine
Disease
Precision
Medicine

13. https://www.nasa.gov/content/nasas-journey-to-mars
2035

14. (1)
Single Cell
Revolution

16. Rapid and Efficient Microfluidics
•Partition 100-10,000+ cells
per channel in < 7 minutes
•Run 1 to 8 channels in
parallel
•No lower size limit on cells
•Recovers up to 65% of all
loaded cells, including:
–T cells, B cells, PBMCs and cell
lines
–FACS-isolated cells
–MACS MicroBead-enriched cells
•Low doublet rate: 0.9% per
1,000 cells

17. Assay Scheme for 5’ Barcoding and V(D)J Enrichment
• RT enzyme and poly(dT)
primer delivered to all GEMs
as part of master mix
• Barcoded template switch
oligo delivered to GEMs from
Gel Beads
• RT reaction generates
unbiased cDNA with a
sequencing adapter, a cell
barcode and a UMI on the 5’
end
• PCR with one primer for the
5’ adapter and one or more
primers for the desired
TCR/Ig constant regions.
• Fragmentation and sequencing
optimized for assembly of the
full V(D)J sequence (5’ UTR to
constant regions) from short
reads on a cell-by-cell basis.

18. Sc - options
Source Instrument
Number
of Cells
input
cells
est. cost
per run
est. cost
per cell
UMIs
Cell
Phenotype
DNA RNA ATAC 3'
full
cDNA
Size Range (mm)
Doublet
rate/ 1k
cells
10X Genomics Chromium 5,000 100,000 1,290$ 0.26$ yes no no yes unk yes no 1_60 0.8%
Becton Dickinson FACSseq / BDPrecise 96 unk 10,000$ 104.17$ yes no unk unk unk unk unk 5-100
Becton Dickinson Resolve 10,000 50,000 10,000$ 1.00$ yes yes unk unk unk unk unk 5-100
BioRad-ILMN ddSeq 10,000 unk 10,000$ 1.00$ unk no no yes unk unk unk unk
Drop-Seq DropSeq 10,000 100,000 1,000$ 0.10$ yes no no yes yes yes no 1-100
Fluidigm C1 96 5,000 1,900$ 19.79$ yes no yes yes yes no yes 5-10, 11-17, 17-24
Fluidigm scRRBS 96 5,000 1,900$ 45.00$ yes no yes yes yes no yes 5-10, 11-17, 17-24
Fluidigm C1- high throughput 800 5,000 4,000$ 5.00$ yes no yes yes yes yes no 5-10, 11-17, 17-24
Fluidigm Polaris 800 5,000 10,000$ 12.50$ no yes no yes no yes yes 5-10, 11-17, 17-24
In-Drop custom 10,000 100,000 5,000$ 0.50$ yes no no yes no yes no 5-100
Raindance RainDrop unk unk unk unk yes no unk unk unk unk unk unk
QIAGEN CellRaft (Cell Microsystems) 44,000 unk unk unk unk no unk unk unk unk unk unk
WaferGen iCell8 1,800 40,000 2,750$ 1.53$ yes limited soon yes unk unk maybe 5-100 2%

19. Many options for single cells
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359768/pdf/jbt.17-2801-006-jbt.17-2801-006.pdf

21. Composite measurements and
molecular compressed sensing for
highly efficient transcriptomics
http://biorxiv.org/content/early/2017/01/02/091926

22. http://biorxiv.org/content/early/2017/01/02/091926

23. http://biorxiv.org/content/early/2017/01/02/091926

24. data = opportunities
1. Quantify heterogeneity
2. Visualize relationships
between
single cell transcriptomes
3. Identify
signatures
of response
4. Explore
variable
isoform
expression
5. variant
calling from sc-
RNAseq

25. How can we study the vast transcriptome?
exon1
exon2
exon3
exon1-exon2
exon1-exon3
exon2-exon3
exon1-exon2-exon3
6 63 15
7 127 21
8 255 28
3 7 3
4 15 6
5 31 10
1 1 0
2 3 1
Exons Variants Junctions
2n-1
Exon 1 Exon 2 Exon 3
Exon 1 Exon 2 Exon 3
n(n-1)
2
Exon4
Exon4
exon4
exon1- exon4
exon2-exon4
exon3-exon4
exon1-exon2-exon4
exon1-exon3-exon4
exon2-exon3-exon4
exon1-exon2-exon3-exon4
8x1083 theoretical transcript combinations
8x1080 atoms in the universe
(159 atoms/star, 111 stars/galaxy, 110 galaxies)
Li and Mason, “The Pivotal
Regulatory Landscape of RNA
Modifications.” Annual Review of
Genomics and Hunan Genetics, 2014

26. What are the differences between cells?
Exon 1 Exon 2 Exon 3
Exon 1 Exon 2 Exon 3
Exon4
Exon4

27. Need a tool to characterize broken up
reads…

28. Since we already had Jitterbug for
Transposable Element Insertions
(TEIs)…

29. DISCO
Distributions of Isoforms in Single Cell Omics

30. https://pbtech-vc.med.cornell.edu/git/mason-lab/disco/tree/master

31. Pipeline
1. Align reads to reference genome (STAR, two-pass)
2. Use MISO’s probabilistic framework to assign reads to
isoforms and estimate relative abundance of each isoform
in each cell
3. Run DISCO to
– filter miso results for coverage, presence of isoform in a
minimum number of cells, etc.
– compare 2 groups of single cells (or any RNA-seq samples) using
Kolmogorov-Smirnov tests
– visualize significant shifts
disco <miso_filelist.txt> <group1> <group2>

32. DISCO: Distributions of Isoforms in Single Cell Omics
positional arguments:
SampleAnnotationFile filename of tab separated text, no header, with
columns: <path to miso summary file> <sample name>
<group name>
Group1 must match a group name in sample annotation file
Group2 must match a group name in sample annotation file
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
--outdir Output directory (default: ./disco_output/)
--pkldir Directory to store intermediate data processing files
(default: ./pkldir)
--group1color Color in plots for group 1; can be {y, m, c, r, g, b,
w, k} or html code (default: r)
--group2color Color in plots for group 2; can be {y, m, c, r, g, b,
w, k} or html code (default: b)
--group1file output file for sample group 1. If not specified, will
save to <outdir>/<group1name>_alldatadf.txt (default:
None)
--group2file output file for sample group 2. If not specified, will
save to <outdir>/<group2name>_alldatadf.txt (default:
None)
--geneannotationfile
Mapping of Ensembl gene IDs to HGNC symbol and gene
descriptions (default: None)
--transcriptannotationfile
Mapping of Ensembl transcript IDs to isoform function
(ex. protein coding, NMD, etc) (default: None)
--maxciwidth Maximum width of confidence interval of PSI estimate
(default: 1.0)
--mininfreads Minimum number of informative reads to include PSI
estimate (default: 0)
--mindefreads Minimum number of definitive reads to include PSI
estimate (default: 0)
--minavgpsi Do not run statistical tests for isoforms with average
PSI in both groups less than minavgpsi (default: 0.0)
--minnumcells Do not run statistical test for isoform if less than
minnumcells have information (default: 0)
--minmedianshift Do not run statistical test for isoform if shift in
median between the two groups is less than
minmedianshift (default: 0)
--stattest Which test to run? options: {KS, T} (default: KS)
usage: disco [-h] [-v]
[--outdir] [--pkldir]
[--group1color]
[--group2color]
[--group1file]
[--group2file]
[--geneannotationfile]
[--transcriptannotationfile]
[--maxciwidth]
[--mininfreads]
[--mindefreads]
[--minavgpsi]
[--minnumcells]
[--minmedianshift]
[--stattest]
SampleAnnotationFile Group1 Group2
Disco, run-time options

33. (2)
MDS

34. Myelodysplastic Syndromes (MDS)
• class of bone marrow failure disorders

35. Myelodysplastic Syndromes (MDS)
• class of bone marrow failure disorders
• accumulation of abnormal hematopoietic
stem cells (HSCs) --> ineffective
hematopoiesis
- Pang et al., 2013; Woll et al., 2014
- HSC = Lin-CD34+CD38-
CD90+CD45RA-
HSCs
Progenitors
Normal MDS

36. Myelodysplastic Syndromes (MDS)
• class of bone marrow failure disorders
• accumulation of abnormal hematopoietic
stem cells (HSCs) --> ineffective
hematopoiesis
HSCs
Progenitors
Normal MDS heterogeneity
response to
therapy
disease
progression
?
? ?

37. Myelodysplastic Syndromes (MDS)
• 30% patients progress to acute myeloid
leukemia (AML)
• current therapies (ex. decitabine) produce
partial or complete remissions in some
patients but disease re-emerges in 100% of
patients

38. Experimental Design
Decitabine
responders
Decitabine
non-responders
Untreated Normal
Pre-Rx / Untreated
serial 1
Post-Rx / Untreated serial 2
Purify HSCs with FACS
single cell
processing
with
Fluidigm C1
RNA-seq! (2 x 100)
max 96
cells per
run
Lin- CD34+
CD38-
CD90+ CD45RA-

39. Samples
Patients Pre Post Response
1 79 - UT
2 63 85 UT
3 19 71 NR
4 56 31 R
5 68 - R
6 33 27 NR
7 - 17 R
9 61 - R
norm1 55 - -
norm2 82 - -
UT = Untreated
NR = Non-responder
R = Responder
Pre = pre-decitabine in R and NR, serial time point 1 in UT
Post = post-decitabine in R and NR, serial time point 2 in UT
number of cells

40. differentially expressed genes between
MDS (pre-treatment) and normal
Z-score of log2(FPKM+1) of DEGs at FDR 0.01

41. Pathways enriched in DEGs

42. Lineage markers
Lineage Negative Positive
B-cell 3 9
Erythroid/Megakaryocytic 0 5
HSC 15 90
Lymphoid 4 10
Myeloid 0 7
T-Cell 7 10
number of genes
MEIS1
CXCR4
HLF
MECOM
NR4A2
MPO
CEBPA
Genes that overlap
with MDS v Norm
DEGs

43. Clouds of patient groups

44. ●
MEIS1 NR4A2
CXCR4 HLF MECOM
−100−50 0 50100 −100−50 0 50100
−100−50 0 50100
−100
−50
0
50
100
−100
−50
0
50
100
t−SNE 1
t−SNE2
0.0
2.5
5.0
7.5
10.0
12.5
log2FPKM
Selected HSC
Signature Genes
●
MPO
CEBPA
−100−50 0 50 100
−100
−50
0
50
100
−100
−50
0
50
100
t−SNE 1
0
3
6
9
12
Selected Myeloid
Signature Genes

45. 0
5
10
15
0 10 20 30
Stem Cell Score
MyeloidScore
assignment
StemCell
Both
Myeloid
Neither
Assign cells to stem cell or myeloid
states

46. Semi-supervised pseudotime ordering
based on 80 marker genes
monocle R package

47. Normal HSCs exclusively occupy
the lower end of the pseudotime
lineage tree
MDS HSCs
Normal HSCs
0
2
4
6
8
Pseudotime
***

48. Differences in cell state distributions between disease
groups

49. Effect of treatment on cell states

50. Effect of treatment on cell states

51. Normalized expression of genes
differentially expressed with
pseudotime (FDR 0.01)

52. Enriched pathways in genes
differentially expressed with
pseudotime

53. Pathways differentiating branch 1 and
2
branch 1
branch 2
Branch 1
Branch 2

54. Participatory medicine
with twin astronauts

55. Planets are really just big cells

56. Conclusions
• Single cell sequencing reveals a complex and heterogeneous
transcriptional landscape in hematopoietic stem cells
• Lineage ordering of HSCs based on stem cell and myeloid marker genes
reveals distinct cell states between:
– MDS and normal HSCs
– Decitabine responders and non-responders
– Pre- and post-treatment
• Decitabine does not eradicate all (or even most) MDS-specific cell
states, suggesting therapies that target these cells may have better
long-term success
• Relevant functional processes altered in these cells include ribosome
function, p53 signaling, Rap1 signaling, B cell receptor signaling, etc.
• We need a planetary-size capture and sequencing system

57. These People are Awesome
@mason_lab

58. Thanks to the Swabbing Teams! www.pathomap.org/people/

59. Deep Gratitude to Many People:
Illumina
Gary Schroth
Marc Van Oene
Univ. Chicago
Yoav Gilad
FDA/SEQC/Fudan Univ.
Leming Shi
NIH/UDP/NCBI
Jean & Danielle Thierry-Mieg
Baylor
Jeff Rogers
MSKCC
Danwei Huangfu
Christina Leslie
Ross Levine
Alex Kentsis
HudsonAlpha
Shawn Levy
Braden Boone
Mason Lab
Ebrahim Afshinnekoo
Sofia Ahsanuddin
Noah Alexander
Pradeep Ambrose
Daniela Bezdan
Marjan Bozinoski
Dhruva Chandramohan
Chou Chou
Tim Donahoe
Francine Garrett-Bakelman
Jonathan Foox
Elizabeth Hénaff
Alexa McIntyre
Cem Meyden
Niamh O’Hara
Rachid Ounit
Lenore Pipes
Jake Reed
Heba Shabaan
Priyanka Vijay
David Westfall
Cornell/WCM
Scott Blanchard
Selina Chen-Kiang
Olivier Elemento
Samie Jaffrey
Ari Melnick
Margaret Ross
Epigenomics Core
Duke
Stacy Horner
Nandan Gokhale
Icahn/MSSM
Eric Schadt,
Andrew Kasarskis,
Joel Dudley, Ali
Bashir,
Bobby Sebra
ABRF
George Grills
Don Baldwin
Charlie Nicolet
Miami
Maria E Figueroa
AMNH
George Amato
Mark Siddall
@mason_lab
NYU
Martin Blaser
Jane Carlton
Julia Maritz
Chris Park
MIT Media Lab
Kevin Slavin
Devora Najjar
Regina Flores
Rockefeller
Jeanne Garbarino
Charles Rice
NASA
Aaron Burton
Sarah Castro-Wallace
Kate Rubins
Graham Scott
Craig Kundrot
Jackson Labs
Sheng Li
UCSF
Charles Chiu
XMP/MGRG
Scott Tighe
Ken McGrath
Russ Carmical
Scott Jackson