Report

Mapping Distinct Bone Ma

rrow Niche Populationsand Their Differentiation Paths

Graphical Abstract

Highlights

d Profiling of mouse bone marrow stromal cells using single-

cell RNA sequencing

d Resolution of stromal, fat, bone, and cartilage progenitor

subtypes

d Inference of differentiation trajectories and regulators

d Validation using fate-marking reporters, gene knockdowns,

and differentiation assays

Wolock et al., 2019, Cell Reports 28, 302–311July 9, 2019 ª 2019 The Author(s).https://doi.org/10.1016/j.celrep.2019.06.031

Authors

Samuel L. Wolock, Indira Krishnan,

Danielle E. Tenen, ..., Daniel G. Tenen,

Allon M. Klein, Robert S. Welner

Correspondencerwelner@uab.edu

In Brief

Using single-cell RNA sequencing,

Wolock et al. reconstruct the

transcriptional hierarchy of mouse bone

marrow stromal cell states and infer

differentiation paths to fat, bone, and

cartilage. These cell state relations were

validated using lineage-specific reporter

strains and targeted knockdowns of

transcription factors that mediate fate

decisions.

Cell Reports

Report

Mapping Distinct Bone Marrow NichePopulations and Their Differentiation PathsSamuel L. Wolock,1,6 Indira Krishnan,2,6 Danielle E. Tenen,3 Victoria Matkins,4 Virginia Camacho,4 Sweta Patel,4

Puneet Agarwal,4 Ravi Bhatia,4 Daniel G. Tenen,2,5 Allon M. Klein,1 and Robert S. Welner4,7,*1Department of System Biology, Harvard Medical School, Boston, MA, USA2Harvard Stem Cell Institute, Harvard Medical School, Boston, MA, USA3Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA4Division of Hematology/Oncology, University of Alabama at Birmingham, Birmingham, AL, USA5Cancer Science Institute, National University of Singapore, Singapore, Singapore6These authors contributed equally7Lead Contact*Correspondence: rwelner@uab.edu

https://doi.org/10.1016/j.celrep.2019.06.031

SUMMARY

The bone marrow microenvironment is composed ofheterogeneous cell populations of non-hematopoiet-ic cells with complex phenotypes and undefined tra-jectories of maturation. Among them, mesenchymalcells maintain the production of stromal, bone, fat,and cartilage cells. Resolving these unique cellularsubsets within the bone marrow remains chal-lenging. Here, we used single-cell RNA sequencingof non-hematopoietic bone marrow cells to definespecific subpopulations. Furthermore, by combiningcomputational prediction of the cell state hierarchywith the known expression of key transcription fac-tors, we mapped differentiation paths to the osteo-cyte, chondrocyte, and adipocyte lineages. Finally,we validated our findings using lineage-specific re-porter strains and targeted knockdowns. Our anal-ysis reveals differentiation hierarchies for maturingstromal cells, determines key transcription factorsalong these trajectories, and provides an under-standing of the complexity of the bone marrowmicroenvironment.

INTRODUCTION

The non-hematopoietic cells of the bone marrow microenviron-

ment include multipotent stromal and/or stem cells (MSCs),

which have been defined in culture by their capacity to differen-

tiate into osteocytes, adipocytes, and chondrocytes (Ashton

et al., 1980; Bab et al., 1986; Castro-Malaspina et al., 1980;

Dominici et al., 2006; Pittenger et al., 1999). However, it has

been difficult to resolve the subpopulations that make up stromal

progenitor and precursor cells, and identifying the transcription

factors that mediate their function and differentiation remains

challenging.

A number of methods have been used to functionally charac-

terize populations enriched for MSCs from the adult mouse bone

302 Cell Reports 28, 302–311, July 9, 2019 ª 2019 The Author(s).This is an open access article under the CC BY-NC-ND license (http://

marrow. For example, several reports have shown that MSC

potential resides within a population of platelet-derived growth

factor receptor a+ Sca-1+ (PDGFRa+ Sca-1+) cells (Mendelson

and Frenette, 2014; Mendez-Ferrer et al., 2010; Morikawa

et al., 2009; Pinho et al., 2013). Meanwhile, ablation studies

have shown that MSC populations expressing Nestin, Cxcl12,

stem cell factor (SCF), and Leptin receptor are essential for sup-

porting blood cell maintenance and differentiation (Dar et al.,

2005; Ding andMorrison, 2013; Ding et al., 2012; Mendez-Ferrer

et al., 2010; Omatsu et al., 2010; Zhou et al., 2014). Further

studies have shown that hematopoietic progenitors are predom-

inantly localized in very close proximity to MSCs secreting key

factors related to hematopoietic stem cell (HSC) maintenance

and adjacent to either small arterioles or sinusoidal endothelium

(Mendez-Ferrer et al., 2010; Morikawa et al., 2009; Silberstein

et al., 2016).

It remains difficult to establish relations and hierarchies among

bone marrow stromal cell populations. However, differential

expression of CD73 and CD90 provides some information about

their ontogeny (Nusspaumer et al., 2017) and indicates that con-

ventional PDGFRa+ Sca-1+ cells are not homogeneous but

rather comprise several populations that exhibit different func-

tions during endochondral ossification. In addition, evidence

for a common mesenchymal stem cell in the bone marrow

compartment was recently demonstrated using rigorous sin-

gle-cell analyses and lineage tracing strategies, in which skeletal

stem cells were identified in the post-natal bone marrow. More-

over, several of these distinct skeletal progenitors were defined

based on their ability to generate bone or cartilage when trans-

planted under the kidney capsule of immunodeficient mice

(Chan et al., 2015; Worthley et al., 2015). However, important

questions remain about the relation between subpopulations

and the transcription factors that mediate their differentiation.

To provide deeper insight into stromal cell differentiation, we

performed a single-cell RNA sequencing (scRNA-seq) survey of

the non-hematopoietic cells of the mouse bone marrow during

homeostasis. We identified gene signatures of unique subpop-

ulations and predicted and validated transcription factors that

mediate stromal cell differentiation. Our data suggest a simple

branching hierarchy of differentiation, and we demonstrate

creativecommons.org/licenses/by-nc-nd/4.0/).

(legend on next page)

Cell Reports 28, 302–311, July 9, 2019 303

how several transcription factors influence fate decisions to

specific bone marrow lineages. These findings were validated

using fate-marked reporter strains and by measuring differenti-

ation potential in culture.

RESULTS

Identification of Cellular Populations in the BoneMarrow MicroenvironmentSingle-cell RNA-sequencing (scRNA-seq) has become a

powerful tool for characterizing maturing hematopoietic cells

in the bone marrow (Laurenti and Gottgens, 2018), but an

extensive mapping of the differentiation paths of non-hemato-

poietic cells has not been performed. Consequently, we used

scRNA-seq to profile non-hematopoietic and non-epithelial

cells of normal 8- to 16-week-old C57BL/6 mice. Single-cell

suspensions were prepared with a combination of grinding

and collagenase-dispase treatment of long bones, followed

by sorting viable CD45�Ter119� (non-hematopoietic) and

CD31� (non-endothelial) cells. While endothelial cells represent

a significant population in the bone marrow (Mendelson and

Frenette, 2014), they were not the focus of our present study

to describe the differentiation hierarchy of the stroma. Sorted

cells were profiled by 30 droplet-based scRNA-seq (inDrops)

(Klein et al., 2015) (Figure 1A). Starting with 5,107 cells (median

of 1,651 molecules and 1,085 genes per cell) from duplicate

mouse samples, we observed no major differences in gene

expression or cell type abundance between the replicates

(Figures S1A and S1B). We then removed putative doublets

(Wolock et al., 2019) and contaminating hematopoietic and

endothelial cells (Figures S1C and S1D) using the remaining

2,847 cells (median of 1,394 molecules and 736 genes per

cell) for the analyses presented here. After performing spectral

clustering (7 clusters, P1–P7) and identifying genes enriched in

each cluster (Figure 1B), we used gene set enrichment analysis

to characterize the most significant gene expression signatures

from each cell state (Figure 1C). Distinct clusters expressed

genes related to cell adhesion, cytokine production, HSC sup-

port, adipogenesis, and ossification. Individual genes were also

expressed in expected patterns, with separate clusters ex-

pressing stromal (e.g., Cxcl12, Kitl) and bone-related (e.g.,

Bglap, Col1a1) genes at high levels (Figure 1D). Visualization

of the single-cell transcriptomes using SPRING (Weinreb

et al., 2018a) revealed a continuum of cell states forming two

major branches (Figure 1E), which is suggestive of a steady-

state differentiation process.

Landscape of Cellular Heterogeneity within the StromaBased on the above analyses and the expression patterns of pre-

viously characterized bone marrow stroma genes, we assigned

Figure 1. scRNA-seq Sequencing Reveals Heterogeneous Gene Expre

(A) Schematic of scRNA-seq of non-hematopoietic (CD45�/Ter119�-), non-endo(B) Heatmap of the most specific significantly enriched genes for each cell cluste

(C) Selected gene sets significantly enriched in the most highly specific genes of

(D) MA plot for genes significantly differentially expressed (permutation test, false

Selected genes of interest are highlighted in black, and all of the significant gene

(E) SPRING plot of single-cell transcriptomes. Each point is one cell, and colors

304 Cell Reports 28, 302–311, July 9, 2019

cell state labels to each cluster of our scRNA-seq dataset (Fig-

ures 1E and 2A). These include multipotent stromal cells

(MSCs), which represent the most abundant population in our

dataset, adipocyte progenitors (AdPs) and pre-adipocytes, oste-

oblast-chondrocyte progenitors, (OsPs) pre-osteoblast-chon-

drocytes (Pre-OCs), pro-osteoblasts, and pro-chondrocytes.

As our sample preparation includes treatment with collagenase

and dispase as well as cell sorting, the fat, bone, and cartilage

lineages are likely under-represented within our dataset. More-

over, collagenase dissociation has been shown to induce a

stress signature characterized by several transcription factors

(van den Brink et al., 2017), some of which are also believed to

mark adipocytes in the bone marrow (Ambrosi et al., 2017).

Therefore, we relied on additional genes (e.g., Ccl2, Ccl7,

Nr4a3, Adipoq, Icam1) that were not implicated in the stress

signature to classify the adipocyte clusters (Figures 2B and

S2A). In addition, we characterized the expression of significant

niche regulatory factors (Figure 2C), transcription factors

and other genes that map to distinct lineages (Figure 2D), previ-

ously uncharacterized gene expression specific to stromal

cells (Figure S2B), and important mediators of hematopoiesis

(Figure S2C). Of note, genes independently associated with

HSC-supportive CXCL12-abundant reticular cells were highly

expressed by a single MSC population in our data (Isern et al.,

2014; Sugiyama et al., 2006). Our data provides a landscape

for interrogating the marrow stroma.

Transcriptional Trajectories of Stromal and ProgenitorDifferentiationBefore attempting to identify transcription factors that regulate

the differentiation of stromal cell types, we first inferred the

gene expression trajectories of these cells as they differentiate.

MSCs are believed to give rise to bone, fat, and cartilage, and

this was supported by the application of Velocyto (La Manno

et al., 2018) to our data (Figure 3A). Velocyto identified MSCs

as the strongest ‘‘source’’ cell state in our dataset, with pre-

adipocytes, pro-osteoblasts, and pro-chondrocytes serving as

likely end states (Figure 3B). We then used population balance

analysis (PBA) (Weinreb et al., 2018b) to predict the average

gene expression trajectory of cells as they differentiate from

MSCs toward these end states (Figure 3C) and clustered genes

based on their expression patterns along the differentiation tra-

jectory of each lineage (Figure 3D). In addition to the expected

cell type-specific genes upregulated in the most differentiated

cells of each lineage (e.g., Sp7, Bglap, Col1a1 for osteoblasts;

Cebpb, Fosb, Junb for adipocytes), a subset of genes was

consistently downregulated as cells left the MSC compartment

(e.g., Foxc1, Cbln1, Clec2d, Snai2, Klf15). Furthermore, this

analysis revealed both established and unique transcription

factors specific to the adipogenic and osteogenic lineages

ssion in Bone Marrow Stromal Cells

thelial (CD31�) mouse bone marrow cells.

r.

each cluster.

discovery rate [FDR]-corrected p < 0.05) in each cluster versus all other cells.

s are shown in gray.

indicate graph-based cluster assignments.

Figure 2. Characterization of Stromal Subpopulations within the scRNA-seq Data

(A) Heatmap of the five most differentially expressed genes significantly enriched in each cell cluster.

(B) Violin plots of previously characterized cluster-specific genes.

(C) SPRING plots of stromal cells, colored by expression of the indicated gene. Shown are genes previously shown to play a role in the bone marrow HSC niche.

(D) Expression of key lineage specific genes and transcription factors.

(e.g., Nr4a3, Irf1, Maff for adipocytes; Creb3l3, Mef2c, Satb2 for

osteoblasts) (Figure S3A). Osteogenic genes were upregulated

in multiple waves (Figure 3E), with early activation of a subset

of genes (e.g., Sp7) followed by the induction of others (e.g.,

Creb3l3) in pro-osteoblasts. An analysis of transcriptional cas-

cades for pre-adipogenic and pro-chondrocyte differentiation

also demonstrated several distinct patterns (Figures S3B and

S3C). Finally, PBA was compared to and consistent with another

single-cell ordering tool, Monocle (Trapnell et al., 2014) (Figures

S3D–S3F). In summary, these results suggest previously un-

known gene regulators for differentiation to mesenchymal line-

ages that will require further investigation.

Validation of Stromal Subpopulations Using Fate-Marked ReportersTo validate our cell type and transcription factor predictions from

the scRNA-seq data, we used mouse reporter strains for stroma-

marked lineage cells. We generated Prrx1-Cre-tdTomato (pan-

stroma), Adipoq-Cre-tdTomato (adipocyte lineage cells), Osx-

Cre-tdTomato (early bone-forming cells), and Ocn-Cre-tdTomato

(late bone-forming cells) strains (Figures S4A and S4B). When

MSCs (CD45-Ter119�CD31�CD51+Sca1+) were sorted from

these tdTOM+mice and subcultured for the ability to differentiate

into adipocytes or osteoblasts, no alternative fate potential

was observed. For example, no AdipoQ-Cre-tdTOM+ cells were

Cell Reports 28, 302–311, July 9, 2019 305

E

Adipocyte ChondrocyteOsteoblast

0

20

40

60

80

100

Cel

lord

erin

g(%

)

C

Dmp1Col1a1

Wif1

BglapCd44

AlplSpp1

Sp7

Mmp13

Cxcl12

Kitl

Lpl

CebpbCcl2Ccl7

Foxc1

noisserpxe.

mroN

0

max

Cell ordering (%) Cell ordering (%) Cell ordering (%)0 10050 0 10050 0 10050

DAdipocyteMSC OsteoblastMSC ChondrocyteMSC

ARNA velocity

B

PBA differentiation trajectories

Start points End pointsMSC

Ad Ch

Os

Low High

Density

Figure 3. Population Balance Analysis Pre-

dicts Early Differentiation Trajectory of Adi-

pocytes, Osteoblasts, and Chondrocytes

(A) Velocyto-calculated RNA velocity vector field

overlaid on a SPRING plot.

(B) Prediction of start and end cell states using RNA

velocity-based Markov process simulation.

(C) PBA-predicted differentiation trajectories for

each stromal lineage. Colors indicate the ordering of

cells from least (black) to most (red) differentiated,

with gray cells excluded from the ordering.

(D) Heatmaps of dynamically varying genes for each

lineage, with cells ordered from least to most differ-

entiated and genes ordered by the clustering of

expressionpatterns.Geneexpressionwassmoothed

using a Gaussian kernel along the cell ordering axis.

(E) Clustering of gene expression traces for signifi-

cantly variable genes along the PBA-predicted

osteoblast differentiation ordering. Z score normal-

ized, Gaussian-smoothed expression traces were

clustered using k-means clustering. Individual gene

traces are shown in black, and the average cluster

trace is shown in red.

found in osteoblast culture conditions (Figure S4C).We then used

our scRNA-seq data to identify cell surface markers for each

population (Figure S4D) and used flow cytometry to measure

their expression by tdTOM+ cells from each strain. The surface

marker-defined populations were present in the expected

tdTOM+ lineages: pan-stroma Prrx1-Cre-tdTOM+ cells were

observed in all of the marker-based populations; Osx-Cre-

tdTOM+ cells were present in MSC, osteoblastic, and chondro-

cytic fluorescence-activated cell sorting (FACS) gates; and

AdipoQ-Cre and tdTOM+ cells were restricted to the MSC and

306 Cell Reports 28, 302–311, July 9, 2019

adipogenic gates (Figure 4A). Sorted

tdTOM+ cells from each strain were used

to conduct qPCR of lineage-specific genes

(Figures 4B and S4E). To further validate

these identified surface markers, they

were used to sort cells for qPCR expression

measurements, which closely matched the

patterns predicted by the scRNA-seq data

(Figures 4C and S4F). Finally, the marker-

defined populations were assayed for their

potential to mature into adipocytes, osteo-

blasts, or chondrocytes in culture (Fig-

ure 4D). Only the MSC-like FACS gate con-

tained cells with tri-lineage potential,

whereas the other sorted populations were

more restricted in their differentiation

potential.

In summary, we determined a continuous

trajectory to adipocytes or bone and carti-

lage, where bone and cartilage progenitors

were bi-potential in culture. These findings

are reminiscent of lineage-restricted, bi-

potent progenitors within the hematopoiet-

ic lineages (Akashi et al., 2000; Kondo et al.,

1997). Furthermore, the ordering of gene

expression programs from scRNA-seq could be validated using

lineage reporter strains marking cell fate.

Identification of Stroma Subtypes and Their LineagePotentialsSeveral transcription factors have been shown to play a role in

the differentiation of stromal cells to adipocytes and osteoblasts

(Dominici et al., 2006; Friedenstein et al., 1968). Therefore, hav-

ing described the differentiation trajectories of these cell types,

we next predicted transcription factors that may play a role in

A

D

B

C

0

1

2

3Pparg

Rel

ativ

eEx

pres

sion

0

500

1000

1500

2000Col1a1

Prrx1

OsxOCN

AdipoQ

0

20

40

60

80Mmp13

Rel

ativ

eEx

pres

sion

0

10

20

30

40Alpl

Prrx1

OsxOCN

AdipoQ

Prrx1-Cre/tdTOM Osx-Cre/tdTOM AdipoQ-Cre/tdTOM

ENPP1

CD140a

CX3Cl1

CD

276

CD

59a

CD

54

MSC MSC MSC

OsP OsP OsP

AdP AdP AdP

Pro-Ch Pro-Ch Pro-Ch

13%2% 0%

18%30% 37%

0%2% 14%

22%17% 1%

1%0% 0%

14%3% 0%

Pro-Os

Pro-Os

Pro-Os

Pre-Osteo/Chondrocyte

MSC Pre-Adipocyte Osteoblast Progenitor Pre-Osteoblast-Chondrocyte

FACS population:

Adi

poC

hond

roO

steo

0 10080604020Lineage potential (%)

Cul

ture

con

ditio

nFACS

Culturecondition:

0.0

0.5

1.0

1.5

2.0 Adipoq

0.0

0.5

1.0

1.5

2.0

2.5 Pparg

0

20

40

60Bglap

0

50

100

150Mmp13

MSCOsP

Pre-OC

Pro-Os

AdP

Pre-Ad

Rel

ativ

eex

pres

sion

0

1

0

1

0

500

0

25

MSCOsP

Pre-OC

Pro-Os

AdP

Pre-Ad

Adipoq

Pparg

Bglap

Mmp13

qPCRFACS scRNA-seq

Osteoblast Adipocytes Chondrocyte

Figure 4. Validation of Predicted Gene Expression States and their Differentiation Potential

(A) Flow cytometry of tdTOM+ cells for each marked Cre reporter, showing the expression of cluster-specific surface markers.

(B) Sorted tdTOM+ cells were used for qPCR of lineage-specific genes. Error bars represent the SDs of three replicates.

(C) Cell surfacemarkers were used to sort cells for qPCR of known lineage-specific genes. Left: the relative gene expression based on the qPCRmeasurements of

sorted cells. Error bars represent the SDs of four replicates. Right: the relative average gene expression in the scRNA-seq clusters. Error bars represent 95%

confidence intervals based on 1,000 bootstrap iterations.

(D) Cells were sorted based on specific surface markers, and their differentiation potential was determined. Top: representative images for the differentiation of

100 sorted Pre-OP cells. Bottom: the differentiation potential (percentage of wells with colonies) of each FACS population. Replicates are shown as individual

points, and error bars represent the SDs of 4 independent trials, with 12 wells per trial.

fate choice and maturation and tested several of them using dif-

ferentiation assays. It was important to determine whether we

had an appropriate experimental assay to test the significance

of the transcription factors. To do this, we established primary

stromal cell cultures (Figure S5A). We profiled cultured cells us-

ing scRNA-seq and compared them to cells freshly isolated from

the bone marrow (Figures S5B and S5C). Despite differences in

gene expression between cultured and freshly isolated cells, a

subset of the cultured cells maintained recognizable expression

of the MSC signature. However, consistent with previous find-

ings, these cultured stromal cells most resembled cells in our

osteoblast progenitor cluster (Figure 5A) (Ghazanfari et al.,

Cell Reports 28, 302–311, July 9, 2019 307

Osteo Chondro Adipo0

1

2

3

4

Rel

ativ

e ar

eas

of d

iffer

entia

tion Maff

ScrSh1Sh2

Osteo Chondro Adipo0.0

0.5

1.0

1.5

Rel

ativ

e di

ffere

ntia

tion

Pparg

Osteo Chondro Adipo0.0

0.5

1.0

1.5

Rel

ativ

e di

ffere

ntia

tion

Runx2

Sh1Scr

Sh2

Osteo Chondro Adipo0.0

0.5

1.0

1.5

Rel

ativ

e di

ffere

ntia

tion

Sp7

Osteo Chondro Adipo0.0

0.5

1.0

1.5

Rel

ativ

e di

ffere

ntia

tion

Mef2c

ScrSh1Sh2

Osteo Chondro Adipo0.0

0.5

1.0

1.5

Rel

ativ

e di

ffere

ntia

tion

Creb3l3

C D

A BCollagenase

Sort3’ droplet based

scRNA-seq

Passage 2 A

dipo

cyte

Ost

eobl

ast

Cho

ndro

cyte

shControl sh1 Pparg sh2 Pparg

shControl sh1 Creb3l3 sh2 Creb3l3

shControl sh1 Runx sh2 Runx

Figure 5. Validation of Predicted Lineage-Specific Transcription Factors in Cultured Stromal Cells

(A) SPRING plot quantifying the mapping of cultured stromal cells to their most similar in vivo counterparts.

(B) SPRING plots showing the expression of key stromal cell transcription factors.

(C) Representative images of adipocytes, osteoblasts, and chondrocytes with varying degrees of differentiation following shRNA knockdown in passage 1

cultured MSCs. Scale bars represent 50 mm.

(D) Transcription factor knockdown using shRNA impeded the ability of stromal cells to differentiate into specific lineages. Differentiation potential was measured

as an area of staining in wells, relative to controls. Error bars represent the SDs of four independent experiments.

2017; Whitfield et al., 2013). Next, we targeted transcription fac-

tors that were possible mediators of differentiation from stromal

cells to bone, cartilage, and fat (Figure 5B). Specifically, we

knocked down expression of Pparg, Sp7, Runx2, Maff, Creb3l3,

and Mef2c to assay the differentiation potential for each lineage

(Figures 5C and 5D). We confirmed the role of several transcrip-

tion factors (Pparg, Sp7, Runx2, Mef2c) that were previously

reported to govern bone and fat differentiation (Bab et al.,

1986; Elsafadi et al., 2016; Freeman et al., 2015; Luan et al.,

2015; Rosen et al., 2009; Scott and Underhill, 2016; Wu

et al., 2017), but also characterized the role of two other tran-

scription factors (Maff, Creb3I3). Knocking down Maff resulted

in the loss of differentiation to adipocytes, as predicted by our

scRNA-seq data, but also increased the formation of bone

several-fold above that of scramble controls, with no change

to chondrocyte production (Figure 5D). Notwithstanding the dif-

ferences in transcriptional state between cultured and fresh

308 Cell Reports 28, 302–311, July 9, 2019

samples, these experiments support the importance of original

transcription factors in mediating stromal cell fate.

DISCUSSION

In this report, we provide key insights into transcriptional events

that direct osteoblast, chondrocyte, and adipocyte differentia-

tion from stromal cells. The scRNA-seq gene expression profiles

generated here permit a real-time depiction of the dynamic pro-

cesses associated with fate choices within the bone marrow

microenvironment. A major result of this study is the detailed

characterization of three distinct differentiation paths. We identi-

fied intermediates along each path that can be prospectively

selected to test their lineage potentials. Finally, we found that

these populations are consistent with fate-marked lineages

and that their predicted differentiation potential is recapitulated

in culture.

Significant progress has been made in recent years in charac-

terizing the stroma populations during steady state and disease,

and our study provides a landscape for a better understanding of

transcriptional networks regulating the differentiation of bone

marrow microenvironment cells (Hoggatt et al., 2016; Mendez-

Ferrer et al., 2010; Mercier et al., 2011; Morrison and Scadden,

2014; Tikhonova et al., 2019; Baryawno et al., 2019). Dysregula-

tion of stromal cells has been linked to several pathophysiologic

processes, such as obesity, osteopenia, osteoporosis, cancer,

tooth loss, and aging (Engblom et al., 2017; Medyouf et al.,

2014; Mendelson and Frenette, 2014; Raaijmakers et al., 2010;

Zambetti et al., 2016). Therefore, understanding mechanisms

for regulating stromal cell differentiation could lead to improved

understanding of the pathogenesis of these disorders and even-

tually new treatments.

Our pseudotime results, as well as transcript validation, sup-

port relations between subpopulations and allowed us to explore

the transcriptional hierarchy of stromal cell phenotypes. Even

with the expansion of scRNA-seq tools and their many applica-

tions, the transcriptomic snapshot does not provide a complete

picture of the cell state (Cie�slik and Chinnaiyan, 2018; Kumar

et al., 2017). Hence, adopting a multi-modal analysis will help

yield an enhanced understanding because such technologies

hold the ability to measure multiple molecular phenotypes. More-

over, advances in understanding the fate decision of MSCs will

require more intense studies using inflammation and disease

models as well as patient samples based on our initial findings.

We show here the importance of validating the scRNA-seq data

with fate mapping and reporter strains. The same models can

be further exploited to find the significance of specific transcrip-

tion factors in lineage commitment decisions during perturbation.

In summary, this study provides important insights into the cell

composition of the bonemarrowmicroenvironment and the tran-

scriptional intermediates along differentiation paths to osteo-

blast, chondrocyte, and adipocyte fates. Moreover, despite the

differences between in vivo and cultured stromal cells, the

in vitro differentiation experiments proved useful for assaying

transcription factor relevance for different stromal fates. Our da-

taset and analysis (kleintools.hms.harvard.edu/paper_websites/

bone_marrow_stroma) will serve as a resource for future studies

investigating stromal cell differentiation.

STAR+METHODS

Detailed methods are provided in the online version of this paper

and include the following:

d KEY RESOURCES TABLE

d LEAD CONTACT AND MATERIALS AVAILABILITY

d EXPERIMENTAL MODEL AND SUBJECT DETAILS

B Mice

d METHOD DETAILS

B Cell preparation for single-cell RNA-sequencing

B scRNA-seq Analysis Method

B Quantification and Statistical Analysis of scRNA-seq

B Culture assays

d QUANTIFICATION AND STATISTICAL ANALYSIS

d DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION

Supplemental Information can be found online at https://doi.org/10.1016/j.

celrep.2019.06.031.

ACKNOWLEDGMENTS

We would like to thank members of the groups of Ravi Bhatia, Allon Klein, and

Daniel Tenen for their feedback. This project was supported by NIH grants

HL131477, CA66996, CA197697, GM080177, and 5T32GM080177-07;

startup funds from the Division of Hematology/Oncology at the University of

Alabama at Birmingham (UAB); and the American Society of Hematology

Bridge Grant (2018). D.G.T. was supported by a STaR Investigator Award,

an RCE Core grant, and a Tier 3 RNA Biology Center grant MOE2014-T3-1-

006 from the National Research Foundation (NRF) and the Ministry of Educa-

tion (MOE), Singapore. V.C. was supported by UAB T32-AI007051, and V.M.

was supported by UAB T90 DART. BioRender was used for the graphical

abstract.

AUTHOR CONTRIBUTIONS

S.L.W., I.K., and R.S.W. designed and performed the experiments, assisted

with the experimental design, and wrote the manuscript; S.L.W., D.E.T.,

V.M., V.C., S.P., and P.A. performed the experiments and analyzed the data;

R.B., D.G.T., and A.M.K. supervised the study and edited the manuscript;

R.S.W. supervised the study, designed the experiments, performed the exper-

iments, and assisted with data interpretation and manuscript writing.

DECLARATION OF INTERESTS

A.M.K. is a co-founder of 1Cell-Bio.

Received: March 27, 2018

Revised: April 5, 2019

Accepted: June 7, 2019

Published: July 9, 2019

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Cell Reports 28, 302–311, July 9, 2019 311

STAR+METHODS

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Antibodies

CD45 Biolegend Cat# 109804; RRID: AB_313441

Ter119 Biolegend Cat # 116204; RRID: AB_313704

CD31 Biolegend Cat # 102404; RRID: 312889

CD51 BD Biosciences Cat # 740062

Sca1 Biolegend Cat # 108112; RRID: AB_313349

CD140a eBioscience Cat # 25-1401-82; RRID: AB_2573400

CD276 Biolegend Cat # 124508; RRID: AB_1279206

CX3CL1 R&D Systems Cat # FAB571G-025

CD59a Miltenyi Biotec Cat # 130-104-106

ENPP1 Biolegend Cat # 149207; RRID: AB_2565483

CD44 Biolegend Cat # 103028; RRID: 830785

ICAM Miltenyi Biotec Cat # 130-104-218

CD49a Miltenyi Biotec Cat # 130-107-636

CD56 Novus Biologicals Cat # FAB7820N

CD55 Biolegend Cat# 131805; RRID: 1279263

Chemicals, Peptides, and Recombinant Proteins

Collagenase/Dispase Sigma-Aldrich Cat# 11097113001

Critical Commercial Assays

Osteogenic media STEMCELL Technologies Cat# 05504

Adipogenesis media STEMCELL Technologies Cat# 05503

Chondrocyte differentiation GIBCO Cat# A1007101

shRNA – TF501493 (Nr4a3) Origene Mouse, 4 unique 29-mer shRNA constructs in

retroviral RFP vector

shRNA - TF501290 (Maff) Origene 4 unique shRNA

shRNA - TF500537 (Dlx3) Origene 4 unique shRNA

shRNA - TF513785 (Creb3l3) Origene 4 unique shRNA

shRNA - TF511428 (Mef2c) Origene 4 unique shRNA

shRNA - TF510502 (Runx2) Origene 4 unique shRNA

shRNA - TF511411 (Sox9) Origene 4 unique shRNA

shRNA - TF500345 (Cebpb) Origene 4 unique shRNA

shRNA - TF500346 (Cebpd) Origene 4 unique shRNA

shRNA - TF514041 (Sp7) Origene 4 unique shRNA

shRNA - TF510219 (Pparg) Origene 4 unique shRNA

Deposited Data

Sequencing Reads, feature-barcode matrices GEO GSE132151

Experimental Models: Organisms/Strains

C57BL/6J Jackson Laboratory Cat# 000664

Mouse: Prrx1-Cre Jackson Laboratory Cat# 005584

Mouse: Ocn-Cre Jackson Laboratory Cat# 019509

Mouse: AdipoQ-Cre Jackson Laboratory Cat# 010803

Mouse: tdTom Jackson Laboratory Cat# 007914

Software and Algorithms

Velocyto (La Manno et al., 2018) http://velocyto.org/

dropEst (Petukhov et al., 2018) https://github.com/hms-dbmi/dropEst

(Continued on next page)

e1 Cell Reports 28, 302–311.e1–e5, July 9, 2019

Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

Monocle2 (Qiu et al., 2017) http://cole-trapnell-lab.github.io/monocle-release/

SPRING (Weinreb et al., 2018a) https://github.com/AllonKleinLab/SPRING_dev

PBA (Weinreb et al., 2018b) https://github.com/AllonKleinLab/PBA

Scrublet (Wolock et al., 2019) https://github.com/AllonKleinLab/scrublet

GSEA (Subramanian et al., 2005);

(Mootha et al., 2003)

http://software.broadinstitute.org/gsea/index.jsp

Other

MEM alpha Fisher Scientific Cat# 41-061-037

Penicillin/Streptomycin Fisher Scientific Cat# BW17-602E

Fetal Bovine Serum Denville Cat# FB5001-H

Anti-anti Fisher Scientific Cat# 15-240-062

LEAD CONTACT AND MATERIALS AVAILABILITY

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Rob

Welner (rwelner@uab.edu).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

MiceFor profiling of freshly isolated tissue and of cultured samples, bonemarrow was harvested from 8-16 week old adult C57BL/6J mice

(Jackson Laboratories). Male and female mice were used for all experiments. All experiments were approved by the Institutional

Animal Care and Use Committees of Beth Israel Deaconess Medical Center and the University of Alabama at Birmingham.

METHOD DETAILS

Cell preparation for single-cell RNA-sequencingTissue harvesting: For bone marrow preparation, femurs, tibiae, and pelvic bones were harvested immediately following euthanasia,

and placed in cold (4�C) PBS. Bones were crushed with a pestle and mortar to maximize cell recovery. Remaining bone fragments

were treated with collagenase/dispase for 45min at 37�C, these were then washed to obtain additional bone adherent cells.

Harvested bone marrow cells were lysed and then filtered through a 100 mm strainer. FACS isolation of non-hematopoietic,

non-endothelial cells: Bone marrow cell samples were stained for CD45, Ter119, and CD31, gating was set based on staining

controls and viable, triple negative cells were sorted on a BDAria.

scRNA-seq Analysis MethodSingle-cell RNA-sequencing

For scRNA-seq, we used inDrops (Klein et al., 2015) following a previously described protocol (Zilionis et al., 2017) with the following

modifications: the sequence of the primer on the hydrogel beads was 50-CGATGACGTAATACGACTCACTATAGGGTGTCGGGTG

CAG[bc1,8nt]GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG[bc2,8nt]NNNNNNTTTTTTTTTTTTTTTTTTTV-30; the sequence

of the PE2-N6 primer (step 151 in (Zilionis et al., 2017) was 50-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGNNNNNN-30; andthe sequences of the PCR primers (steps 157 and 160 in (Zilionis et al., 2017)) were 50-AATGATACGGCGACCACCGAGATCTA

CACXXXXXXXXTCGTCGGCAGCGTC-30 and 50-CAAGCAGAAGACGGCATACGAGATGGGTGTCGGGTGCAG-30. Following droplet

barcoding reverse transcription, emulsions were split into aliquots of approximately 1,000 (in vivo samples) or 3,000 (cultured sam-

ples) single-cell transcriptomes and frozen at �80�C. For the in vivo samples, two libraries (n = 1,533 cells total) were prepared for

mouse 1 and three libraries (n = 3,574 cells total) were prepared for mouse 2. For the three cultured samples, one library per sample

was prepared (n = 2,837, n = 2,164, and n = 2,520 total cells, respectively). These cell numbers correspond to the final number of

transcriptomes detected upon removal of background barcodes and stressed or dying cells (see section below).

Sequencing and read mapping

All libraries were sequenced on two runs of a NextSeq 500 (one for the in vivo samples and one for cultured samples). Raw

sequencing data (FASTQ files) were processed using the previously described (Zilionis et al., 2017) inDrops.py bioinformatics pipe-

line (available at https://github.com/indrops/indrops), with a few modifications: Bowtie v.1.1.1 was used with parameter -e 80; all

ambiguously mapped reads were excluded from analysis; and reads were aligned to the Ensembl release 85 Mus musculus

GRCm38 reference.

Cell Reports 28, 302–311.e1–e5, July 9, 2019 e2

Quantification and Statistical Analysis of scRNA-seqCell filtering and normalization

Each library was initially filtered to include only abundant barcodes (> 500 total counts for all in vivo libraries; > 800 counts for cultured

sample 1; > 1,000 counts for cultured samples 2 and 3), based on visual inspection of the histograms of total transcript counts per cell

barcode. Next, we excluded putatively stressed or dying cells with > 30% (in vivo samples) or > 20% (cultured samples) of their

transcripts coming from mitochondrial genes.

The gene expression counts of each cell were then normalized using a variant of total-count normalization that avoids distortion

from very highly expressed genes. Specifically, we calculated bxi;j, the normalized transcript counts for gene j in cell i, from the raw

counts xi;jas follows: bxi;j = xi;jX=Xi, where Xi =Pj

xi;j and X is the average of Xi over all cells. To prevent very highly expressed genes

from correspondingly decreasing the relative expression of other genes, we excluded genes comprising > 5% of the total counts of

any cell when calculating X and Xi:

Inorder to focusonheterogeneitywithin thestromalcell population,wefirstclustered thedataandexcludedhematopoieticandendo-

thelial cell clusters based on their expression of previously knownmarker genes, aswell as putative cell doublets. Specifically, we iden-

tified genes that were highly variable (top 25% by v-score (Klein et al., 2015), a measure of above-Poisson noise) and expressed at

reasonably high levels (at least 3 counts in at least 5 cells). The counts for these genes were z-score normalized and used to perform

principal components analysis (PCA), keeping the top 35 dimensions. After PCA, a k-nearest-neighbor (kNN) graph (k = 4) was con-

structed by connecting each cell to its four nearest neighbors, using Euclidean distance in the principal component space. Finally,

we applied spectral clustering (scikit-learnSpectralClustering functionwith assign_labels= ’discretize’) to the kNNgraphand visualized

the clustering by projecting the graph into two dimensions using a force-directed graph layout (SPRING(Weinreb et al., 2018a)).

We then identified enriched genes in each cluster and assigned cell type labels based on well-characterized cell type-specific

marker genes (Figure S1D). Using this approach, we excluded putative endothelial cells, granulocytes, lymphoid progenitors, mega-

karyocytes, and erythroid progenitors.

For the in vivo samples, we also used Scrublet(Wolock et al., 2019) to identify two clusters of cell doublets that co-expressed

marker genes of distinct cell types. 142 putative doublets were excluded.

Clustering and visualization of stromal cells

We repeated cell clustering and visualization using only the non-hematopoietic, non-endothelial clusters. Gene filtering, PCA, and

kNN graph construction were performed as above, except only the top 25 principal components were used, and only seven spectral

clusters were generated.

Permutation test for gene enrichment

To find significantly enriched genes in each cell cluster, we used a parameter-free permutation-based test to calculate p values, with

the difference in means as the test statistic (Engblom et al., 2017). We accounted for multiple hypotheses testing with a false discov-

ery rate of 5% using the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995). To be considered for differential gene

expression analysis, genes had to be expressed by at least 5 of the cells in the cluster of interest.

Gene set enrichment analysis (GSEA)

We used the online GSEA tool (http://software.broadinstitute.org/gsea/login.jsp)(Mootha et al., 2003; Subramanian et al., 2005) to

find terms enriched in cluster-specific genes. As input, we used significantly enriched genes with > 2-fold higher average expression

(adding a pseudocount of 0.1 transcript counts) in the cluster of interest compared to the remaining cells. The following gene set col-

lections were tested: H (hallmark), C2 (curated), and C5 (Gene Ontology).

Population balance analysis (PBA)

The PBA algorithm calculates a scalar ‘‘potential’’ for each cell that is analogous to a distance, or pseudotime, from an undifferen-

tiated source, and a vector of fate probabilities that indicate the distance to fate branch points. These fate probabilities and temporal

ordering were computed using the Python implementation of PBA (available online https://github.com/AllonKleinLab/PBA), as de-

scribed(Weinreb et al., 2018b).

The inputs to the PBA scripts are a set of comma-separated value (.csv) files encoding: the edge list of a kNN graph (k = 50) of the

cell transcriptomes (A.csv); a vector assigning a net source/sink rate to each graph node (R.csv); and a lineage-specific binary matrix

identifying the subset of graph nodes that reside at the tips of branches (S.csv). These files are provided online at http://kleintools.

hms.harvard.edu/paper_websites/bone_marrow_stroma/. PBA is then run according to the following steps:

(1) Apply the script ‘compute_Linv.py -e A.csv’, here inputting edges (flag ‘-e’) from the SPRING kNN graph (see above). This step

outputs the random-walk graph Laplacian, Linv.npy.

(2) Apply the script ‘compute_potential.py -L Linv.npy -R R.csv’, here inputting the inverse graph Laplacian (flag ‘-L’) computed in

step (1) and the net source/sink rate to each graph node (flag ‘-R’). This step outputs a potential vector (V.npy) that is used for

temporal ordering (cells ordered from high to low potential).

(3) Apply the script ‘compute_fate_probabilities.py -S S.csv -V V.npy -e A.csv -D 1’, here inputting the lineage-specific exit rate

matrix (flag ‘-S’), the potential (flag ‘-V’) computed in step (2), the same edges (flag ‘-e’) used in step (1) and a diffusion constant

(flat ‘-D’) of 1. This step outputs fate probabilities for each cell.

e3 Cell Reports 28, 302–311.e1–e5, July 9, 2019

Estimation of net source/sink rate vector R

A complete definition of the vector R in terms of biophysical quantities has been published previously(Weinreb et al., 2018b). We

assigned negative values to R for the five cells with the highest expression of marker genes for each of the three terminal lineages.

Specifically, for each lineage, we identified genes enriched in the most mature cell cluster (cluster 1 for adipocytes, cluster 6 for

osteoblasts, and cluster 7 for chondrocytes), keeping genes expressed in > 25% of cells with an average expression level

of > 0.5 transcript counts and > 2-fold higher average expression within the cluster than in the rest of the cells. We then identified

the five cells with the highest average z-score normalized expression of thesemarker genes. We used the same procedure to identify

ten starting cells (cells with highest score of cluster 3 [MSC] genes). We assigned different exit rates to each of the three lineages

using a fitting procedure that ensured that cells identified as the putative starting MSCs would have a uniform probability to become

each fate. We assigned a single positive value to all remaining cells, with the value chosen to enforce the steady-state conditionPi

Ri = 0. In the fitting procedure, all exit were initially set to one and iteratively incremented or decremented until the average

fate probabilities of the putative starting MSCs were within 1% of uniform. The separate lineage exit rates were then used to form

the lineage-specific exit rate matrix S.

Extracting and ordering cells for each lineage

To isolate the differentiation trajectory for each lineage (adipocyte, osteoblast, and chondrocyte), we ordered cells on the basis of

their graph distance from the earliest predicted MSC progenitors, keeping only cells for which the probability of the given fate

increased or remained constant with graph distance. Graph distance was measured by PBA potential, and starting with the cell

closest to the MSC origin, we added the cell with next highest potential to the trajectory if the PBA-predicted lineage probability

for cell i was at least 99.5% of the average lineage probability of the cell(s) already in the trajectory.

More formally, the procedure is as follows: order all N cells in the experiment from highest to lowest PBA potential V, with

decreasing potential corresponding to increasing distance fromMSCs. Let Ei be an indicator variable for the membership of ordered

cell i in the erythroid trajectory (Ei = 1 if cell i is in the trajectory; otherwise, Ei = 0). If Pi is the PBA-predicted lineage probability for

ordered cell i, then Ei = 1 if

Pi > 0:9953

Pk < iPk 3EkP

k < iEk

Cells on a given lineage’s trajectory were then ordered by decreasing potential. Defining tj as the index of the jth cell on a given

trajectory,

tj = 1+Xk < j

Ek

Throughout this paper, we report this cell order (akin to the ‘‘pseudotime’’ in other publications) as a percentage of ordered cells, with

the first, least differentiated cell at 0% and the most mature cell at 100%.

Significant dynamic genes

To find genes with significant changes in expression across each lineage’s cell ordering, we used a modified version of permutation

test described above (see ‘‘Permutation test for gene enrichment’’). Specifically, we applied a sliding window (n = 50 cells) to the cell

ordering and used the difference in means between the windows with the highest and lowest expression as the test statistic,

comparing the observed difference to the differences obtained after permuting the cell ordering. To be considered for analysis, genes

were required to have a mean expression of at least 0.01 transcript counts in the input cells.

Dynamic gene clustering

To find groups of genes with similar expression patterns along each lineage’s differentiation ordering, we clustered the smoothed

expression traces for all significantly variable genes (see previous section) with at least two-fold change between the windows

with minimum and maximum expression. In detail, we smoothed the gene expression traces using a Gaussian kernel (s = 10% of

cell ordering), z-score normalized the smoothed traces, and clustered the traces using k-means clustering.

Mapping cultured cell transcriptomes to freshly isolated cell data

For Figures 5A and S5B, cells from the cultured samples were projected into the same principal component space as the in vivo data,

thenmapped to their most similar in vivo neighbors. In detail, counts were first converted to TPM for all samples. Then, using only the

in vivo cells, the top 25% most variable genes (measured by v-score) with at least three transcript counts in at least five cells were

z-score normalized and used to find the top 35 principal components. Next, the cultured cells were z-score normalized using the gene

expressionmeans and s.d. from the in vivo data and transformed into the in vivo principal component space. Lastly, each cultured cell

was mapped to its closest in vivo neighbor in principal component space (Euclidean distance). In the visualization in Figure 5A, the

number of cultured cells mapping to each in vivo cell was smoothed over the kNN graph (see section ‘‘Smoothing over the kNN

graph’’). For Figure S5B, we compared cells in the in vivo MSC cluster to the cultured cells mapping to them.

Smoothing over the kNN graph

We smoothed data over the kNN graph for visualization of the density of cultured cells mapping to in vivo cells (Figure 5A).

Smoothing was performed by diffusing the number of mapped cells over the graph, as described. In brief, if G is the kNN graph,

Cell Reports 28, 302–311.e1–e5, July 9, 2019 e4

then the smoothing operator S is S = expmð� bLÞ, where L is the Laplacian matrix of G, b is the strength of smoothing ðb = 2Þ, andexpm is the matrix exponential. Then the smoothed vector X� of a vector of raw values X (number of mapped cells) is X� = SX:

RNA velocity

In order to generate the input for Velocyto (v0.17.13) (La Manno et al., 2018), which requires annotation of exons and introns for read

alignments, the raw reads were reprocessed using dropEst (v0.8.5) (Petukhov et al., 2018). We first ran droptag with the default

parameters, then aligned reads to the mouse genome (mm10) using STAR (v2.7.0a) (Dobin et al., 2013), allowing unique alignments

only (‘–outSAMmultNmax 1’). Then dropEst was run with default settings, aside from the following: ‘-m -V -b -F -L eiEIBA’. Cell

barcodes were error-corrected using the Velocyto ‘dropest-bc-correct’ command, followed by generation of Velocyto loom files

using ‘run-dropest’.

Velocyto.py was run following an example notebook (https://github.com/velocyto-team/velocyto-notebooks/blob/master/python/

DentateGyrus.ipynb). Briefly, the loom files generated by dropEst were merged and then filtered to include cell barcodes used in this

paper’s other analyses (see ‘Cell filtering and normalization’ section). Following gene filtering (3000 most variable genes with a min-

imum of 3 counts and detection in 3 cells), spliced and unspliced counts were normalized separately based on total counts per cell,

with a target size of the mean total counts across cells. PCA was run using 33 components, followed by KNN imputation with 66

neighbors. Gamma fitting, RNA velocity calculations, and Markov process simulations were conducted as in the Dentate Gyrus

example (code for the full analysis is available at kleintools.hms.harvard.edu/paper_websites/bone_marrow_stroma).

Monocle

Monocle (v2.10.1) (Qiu et al., 2017) was run on our normalized counts matrix. Using the same gene filter as in the other analyses (see

‘Cell filtering and normalization’ section), we generated an embedding using the reduceDimension() function (‘max_components = 2,

method = ’’DDRTree’’ norm_method = ’’none,’’ pseudo_expr = 0, relative_expr = FALSE, scaling = TRUE’). After generating an initial

ordering with the orderCells() function, we identified the state corresponding to MSCs and re-ran orderCells() using this state as the

root state. To compare the Monocle osteoblast cell ordering to that of PBA, we selected cells in theMSC and osteoblast states of the

Monocle embedding and then ordered cells by Monocle pseudotime.

Culture assaysStromal cell differentiation

The CD45/TER119- CD31- CD51+ Sca1+/� or tdTOM+ cells were sorted and cultured for 7-14 days. MSCs were induced toward

osteogenic and adipogenic lineages after plating to 70%–80% confluency. The cells were then cultured in differentiation induction

media for osteoblast, adipocyte and chondrocyte differentiation. The media were changed twice a week for 21 days. For osteoblast

differentiation, the cells were cultured in complete osteogenic medium from STEMCELL Technologies (05504). At day 21 after

induction, the cells were fixed with ice-cold methanol and stained with 1%Alizarin Red S for 5-10 min. The excess dye was removed,

the wells were dehydrated, and imaged. For adipogenesis, the cells were cultured in STEMCELL Technologies (05503). The cells

were fixed with 10% formalin and stained with 0.5%Oil Red O in isopropanol. Chondrocyte differentiation was induced in monolayer

culture, and the cells were cultured in 12-well plates inGIBCO (A1007101). Chondrocyte differentiation was assessed by staining with

Toluidine blue, followed by removal of the excess dye and three washes with distilled water. All images were taken using a bright field

microscope.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical and graphical analyses was performed using Graphpad Prism and Microsoft Excel 2018. Statistical analyses were

performed depending on the spread of the variable as specified and were reported as means ± standard deviation (SD). A Sha-

piro-Wilk test was used to determine normal versus perturbed distributions, and all continuous variables were tested for mean dif-

ferences. Depending on the spread of variable both nonparametric: Mann–Whitney U test, ANOVAKruskal-Wallis test, Wilcoxon test,

and parametric: Student’s t test and ANOVA were used. For ANOVA, Tukey’s post-test was used to compare individual groups.

Depending on the spread of variable, nonparametric Mann–Whitney U test, ANOVA Kruskal-Wallis test, Wilcoxon test, and

parametric Student’s t test and ANOVA were used. For ANOVA, Tukey’s post-test was used to compare individual groups. A-priori

sample size calculation was determined based on estimates from preliminary experiments in order to provide power of > 80% to

detect a 30%differencewith an alpha error of 0.05. All flow cytometry data was obtained from at least 5mice per condition. Statistical

details, including statistical tests used, number of mice analyzed can be found in the legend for each figure.

DATA AND CODE AVAILABILITY

The accession number for the scRNA-seq data reported in this paper is GEO: GSE132151 (https://www.ncbi.nlm.nih.gov/geo/).

e5 Cell Reports 28, 302–311.e1–e5, July 9, 2019

Cell Reports, Volume 28

Supplemental Information

Mapping Distinct Bone Marrow Niche

Populations and Their Differentiation Paths

Samuel L. Wolock, Indira Krishnan, Danielle E. Tenen, Victoria Matkins, VirginiaCamacho, Sweta Patel, Puneet Agarwal, Ravi Bhatia, Daniel G. Tenen, Allon M.Klein, and Robert S. Welner

Supplemental Figure 1. Filtering and Validation of scRNA-seq data. Related to Figure 1. (A) Histograms of total transcript (left) and

gene (right) counts per cell. Top: mouse replicate 1; bottom: mouse replicate 2. (B) SPRING plot of all cells prior to filtering, colored

by sample. (C) Prediction of cell doublets using Scrublet. Left, histogram of predicted doublet scores, with the vertical line marking the

threshold used to classify cells as singlets or doublets. Right, SPRING plot with cells classified as singlets (white) or doublets (red). (D)

SPRING plot of all non-doublet cells, with cells colored by spectral clustering. Cell type labels are based on expression of well-

characterized marker genes. Representative marker genes are listed below the label for each non-stromal cell type.

Doublet prediction

Removed doubletsMouse 1Mouse 2

Stromal 1

EndothelialCdh5, Pecam1

Stromal 3GranulocyteNgp, Ltf

LymphoidCd79a, Cd19

MegakaryocytePf4, Plek

Stromal 2

Early erythroidKlf1, Car1

Late erythroidHbb-bs, Alas2

2 libraries1,533 cellsMedian 1,651 UMIFMMean 2,713 UMIFMMedian 846 genesMean 1,085 genes

3 libraries3,574 cellsMedian 1,502 UMIFMMean 2,841 UMIFMMedian 770 genesMean 1,064 genes

Num

ber o

f cel

ls

102 103 104020406080100120140160

Num. genes per cell

102 103 1040

50

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103 1040

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140Mouse 1

Transcript counts per cell

103 1040

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Mouse 2

Replicates

After doublet removal

A

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D

Doublet score

num

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ls

Supplemental Figure 2. Marker gene expression in scRNA-seq data. Related to Figure 2. (A) Violin plots of cluster-specific genes and

transcription factors. (B) SPRING plots of newly identified MSC-enriched transcription factors. (C) Expression of secreted factors from

the niche that have been implicated in regulation of hematopoiesis.

Supplemental Figure 2

A

B

C

0

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4Ccl2

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Supplemental Figure 3. Patterns of gene expression across differentiation. Related to Figure 3. (A) Gene expression traces and SPRING

plots for selected lineage-specific transcription factors. (B) Clustering of gene expression traces for significantly variable genes along

the PBA-predicted Chondrocyte differentiation ordering. Z-score normalized, Gaussian-smoothed expression traces were clustered

using k-means clustering. Individual gene traces are shown in black, and the average cluster trace is shown in red. (C) Same as (B) but

for the Adipocyte trajectory. (D) Monocle 2-D embedding of the data, with each panel showing cells the previously defined clusters.

(E) Monocle 2-D embedding colored by Monocle-predicted pseudotime. (F) Comparison of transcription factor expression traces across

the osteoblast trajectory, as predicted by PBA (upper panel) and Monocle (lower panel).

Supplemental Figure 3

B

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Sp7Klf4MafNotch2PpardTbx2Zfp36l1

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Monocle ordering0 6 12

Monoclepseudotime

Pre-Ad AdP MSC OsP

Pro-Os Pre-OC Pro-Ch

E FCompare to Monocle

Component 1

Com

pone

nt 2 In cell cluster

Not in cell cluster

Original clusters on Monocle embedding

Supplemental Figure 4. Validation of scRNA-seq using fate-marked reporter strains. Related to Figure 4. (A) Mouse models marking

lineage committed cells across bone and fat. (B) FACS plots with gates for each lineage-specific reporter strain in CD45-, Ter119-,

CD31-, and viable cells. (C) In vivo reporters maintain their lineage specificity in culture during maturation. (D) Dot plot of the most

specific cell surface genes for each cell cluster. These surface markers (ICAM1 for adipocyte progenitors and pre-adipocytes; CD49a

and CX3CL1 for MSCs; CD56 for osteoblast/chondrocyte progenitors; CD276 and CD55 for pre-osteoblast/chondrocytes; ENPP1 and

CD44 for pro-osteoblasts; and CD59a for pro-chondrocytes) were used to isolate stromal populations. (E) Sorted tdTOM+ cells were

used for qPCR of lineage specific gene expression. Error bars represent standard deviation of three replicates. (F) Cluster-restricted

Supplemental Figure 4

A

C

No Cre

Prrx1

OsxOCN

AdipoQ0.0

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OCN-cre Prx1-cre Osx-cre AdipoQ-cre

Osteoblast

Adipocyte

D

E F Pre-Ad

MSC OsP

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AdQ-Cre

Prrx1-Cre

Osx-Cre

0.02%

3.6%

8.9%

2.9%

tdTomato

Sca

-1

Adipocyte culture Osteoblast culture

TdTomato

Ocn/TOMAdipoQ/TOM

Ocn/TOMAdipoQ/TOM

Freq

uenc

y

surface markers were used to sort cells for qPCR measurement of lineage-specific gene expression. Error bars represent standard

deviation of four replicates.

Supplemental Figure 5. Cultured stromal cells are biased towards the osteolineage. Related to Figure 5. (A) Bone marrow stromal cells

were cultured for 2 passages and profiled by scRNA-seq. (B) Comparison of gene expression in the in vivo MSC cluster and the cultured

cells mapping to it. Expression was z-score normalized separately for the in vivo and cultured samples. (C) Heatmaps show gene-gene

Pearson correlation of population-specific genes identified in the in vivo data set. Genes were clustered using the in vivo data (left) and

plotted in the same order for the cultured data (right).