Cells that fell into wells with stories to tell

Christopher E. MasonAssociate 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

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Background

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?

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.

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.

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

Epigenetic Drift in Twins

Mario F. Fraga et al. PNAS 2005;102:10604-10609

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

Prediction and Precision

Predictive Medicine

DiseasePrecision Medicine

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

ETA: 2035

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Single Cell Revolution

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

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.

Sc - options

Many options for single cells

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359768/pdf/jbt.17-2801-006-jbt.17-2801-006.pdf

Composite measurements and molecular compressed sensing for highly efficient transcriptomics

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

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

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

data = opportunities1. Quantify heterogeneity

2. Visualize relationships between single cell transcriptomes3. Identify

signatures of response

4. Explore variable isoform expression

5. variant calling from sc-RNAseq

How can we study the vast transcriptome?

exon1 exon2 exon3

exon1-exon2exon1-exon3exon2-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- exon4exon2-exon4exon3-exon4

exon1-exon2-exon4exon1-exon3-exon4exon2-exon3-exon4

exon1-exon2-exon3-exon4

8x1083 theoretical transcript combinations8x1080 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

What are the differences between cells?Exon 1 Exon 2 Exon 3

Exon 1 Exon 2 Exon 3

Exon4

Exon4

Need a tool to characterize broken up reads…

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

DISCODistributions of Isoforms in Single Cell

Omics

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

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 cell3. 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>

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

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MDS

Myelodysplastic Syndromes (MDS)

• class of bone marrow failure disorders

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

Myelodysplastic Syndromes (MDS)

• class of bone marrow failure disorders• accumulation of abnormal hematopoietic

stem cells (HSCs) --> ineffective hematopoiesis

HSCs

Progenitors

Normal MDS heterogeneity

response totherapy

disease progression

?

? ?

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

Experimental DesignDecitabine 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 C1RNA-seq! (2 x 100)

max 96 cells per run

Lin- CD34+ CD38- CD90+ CD45RA-

Samples

Patients Pre Post Response1 79 - UT2 63 85 UT3 19 71 NR4 56 31 R5 68 - R6 33 27 NR7 - 17 R9 61 - R

norm1 55 - -norm2 82 - -UT = Untreated

NR = Non-responderR = Responder

Pre = pre-decitabine in R and NR, serial time point 1 in UTPost = post-decitabine in R and NR, serial time point 2 in UT

number of cells

differentially expressed genes between MDS (pre-treatment) and normal

Z-score of log2(FPKM+1) of DEGs at FDR 0.01

Pathways enriched in DEGs

Lineage markers

Lineage Negative PositiveB-cell 3 9

Erythroid/Megakaryocytic 0 5

HSC 15 90Lymphoid 4 10Myeloid 0 7

T-Cell 7 10

number of genes

MEIS1CXCR4HLFMECOMNR4A2

MPOCEBPA

Genes that overlap with MDS v Norm DEGs

Clouds of patient groups

Assign cells to stem cell or myeloid states

Semi-supervised pseudotime ordering based on 80 marker genes

monocle R package

Normal HSCs exclusively occupy the lower end of the pseudotime lineage tree

***

Differences in cell state distributions between disease groups

Effect of treatment on cell states

Effect of treatment on cell states

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

Enriched pathways in genes differentially expressed with pseudotime

Pathways differentiating branch 1 and 2

branch 1

branch 2

Branch 1

Branch 2

Participatory medicine with twin astronauts

Planets are really just big cells

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

These People are Awesome @mason_lab

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

Deep Gratitude to Many People:

IlluminaGary SchrothMarc Van Oene

Univ. ChicagoYoav Gilad

FDA/SEQC/Fudan Univ.Leming Shi

NIH/UDP/NCBIJean & Danielle Thierry-Mieg

BaylorJeff Rogers

MSKCCDanwei HuangfuChristina LeslieRoss LevineAlex Kentsis

HudsonAlphaShawn LevyBraden Boone

Mason LabEbrahim AfshinnekooSofia AhsanuddinNoah AlexanderPradeep AmbroseDaniela BezdanMarjan BozinoskiDhruva ChandramohanChou ChouTim DonahoeFrancine Garrett-BakelmanJonathan FooxElizabeth Hénaff Alexa McIntyreCem MeydenNiamh O’HaraRachid OunitLenore PipesJake ReedHeba ShabaanPriyanka VijayDavid Westfall

Cornell/WCMScott BlanchardSelina Chen-KiangOlivier ElementoSamie JaffreyAri MelnickMargaret RossEpigenomics Core

DukeStacy HornerNandan Gokhale

Icahn/MSSMEric Schadt, Andrew Kasarskis,Joel Dudley, Ali Bashir, Bobby Sebra

ABRF George GrillsDon BaldwinCharlie Nicolet

MiamiMaria E Figueroa

AMNHGeorge AmatoMark Siddall

@mason_lab

NYUMartin BlaserJane CarltonJulia MaritzChris Park

MIT Media LabKevin SlavinDevora NajjarRegina Flores

RockefellerJeanne GarbarinoCharles Rice

NASAAaron BurtonSarah Castro-WallaceKate RubinsGraham ScottCraig Kundrot

Jackson LabsSheng Li

UCSFCharles Chiu

XMP/MGRGScott TigheKen McGrathRuss CarmicalScott Jackson