Single-cell Transcriptomics

and Flow Cytometry Reveal

Disease-associated Fibroblast Subsets

in Rheumatoid Arthritis

Kamil Slowikowski

Soumya Raychaudhuri Laboratory

January 27, 2016

Strategy to define fibroblast subsets

Isolated

synovial

fibroblasts

Transcriptomics of

sorted bulk populations

PRO: robust gene expression signal

CON: bias due to marker selection Fresh tissue

from joint

replacement

surgery

Transcriptomics of

unsorted single cells

PRO: unbiased, no marker selection

CON: noisy gene expression signal

Study design

• Microarray gene expression profiling

– Donors: 3 OA and 3 RA

– Gates: 5-8 sorted subsets per donor

• Low-input RNA-seq

– Donors: 4 RA

– Gates: 5-8 sorted subsets per donor

• Single-cell RNA-seq

– Donors: 2 OA and 2 RA

– Cells: 384

DISCOVERY

VALIDATION

Sorting fibroblast subsets by surface markers

Figure 1. Gating strategy for

MSCs with heterogeneous

expression of surface proteins.

(A) We obtained fresh synovial

tissue from surgery and enriched the

sample for MSCs by depleting

hematopoietic and endothelial cells

(see Methods for details). (B) We

separated CD34- and C34+ cells,

then (C) selected PDPN+ cells, then

(D) gated 7 subpopulations of cells.

(E) For each subpopulation, we

tested for a difference in the propor-

tion of total fibroblasts between OA

and RA (rank sum test) and found

two populations that are significantly

different.

A

B

C

D

E P = 0.141

P = 0.003 *

P = 0.033

P = 0.005 *

P = 0.045

P = 0.632

P = 0.241

0% 20% 40% 60% 80%OA (n = 26) RA (n = 17)

Fresh synovial tissue

from surgery

Remove hematopoietic cells,

endothelial cells, and RBCs

CD34

CD

146

PD

PN

CD

H11

THY1

CD

H11

THY1

PD

PN

THY1 THY1

Fumitaka Mizoguchi

CD34– CD34+

PD

PN

C

DH

11

THY1

Up to 8 subsets by surface protein phenotype.

How many subsets by gene expression?

Sorting fibroblast subsets by surface markers

Figure 1. Gating strategy for

MSCs with heterogeneous

expression of surface proteins.

(A) We obtained fresh synovial

tissue from surgery and enriched the

sample for MSCs by depleting

hematopoietic and endothelial cells

(see Methods for details). (B) We

separated CD34- and C34+ cells,

then (C) selected PDPN+ cells, then

(D) gated 7 subpopulations of cells.

(E) For each subpopulation, we

tested for a difference in the propor-

tion of total fibroblasts between OA

and RA (rank sum test) and found

two populations that are significantly

different.

A

B

C

D

E P = 0.141

P = 0.003 *

P = 0.033

P = 0.005 *

P = 0.045

P = 0.632

P = 0.241

0% 20% 40% 60% 80%OA (n = 26) RA (n = 17)

Fresh synovial tissue

from surgery

Remove hematopoietic cells,

endothelial cells, and RBCs

CD34

CD

146

PD

PN

CD

H11

THY1

CD

H11

THY1

PD

PN

THY1 THY1

436 genes have significant variation (ANOVA 1% FDR)

across 7 gated subsets in microarray data and also in

independent RNA-seq data.

3 major populations of synovial fibroblasts

Pairwise correlation between samples and principal components analysis (PCA) both

suggest 3 major populations.

Pearson’s r

PCA Pairwise Correlation

Gene expression suggests different functions

CD

34nT

HY

1n

CD

34nT

HY

1p

CD

34p

MITOTIC_CELL_CYCLE_CHECKPOINT

MITOTIC_CELL_CYCLE

CELL_CYCLE_PROCESS

SPINDLE

COLLAGEN

REGULATION_OF_MITOSIS

CELL_CYCLE_PHASE

M_PHASE

MITOSIS

M_PHASE_OF_MITOTIC_CELL_CYCLE

GROWTH_FACTOR_ACTIVITY

INFLAMMATORY_RESPONSE

AMINE_METABOLIC_PROCESS

PURINE_RIBONUCLEOTIDE_METABOLIC_PROCESS

NUCLEOTIDE_SUGAR_METABOLIC_PROCESS

LOCOMOTORY_BEHAVIOR

PATTERN_BINDING

RESPONSE_TO_WOUNDING

BEHAVIOR

CYTOKINE_ACTIVITY

EXTRACELLULAR_MATRIX_STRUCTURAL_CONSTITUENT

CELL_SUBSTRATE_ADHESION

CELL_MATRIX_ADHESION

CHEMOKINE_RECEPTOR_BINDING

CHEMOKINE_ACTIVITY

STRUCTURAL_CONSTITUENT_OF_RIBOSOME

SKELETAL_DEVELOPMENT

EXTRACELLULAR_MATRIX

PROTEINACEOUS_EXTRACELLULAR_MATRIX

0 2 4 6 8

MSigDB: C5 Gene Ontology

CD

34nT

HY

1n

CD

34nT

HY

1p

CD

34p

MITOTIC_CELL_CYCLE_CHECKPOINT

MITOTIC_CELL_CYCLE

CELL_CYCLE_PROCESS

SPINDLE

COLLAGEN

REGULATION_OF_MITOSIS

CELL_CYCLE_PHASE

M_PHASE

MITOSIS

M_PHASE_OF_MITOTIC_CELL_CYCLE

GROWTH_FACTOR_ACTIVITY

INFLAMMATORY_RESPONSE

AMINE_METABOLIC_PROCESS

PURINE_RIBONUCLEOTIDE_METABOLIC_PROCESS

NUCLEOTIDE_SUGAR_METABOLIC_PROCESS

LOCOMOTORY_BEHAVIOR

PATTERN_BINDING

RESPONSE_TO_WOUNDING

BEHAVIOR

CYTOKINE_ACTIVITY

EXTRACELLULAR_MATRIX_STRUCTURAL_CONSTITUENT

CELL_SUBSTRATE_ADHESION

CELL_MATRIX_ADHESION

CHEMOKINE_RECEPTOR_BINDING

CHEMOKINE_ACTIVITY

STRUCTURAL_CONSTITUENT_OF_RIBOSOME

SKELETAL_DEVELOPMENT

EXTRACELLULAR_MATRIX

PROTEINACEOUS_EXTRACELLULAR_MATRIX

0 2 4 6 8

MSigDB: C5 Gene Ontology

-Log10(P)

Gene Set Enrichment Analysis

with Gene Ontology (MSigDB C5)

Selected Genes

Proportions of fibroblast populations are altered in RA

Figure 1. Gating strategy for

MSCs with heterogeneous

expression of surface proteins.

(A) We obtained fresh synovial

tissue from surgery and enriched the

sample for MSCs by depleting

hematopoietic and endothelial cells

(see Methods for details). (B) We

separated CD34- and C34+ cells,

then (C) selected PDPN+ cells, then

(D) gated 7 subpopulations of cells.

(E) For each subpopulation, we

tested for a difference in the propor-

tion of total fibroblasts between OA

and RA (rank sum test) and found

two populations that are significantly

different.

A

B

C

D

E P = 0.141

P = 0.003 *

P = 0.033

P = 0.005 *

P = 0.045

P = 0.632

P = 0.241

0% 20% 40% 60% 80%OA (n = 26) RA (n = 17)

Fresh synovial tissue

from surgery

Remove hematopoietic cells,

endothelial cells, and RBCs

CD34

CD

146

PD

PN

CD

H11

THY1

CD

H11

THY1

PD

PN

THY1 THY1

CD34–THY– CD34–THY+ CD34+

OA (n=26) and RA (n=16) joints have

different abundances of fibroblast populations.

Different fibroblast abundances in swollen (n=7) or

non-swollen (n=5) joints.

Strategy to define fibroblast subsets

Isolated

synovial

fibroblasts

Transcriptomics of

sorted bulk populations

Transcriptomics of

unsorted single cells

PRO: robust gene expression signal

CON: bias due to marker selection

PRO: unbiased, no marker selection

CON: noisy gene expression signal

Fresh tissue

from joint

replacement

surgery

Single cell RNA-seq at Broad Technology Labs

Chad Nussbaum, PhD

Director of Broad Technology Labs

Nir Hacohen, PhD

Associate Professor of Medicine

Massachusetts General Hospital

1. Each cell labeled with protein

surface markers:

PDPN, CD34, THY1, CDH11

2. Cells sorted into 96-well plates

3. Illumina Smart-Seq2

4. NextSeq500

~1M reads per cell

Single cell data quality

Donors: 2 OA, 2 RA

Cells: 384

337 (88%)

47 (12%)

2,500

5,000

7,500

10,000

12,500

20% 40% 60%

Fragments assigned to transcripts

Ge

ne

s d

ete

cte

d

DonorOA1OA2RA1RA2

Single−cell RNA−seq

We excluded 47 cells with fewer than 5,000 genes

detected at ≥1 transcript per million (TPM).

Kharchenko, Silberstein, & Scadden Nat. Methods (2014).

We used SCDE to:

• compute robust expression estimates

• correct for batch effects

Jean Fan

Bioinformatics and Integrative

Genomics PhD Candidate

Department of Biomedical Informatics

Genes with high mean and variance across cells

We measured protein fluorescence on each cell

OA1 (n=83) OA2 (n=86) RA1 (n=87) RA2 (n=81)

1

2

3

4

1

2

3

4

CD

34−

CD

34

+

2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5

THY1

CD

H1

1

We measured CD34, THY1, and CHD11 protein markers on each cell.

OA1 (n=83) OA2 (n=86) RA1 (n=87) RA2 (n=81)

1

2

3

4

1

2

3

4

CD

34−

CD

34

+

2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5

THY1

CD

H1

1CD34–THY–

CD34–THY+

CD34+

OA1 (n=83) OA2 (n=86) RA1 (n=87) RA2 (n=81)

1

2

3

4

1

2

3

4

CD

34−

CD

34

+

2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5

THY1

CD

H1

1

THY1

Genes with high mean and variance across cells

Classification of each single cell by gene expression

1. Select ~900 genes that distinguish 3 populations in the bulk RNA-seq data.

2. Build a Linear Discriminant Analysis (LDA) model with these genes.

3. Input each single cell expression profile into the model.

4. Get the posterior probability of each cell to belong to one of the 3 populations.

Bulk RNA-seq

LDA Model

Single Cell

Unclassified

95% probability CD34-THY1+

Unbiased scRNA-seq validates discovery of 3 subsets

Fibroblast subsets in microanatomical structures

CD34-THY1-

CD34+

CD34-THY1+

Lining

Sublining

Conclusions

Figure 1. Gating strategy for

MSCs with heterogeneous

expression of surface proteins.

(A) We obtained fresh synovial

tissue from surgery and enriched the

sample for MSCs by depleting

hematopoietic and endothelial cells

(see Methods for details). (B) We

separated CD34- and C34+ cells,

then (C) selected PDPN+ cells, then

(D) gated 7 subpopulations of cells.

(E) For each subpopulation, we

tested for a difference in the propor-

tion of total fibroblasts between OA

and RA (rank sum test) and found

two populations that are significantly

different.

A

B

C

D

E P = 0.141

P = 0.003 *

P = 0.033

P = 0.005 *

P = 0.045

P = 0.632

P = 0.241

0% 20% 40% 60% 80%OA (n = 26) RA (n = 17)

Fresh synovial tissue

from surgery

Remove hematopoietic cells,

endothelial cells, and RBCs

CD34

CD

146

PD

PN

CD

H11

THY1

CD

H11

THY1

PD

PN

THY1 THY1

1. Synovial fibroblasts have distinct cellular subsets:

Protein surface marker expression by FACS

mRNA expression by microarrays and RNA-seq

Microanatomical localization by histology

2. Proportions of subsets differ between OA and RA

3. scRNA-seq on unbiased samples of fibroblasts confirms our findings:

Single cells are classified with high probability into 3 defined classes

Number of classified cells matches expectation by FACS gating

This work was a collaboration with the Brenner Lab

Michael B. Brenner, MD

Theodore Bevier Bayles Professor of Medicine

Harvard Medical School

Chief, Rheumatology, Immunology and Allergy

Brigham And Women's Hospital

Fumitaka Mizoguchi, MD, PhD

Assistant Professor, Department of Rheumatology

Tokyo Medical and Dental University

Acknowledgements

Brigham and Women’s Hospital Brenner Lab

Michael Brenner

Fumitaka Mizoguchi

Erika Noss

Sook Kyung Chang

Deepak Rao

Kevin Wei

Hung Nguyen

Patrick Brennan

Nigrovic Lab

Peter Nigrovic

Sarah Ameri

Allyn Morris

Raychaudhuri Lab

Soumya Raychaudhuri

Department of Orthopedic Surgery

John Wright

Barry Simmons

Scott Martin

Philip Blazar

Brandon Earp

Broad Institute Nir Hacohen

Chad Nussbaum

Boston Children’s Hospital Immunology

Lauren Henderson

Harvard Medical School Department of Biomedical Informatics

Jean Fan