Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations

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Article Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations

Christophe Desterke, Laurence Petit, Nadir Sella, ..., Herve´ Isambert, Thierry Jaffredo, Pierre Charbord

[email protected]

HIGHLIGHTS A correlation network with predictor genes for the BM HSPC-supportive stromal niche

An informationtheoretic network for the supportive perivascular stromal niche

Wnt facilitator Rspo2 together with SCF to amplify ex vivo hematopoietic precursors

Resource combining bioinformatics algorithms to search for novel stromal mediators

Desterke et al., iScience 23, 101222 June 26, 2020 ª 2020 The Authors. https://doi.org/10.1016/ j.isci.2020.101222 iScience ll OPEN ACCESS

Article Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations

Christophe Desterke,1 Laurence Petit,2 Nadir Sella,3 Nathalie Chevallier,4 Vincent Cabeli,3 Laura Coquelin,4 Charles Durand,2 Robert A.J. Oostendorp,5 Herve´ Isambert,3 Thierry Jaffredo,2 and Pierre Charbord2,6,*

SUMMARY The cardinal property of bone marrow (BM) stromal cells is their capacity to contribute to hematopoietic stem cell (HSC) niches by providing mediators assist- ing HSC functions. In this study we first contrasted transcriptomes of stromal cells at different developmental stages and then included large number of HSC-supportive and non-supportive samples. Application of a combination of algorithms, comprising one identifying reliable paths and potential causative relationships in complex systems, revealed gene networks characteristic of the BM stromal HSC- supportive capacity and of defined niche populations of perivascular cells, osteoblasts, and mesenchymal stromal cells. Inclusion of single-cell transcriptomes enabled establishing for the perivascular cell subset a partially oriented graph of direct gene-to-gene interactions. As proof of concept we showed that R-spondin-2, expressed by the perivascular subset, synergized with Kit ligand to amplify ex vivo hematopoietic precursors. This study by identifying classifiers and hubs constitutes a resource to unravel candidate BM stromal mediators.

INTRODUCTION It is now well established that stem cells reside in niches where they are associated with other cell types that control stem cell behavior. This is particularly true for hematopoietic stem cells (HSCs) whose niche include several cell types that express a large array of mediators that facilitate or impede the different stem cell attributes (self-renewal, lineage commitment, proliferation, and survival). During the last decade, in vivo studies, by means of transgenic mice and lineage tracing or transplantation of cells defined by phenotype, have started to unravel the complexity of the bone marrow (BM) HSC niches (Kfoury and Scadden, 2015; 1Universite´ Paris Sud, Hoˆ pital Mendelson and Frenette, 2014; Morrison and Scadden, 2014; Pinho and Frenette, 2019). Several types of Paul Brousse, Villejuif, France microenvironmental cells are involved in HSC regulation, including neural, hematopoietic, and stromal 2Sorbonne Universite´ , UPMC cells. Stromal cell types essential for HSC maintenance include vascular endothelial cells and perivascular Universite´ Paris 06, IBPS, cells, peri-sinusoidal on the abluminal side of the endothelial lining of BM sinusoids, and peri-arteriolar in CNRS UMR7622, Inserm U 1156, Laboratoire de Biologie the media and adventitia of small BM arterioles (Acar et al., 2015; Asada et al., 2017; Chen et al., 2016; Ding du De´ veloppement; Paris and Morrison, 2013; Ding et al., 2012; Greenbaum et al., 2013; Kunisaki et al., 2013; Oguro et al., 2013; Su- 75005, France giyama et al., 2006). Bone-forming osteoblasts also contribute to the HSC niche, in particular in irradiated 3Institut Curie, PSL Research animals transplanted with HSCs when perivascular and endothelial cells are impaired (Silberstein et al., University, CNRS UMR168, Paris, France 2016). Moreover, both perivascular and osteoblastic cell subsets are implicated in the regulation of distinc- 4IMRB U955-E10, INSERM, tive, B-lymphoid and multipotent progenitors (Balzano et al., 2019; Cordeiro Gomes et al., 2016; Ding and Unite´ d’Ingenierie et de Morrison, 2013; Greenbaum et al., 2013; Silberstein et al., 2016).Mesenchymalstromalcells(MSCs)may The´ rapie Cellulaire- EFS, also be considered as niche components by virtue of their phenotype, perivascular location, pro-hemato- Universite´ Paris-EST, Cre´ teil, France poietic factor expression, and precursors of the osteoblastic lineage (Hu et al., 2016; Mendez-Ferrer et al., 5Clinic and Polyclinic for 2010; Morikawa et al., 2009; Pinho et al., 2013; Zhao et al., 2019; Zhou et al., 2014). MSCs with hematopoietic Internal Medicine III, Klinikum stem/progenitor cell (HSPC)-supportive ability have also been isolated from human fetal and adult BM by Rechts der Isar, Technical culture or by phenotype using different antigen membrane combinations, some of which are similar to University Munich, Munich, Germany those used for murine MSCs (Chan et al., 2018; Isern et al., 2013; Pinho et al., 2013; Sacchetti et al., 2007). 6Lead Contact *Correspondence: Recent transcriptomic and proteomic analyses at the single-cell level have helped define the gene sets [email protected] characterizing the BM stromal populations under steady state, after stress, or in diseased conditions (Bal- https://doi.org/10.1016/j.isci. zano et al., 2019; Baryawno et al., 2019; Tikhonova et al., 2019; Wolock et al., 2019). These works first 2020.101222

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Figure 1. Study Flowchart and Cell Cluster Identification (A) Study flowchart. Sup, supportive; Nsup, non-supportive; BM, bone marrow; O, osteoblast; V, perivascular cell; MSC, mesenchymal stromal cell; PCA, principal-component analysis; PAMr, Prediction Analysis for Microarrays; LIMMA, linear model for microarray analysis; SAM, Significance Analysis of Microarrays; WGCNA: Weighted Gene Correlation Network Analysis; miic, multivariate information-based inductive causation algorithm. (B) PCA highlighting the different clusters. Left panel: The 3D score plot (PC1, PC1, PC3) using the entire sample set (224 samples) as observations and the Set1 (1,859 genes) as variables. Right panel: The 3D score plot using the entire sample set as observations and the entire set of protein-coding genes (15,056 genes) as variables. Sup-BM, supportive BM samples; Sup-EM, supportive extra-medullary samples; Nsup-BM, non-supportive BM samples; Nsup-Other, non- supportive samples of other origin than BM. (C) PCA highlighting the cell sources (cell lines versus primary cells).

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Figure 1. Continued (D) Hierarchical clustering highlighting the different clusters. Euclidian distances and Ward’s linkage were used. see also Figure S1. explored the cell heterogeneity and then inferred the possible capacity of support of the cell sets by unraveling their hematopoietic regulator profiles. On the contrary, in this work we aim at developing a strategy to first define the genes that orchestrate a functional phenotype and then investigate the cell populations contributing to that phenotype. In a previous work we have attempted to provide such information, defining several networks characterizing HSC-supportive stromal cells (Charbord et al., 2014). However, our previous study suffered from the following limitations: (1) it included too few observations for statistically sound correlation studies; (2) it did not indicate which genes in the characteristic gene sets were the best classifier for the corresponding cell population; (3) the described networks characterized a core gene set for stromal cells from different developmental origin, and consequently could not apply to defined BM stromal populations; and (4) the described networks were based on the analysis of gene-to-gene correlations; consequently most of the inferred edges included a number of indirect paths resulting from transitive correlations, which did not indicate causal relationships.

We undertook the present work to remedy those limitations. We enlarged the number of genes of interest by filtering out a minimal number of genes. We also considerably enlarged the number of observations by including transcriptomes selected from available databases according to well-defined criteria. The distribution of the samples in the 3-dimensional space defined by principal-component analysis (PCA) then enabled to identify a population of BM HSPC-supportive stromal cells, subsequently dissected in clusters corresponding to stromal cell subsets. We applied a discriminant analysis pipeline to identify the genes predominantly expressed by the different cell sets and analyzed the different cell populations to unravel the gene relatedness (Horvath, 2011; Langfelder and Horvath, 2008). Moreover, we took advantage of the numerous observations provided by recently published single-cell transcriptomes (Tikhonova et al., 2019) to disclose in the perivascular network the direct gene-to-gene interactions and reveal oriented links based on the signature of causality (Sella et al., 2018; Verny et al., 2017). Finally, as proof of concept, we studied the protein expression and activity of the top most discriminant and connected gene, the Wnt signaling pathway facilitator R-spondin 2 (Rspo2), and validated that it was expressed by a small set of perivascular cells, and acted in concert with the Stem Cell Factor (SCF) (also known as Kit Ligand) to amplify ex vivo hematopoietic precursors.

RESULTS The major aim of our work was to unravel how genes expressed by the different stromal niche populations were related to one another, i.e., how genes were organized around central hubs serving as keystones of the network. To achieve this goal we devised a strategy of analysis consisting of a sequence of steps as indicated in the ﬂowchart shown in Figure 1A.

Configuring a Dataset Representative of Stromal Cells of Mesenchymal Origin Our starting hypothesis was the same as that published in 2014 (Charbord et al., 2014), considering that essential genes for HSC support would be those differentially expressed by at least two of the major developmental sites of definitive hematopoiesis. Thus, we used the same stromal cell lines as before, from the aorta-gonad-mesonephros (AGM), fetal liver (FL), and BM, either with supportive activity in ex vivo co-culture experiments with HSPCs or with reportedly less or non-supportive ability. To initiate the present study, we filtered out a minimal number of genes as the subsequent analyses (Weighted Gene Correlation Anal- ysis [WGCNA], discriminant) constituted by themselves dimensional reduction methods. Thus, for comparing supportive with less supportive lines we now took into account all differentially expressed genes given by ANOVA (2-way with support, site and interaction as grouping variables) with a low-stringency p value < 0.06. The different genes sets were then intersected, which resulted in more than a 4-fold increase in the number of retained genes when compared with our previous study (2,327 and 481 genes in the present and previous work, respectively). The Venn diagram is shown in Figure S1A. The resulting gene set was designated as Set 1.

To account for the diversity of stromal cells, we selected in the publicly available databases datasets corresponding to articles wherein the reported supportive capacity had been evidenced by one or several of the following criteria (Table S1): (1) generation of hematopoietic colonies in co-culture of HSPCs with

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stromal cells, (2) hematopoietic differentiation of embryonic stem cells co-cultured with the stromal cells, (3) in vivo co-localization of stromal cells expressing speciﬁc markers with HSCs, (4) lineage reconstitution in vivo by HSCs co-cultured with stromal cells for variable time spans, (5) reduction of the HSC pool in vivo after depletion of the stromal cell population, and (5) characteristic phenotype (e.g., expression of the major hematopoietic regulators Kitl and Cxcl12). The relative relevance of the six criteria in the ensemble of articles is shown in Figure S1B. The supportive datasets included mesenchymal cells from the AGM, spleen, FL, and BM, either derived as continuous cell lines or primary cells isolated from the different tissues by phenotype (and in one case by culture) from transgenic or wild-type mice. The non-supportive datasets included not only stromal cells unable to support HSPCs according to the aforementioned criteria but also pluripotent stem cells (induced or embryonic), either undifferentiated or differentiated into the neural-ectodermal or endodermal lineages, and adult cardiac cells. In all, by analyzing 30 datasets we ex- tracted 224 samples. A normalization step was necessary to get rid of the batch effect (Figure S1C). When genes corresponding to Set 1 were mapped in all the datasets, a gene count of 1,869 genes remained. The ﬁnal expression matrix was therefore 224 3 1,869 (observations 3 variables). Details on samples and the list of genes belonging to Set 1 are given in Tables S1 and S2,respectively.

PCA of the Dataset Allows Identifying Four Distinct Cell Clusters Using the 224 samples and the 1,869 Set 1 genes PCA revealed four major clusters (Figure 1B, left panel). The p value of group discrimination evaluated by correlation on the first principal component (PC) axis was highly significant (p = 2.45 3 10 190). The BM stromal cells segregated in two homogeneous clusters (colored green and magenta) with 69 and 9 samples of supportive cells and non-supportive cells, respectively (Sup-BM and Nsup-BM). The blue cluster included 47 samples of supportive extra-medullary (Sup- EM) cells (26 AGM, 18 FL, and 3 spleen samples). Finally, the red cluster included 99 samples of non-supportive cells (Nsup-Other). The covariate cell source (primary cells or continuous cell lines) was not relevant in the context of this study because it did not generate sub-groups within the clusters (Figure 1C). As expected, the four clusters were not discriminated from one another when including all 15,056 genes, although a gradient from non-supportive to supportive samples was apparent (Figure 1B, right panel). Hi- erarchical clustering confirmed the four major clusters (Figure 1D).

One Gene Network Characterizes the BM Stromal Supportive Capacity Analysis of the score plots in Figure 1B indicated that the PC1 representing 35% of the variance corresponded to the contrast between Sup-BM and Nsup-Other. To understand how Set 1 genes correlated to this trait were inter-related, we applied WGCNA (Horvath, 2011; Langfelder and Horvath, 2008), which enabled identifying one module (sub-network) named turquoise whose eigengene positively correlated to the trait (Figures 2AandS2). This module included 758 genes with signiﬁcant (p < 0.05) gene signiﬁcance and module membership. This gene set characteristic of the Sup-BM cell population was designated as Set 2(Table S3). Gene Ontology analyses (Huang da et al., 2009a, b) indicated that Set 2 genes included molecules of the extracellular matrix (ECM) and implicated in the ECM-to-cell receptor interaction, and genes implicated in protein processing in the endoplasmic reticulum (glycosyl transferases and members of the glycosaminoglycan and N-glycan biosynthesis pathways) (Figure 2B). Importantly, genes belonging to the canonical Wnt signaling pathway (such as Fzd1, Porcn) were also enriched in the turquoise module. The most connected genes of the turquoise module are represented on the connectivity plots (Figure 2C). Of the two major hematopoietic regulators Kitl and Cxcl12, the former was included in the gene set, but not the latter due to its high expression in FL non-supportive lines and in some pluripotent stem cell lines. Among the other molecules some were soluble mediators (e.g., Rspo2, Srpx2, Fam20a, Il7), membrane receptors (e.g., Lpl), ECM components (e.g., Col1a2, Efemp1, Tgfbi), adhesion molecules (e.g., Svep1), and transcription factors (e.g., Ebf3, Snai2, Prrx1).

To further conﬁrm that the turquoise module gene set clearly discriminated the Sup-BM samples from all others, we performed gene set enrichment analyses (GSEA) (Liberzon et al., 2015; Subramanian et al., 2005). Using the Sup-BM gene set as reference, the enrichment plot was clearly shifted to the left corresponding to up-regulated genes in the Sup-BM cell population (Figure 2D).

To uncover the best classiﬁers in the Sup-BM cluster we followed several steps through a customized pipeline. First, using the Prediction Analysis for Microarrays (PAM) algorithm (Tibshirani et al., 2002), supervised machine learning analysis was carried out on the four clusters observed in PCA. Leave-one-out with cross- validation indicated that the gene Set 1 enabled effective discrimination between each cluster because the

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Figure 2. One Gene Network Characterizes the BM Stromal Supportive Capacity (A) Module detection by clustering using the Topological Overlap Matrix (TOM) and merged dynamic tree cut method. The merged dynamic hybrid branch cutting method was used to deﬁne the module named by color. Parameters: power: 14, adjacency: signed; correlation: Pearson’s; linkage: average; minimum module size: 30. The gene Set 2 corresponds to the set of 758 genes belonging to the turquoise module.

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Figure 2. Continued (B) Major Gene Ontology (GO) categories corresponding to Set 2 genes. ER, endoplasmic reticulum; ECM, extracellular matrix; GAG, glycosaminoglycan. (C) Connectivity plots of genes belonging to Set 2. Left panel: Intra-modular connectivity bar plot of the top 30 genes. Right panel: Cytoscape connectivity plot of the 249 most connected genes; genes were selected for representation above a threshold weight R 0.1228. (D) GSEA plot using as reference the gene Set 2, as expression dataset the matrix of all genes and samples, and as phenotype labels Sup versus Nsup. (E) Misclassification error plot using the PAMr algorithm. (F) Volcano plot obtained using the LIMMA algorithm with Rspo2 expression as quantitative linear predictor (only genes linearly dependent on Rspo2 are represented). (G) Last step of the discriminant analysis (118 genes,‘‘Discriminant 2’’ predictors). Scatterplot of the Sup-BM PAMr score versus the fold change (FC) given by the SAM algorithm (FC of gene expression in Sup-BM versus all other populations). (H) Hierarchical clustering (Euclidian distance, average linkage) and heatmap of the 118 0Discriminant 20 genes. (I) Gene intra-modular connectivity versus Sup-BM PAMr score scatterplot for the 28 most discriminant genes. See also Figures S2 and S3. misclassification error was nil when a threshold value of 1,401 genes was reached (Figure 2E). The best 939 genes predictive of BM support were then selected and applied to a Random Forest algorithm (Breiman, 2001). After performing learning on training dataset (n = 168 samples randomly selected) the model was built on 100 trees with optimization of the number of features needed to perform the tree split (the ‘‘mtry’’ parameter) for a minimal error of tree aggregations (bagging). No misclassifications of samples were found using the validation set (n = 52 samples randomly selected) when testing the Sup-BM cluster versus other clusters pooled in one single class. ANOVA on the best 720 discriminating genes (‘‘Discrimi- nant1’’ gene set) indicated that Rspo2 was the best predictive as it was up-regulated uniquely in the Sup- BM cluster. This gene was therefore used as quantitative predictor in linear models such as ‘‘linear model for microarray analysis’’ (LIMMA) (Figure 2F) (Ritchie et al., 2015). Significance Analysis for Microarray (SAM) (Tusher et al., 2001) was performed in parallel by supervised analysis between independent clusters (Sup- BM versus others). The intersection between SAM and LIMMA using Rspo2 as predictor allowed obtaining a gene set of 118 genes called ‘‘Discriminant 2’’ (Figure 2GandTable S3). By unsupervised classification, this gene set achieved a perfect discrimination of Sup-BM when compared with all others (Figure 2H). Remarkably, the ‘‘Discriminant 2’’ gene set was included within the Set 2 (Table S3). The combination of the results given by the discriminant analysis on the one hand and WGCNA and GSEA on the other enabled identifying the most discriminative and most connected genes, foremost Rspo2 (Figure 2I).

A Similar Gene Network Characterizes the BM Stromal Supportive Capacity after Inclusion of Human Samples To investigate whether the most discriminative and connected genes found in mouse BM supportive stromal cells were also characteristic of human samples we included in the study five datasets (24 samples) reporting transcriptomes of supportive or non-supportive MSCs, either primary layers or cell ‘‘strains’’ pool- ing a few clones. Details are given in Table S1. The genes corresponding to Set 1 were selected in all databases, giving a final gene count of 1,619 (instead of 1,871duetodifferenceinspecies,someofthemurine genes, such as Ly6a, having no corresponding human homolog). The final expression matrix was therefore 248 3 1,619 (observations 3 variables). In the PCA the human samples merged well with the murine ones, and the clusters were similar to those obtained with murine samples alone (Figure S3A). WGCNA allowed identifying the turquoise module whose eigengene was positively and most significantly positively correlated to PC1 (p = 10 137). Most connected genes in the mouse turquoise network were among the most connected genes in the human + mouse blue module (e.g., Kitl, Rspo2, Ebf3)(Figure S3B). In conclusion, the inclusion of human BM samples had not introduced major modifications of the gene organization, suggesting its inter-species conservation.

The Supportive Capacity Is Implemented in Four BM Niche Cell Populations As cultured stromal cell lines do not necessarily represent uncultured primary cells directly isolated from the in vivo BM niches, we selected the 51 samples corresponding to primary HSPC-supportive stromal cells isolated by phenotype (and in one case by culture) from transgenic or wild-type mice (Table S1). To investigate in which cell types the genes characteristic of supportive stromal cells were expressed and how they were organized, we analyzed the gene expression in these 51 primary cell samples, taking into account the 758 Set 2 genes up-regulated in the Sup-BM turquoise module described earlier. In the PCA the samples segregated into three clusters (Figure 3A, upper panel), which were conﬁrmed by hierarchical clustering (Figure S4A). In the PCA the cluster on the right included 23 samples designated perivascular cells (V) as they were deﬁned by the expression of the markers Nestin, Leptin receptor, Chondroitin sulfate proteoglycan, Glutamyl aminopeptidase, the cytokine

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Figure 3. The Supportive Capacity Is Implemented in Four BM Niche Cell Populations (A) PCA and module eigenvectors corresponding to samples of osteoblasts (O), perivascular cells (V), and MSCs. Upper panel: PC1 versus PC2 score plot using 51 samples and the 758 Set 2 genes. Lower panel: plot of the module eigenvectors versus PC1 and PC2 traits for the four modules identiﬁed by WGCNA. (B) Module detection by clustering using the Topological Overlap Matrix (TOM) and merged dynamic tree cut method. See legend of Figure 2A. Parameters: power: 6; adjacency: signed; correlation: Pearson’s; linkage: complete; minimum module size: 30.

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Figure 3. Continued (C) Connectivity plots of genes belonging to the V module. Right panel: Intra-modular connectivity bar plot of the top 30 genes. Left panel: Cytoscape connectivity plot of the 109 most connected genes; genes were selected for representation above a threshold weight R0.4623. (D) Discriminant analysis identifying the O-, V-, and MSC-speciﬁc genes. Hierarchical clustering classiﬁcation (Euclidean distance, Ward linkage) performed onthe61bestpredictors. See also Figure S4.

SCF, or the chemokine Stromal cell-derived factor 1 (Asada et al., 2017; Balzano et al., 2019; Chan et al., 2013; Decker et al., 2017; Ding et al., 2012; Greenbaum et al., 2013; Kunisaki et al., 2013; Mendez-Ferrer et al., 2010). The cluster on the left included 15 samples designated osteoblastic cells (O) as they were defined as Sca-1neg, but CD166pos/ne, Pdgfra or CD51pos cells, or derived from Col2.3 mice (Eash et al., 2010; Nakamura et al., 2010; Schepers et al., 2013). The cluster in the middle included 11 samples designated MSCs as they were defined as Sca-1pos, but Pdgfrapos or CD166neg cells, or as adherent cells from a clone cultured at high density (Greenbaum et al., 2013; Mead et al., 2017; Nakamura et al., 2010; Shoshani et al., 2014); it also included two samples of cells expressing Myosin-11 (Asadaetal.,2017), which was probably related to their expression of the Sca-1 membrane antigen (Kunisaki et al., 2013) that also specifies PaSMSCs(Morikawa et al., 2009). PC1 (39.4% of the variance) corresponded to the contrast between the V and O clusters (PC1 trait), whereas PC2 (13.5% of the variance) corresponded to the contrast between the MSC cluster (including the two Myosin-11-positive cells) and the two others (PC2 trait).

In accord with the merged dynamic tree cut, WGCNA identified four modules named red, blue, magenta, and black (Figures 3B, S4B–S4D and Tables S4 and S5). The module eigengene correlations (Figure 3A, lower panel) to PC1 and PC2 traits and the analysis of the genes expressed in each module enabled characterizing the red and magenta modules as specific to V and O cells, respectively. Similarly, the black and blue modules were specified as MSC osteolineage-primed (MSC-Op) and perivascular-primed (MSC-Vp), respectively. The V module (Figure 3C) included the major hematopoietic regulator Kitl and the transcription factor Ebf3 reported to maintain the supportive capacity and the architecture of the HSC niche (Seike et al., 2018). Its core of 109 highly correlated genes (out of 167) was designated as Set 3 (Table S4). The O module (Figure S4F) included the skeletal mineralization-inducing enzyme Alpl and the osteogenic transcription factor Runx2. The MSC-Op module (not shown) included the ECM components Col1a1, Col1a2, and Sparc, all implicated in ossification. Finally the MSC-Vp module (not shown) included the transcription factor Prrx1 essential for the development of vascular and perivascular matrix (Bergwerff et al., 2000), and the ECM components Emilin2 and Thsd7a implicated in vessel assembly and endothelial cell migration. KEGG pathway analysis confirmed the characterization of the V and MSC-Vp modules, canonical Wnt signaling and focal adhesion pathways being preeminent for genes in V and MSC-Vp modules, respectively (Figure S4E).

The proportion of the sum of genes in the niche cell-specific networks (V, MSC-Vp, O, MSC-Op) to the total number of genes in the Sup-BM network was 78.5%. However, the hubs in niche cell-specific networks were not necessarily hubs in the Sup-BM network. This result showed that the gene components found in the niche cell-specific networks were rewired in a different circuitry in the Sup-BM network.

ToinvestigatewhichgeneswerepredominantlyexpressedineachpopulationweusedthePAMralgorithm identifying 61 discriminant genes (designated ‘‘Discriminant 3’’), including Ebf3 and Cd302 (also known as Clec13a or Dcl1)forVandAlpl and Runx2 for O (Figure 3DandTables S4 and S5). The combination of results given by discriminant analysis and WGCNA enabled identifying the most discriminative and most connected genes in V and MSC-Vp modules (Figure S4H).

Single-Cell-Level Analysis Identifies High-Confidence Paths in the Perivascular Cell Network To refine the gene network characterizing the perivascular cell population, we analyzed a recent database (GSE108891) providing the transcriptome of the BM microenvironment at the single-cell level (Tikhonova et al., 2019). This publication compared three cell populations of vascular endothelial cells expressing VE-cadherin, perivascular cellsexpressing Leptin receptor, andosteoblasts expressingCol2.3. Different sub-populations were identified, some of which expressed the major pro-hematopoietic mediators implicated in cell-to-cell communication.

First, we conﬁrmed that Set 3 including the 109 most connected genes of the V module speciﬁed the perivascular population. We randomly selected 100 cells from each of the three populations and applied PCA using either the whole transcriptome or the gene Set 3. The three cell populations were not segregated when including the entire transcriptome, whereas they were clearly resolved when using the Set3 (Figure 4A). The genes with the

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Figure 4. Single-Cell-Level Analysis Using a Recently Described Algorithm Identifies High-Confidence Paths in the Perivascular Cell Network (A) PCA using either the whole transcriptome (left) or the Set 3 gene set (right) and 100 randomly selected genes from each of the three populations (VE- cadherin-positive, Leptin receptor-positive, and Col2.3-positive). (B) t-SNE of example genes expressed in the Leptin receptor-positive population at baseline. All 1,712 cells were included. Negative cells are in gray, and scaled positivity for a given gene is in different hues of blue. (C) Miic plot of the Set3 genes expressed in the Leptin receptor-positive cell population at baseline. The matrix consisting of 1,712 observations (cells of the Leptin receptor-positive population at baseline) and 109 variables (the Set 3 genes) was submitted to the miic algorithm. 52 genes (nodes) were retained. Edge width increases with degree of confidence in the link as given by miic. The edge line is continuous if the link was found both by miic and WGCNA, dotted if the link was found only by miic, and dashed if there is a common latent regulator found by miic. The edge color in red corresponds to a positive correlation by miic; in black, to a negative correlation by miic; and in light blue, to a positive correlation by WGCNA. The edge head pointed corresponds to an activation, and the edge head blunted corresponds to an inhibition as indicated by miic. (D) Miic plot of the Set3 genes expressed in the Leptin receptor-positive cell population after stress (treatment with 5-fluorouracil [5-FU]). The matrix consisted of 3,467 observations (cells of the Leptin receptor-positive population after 5-FU treatment) and 109 variables. The new hub and linked targets appearing in condition of stress is encircled. highest negative PC2 loadings were Kitl, Cd302, and Ebf3. Second, we applied t-distributed stochastic neighbor embedding (t-SNE) to the entire population of perivascular cells (1,712 cells) (Figure 4B). Some genes were expressed at high level in almost all cells (e.g., Marcks), whereas other genes were expressed in only a fraction of the cells. It was roughly possible to distinguish two regions, a large cluster A (circled red) including cells expressing genes of the V module (e.g., Rspo2 and high amounts of Kitl) and a smaller cluster B (circled green) including cells expressing genes of the O module (e.g., Alpl and Runx2).

As the Set 3 genes specified the perivascular population corresponding to the major cell cluster A we took advantage of the large number of Leptin receptor-positive (LEPR) single cells to refine the V gene network. To identify direct paths between genes including causal relationships and inferring latent common regulators of expressed genes we applied the multivariate information-based inductive causation algorithm (miic) (Sella et al., 2018; Verny et al., 2017), submitting to it the matrix consisting of 1,712 observations and 109 variables (the Set 3 genes). As expected only a fraction of nodes (52) defining direct high-confidence paths were retained (Figure 4C). There were two major hubs, Kitl and Cd302.Remarkably,Kitl was linked to Rspo2, Il7, Ebf3, and Snai2. Moreover, the miic algorithm uncovered a number of ‘‘v-structures’’ (e.g., Gja1– > Ctsk < – Ostc) suggesting cause-and-effect interactions and the presence of latent regulators (e.g., a latent negative regulator upstream of Srpx2 and Kitl).

Tikhonova et al. studied the transcriptome of LEPR cells not only under steady-state condition (baseline) but also after stress, i.e., after treatment of the mice with the drug 5-fluorouracil. We introduced therefore in our study this dataset including 3,467 cells. As shown by miic analysis the network was largely modified with a large increase in the number of nodes (89) and appearance of new hubs such as Marcks (Figure 4D). However,thehubsobservedinbaselinecondition,inparticularCd302 and Kitl, were still present and Kitl retained its direct links with Rspo2, Il7, Ebf3, and Snai2. There was also a large increase of new (not found in cells at baseline) ‘‘v-structures’’ (e.g., Marcks– > Ncam <–Srpx2 or Ltbp1– > Ncam < – Srpx2). In short, by means of miic we were able to prune and remodel the gene network generated by WGCNA, which confirmed Kitl as major hub for the perivascular cell network, as well as identify possible causal interactions and show major network remodeling in cells responding to stress.

The Wnt Signaling Pathway Facilitator R-Spondin 2 Acts in Concert with the Kit Ligand to Amplify Ex Vivo Hematopoietic Precursors In our analysis, Rspo2 appeared as the best predictor of the Sup-BM capacity and was directly linked related to Kitl coding for the SCF widely recognized as a major stromal regulator of HSC attributes. R-spondin 2 (encoded by Rspo2) is known as a Wnt signaling pathway facilitator (Niehrs, 2012). Thus, to validate the aforementioned integrative analyses to deﬁne candidate stromal regulators of hematopoiesis, we studied the cell distribution of R-spondin 2 on BM biopsies and its effect on HSPC in vitro.

Histology of murine BM showed that R-spondin 2 was expressed in a subset of the LEPR peri-sinusoidal cell population, which conﬁrmed the results of the single-cell transcriptome analysis described earlier (Fig- ure 5A). It was also expressed in some LEPR-negative hematopoietic cells.

Over the whole range of our 224 samples, the expression level of Rspo2 was positively correlated, in a highly signiﬁcant way, to that of Kitl, being the seventh ranked gene among the top correlated ones (Figure 5B). To

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Figure 5. The Wnt Signaling Pathway Facilitator R-Spondin 2 Acts in Concert with the Stem Cell Factor (SCF) to Amplify Ex Vivo Hematopoietic Precursors (A) Expression of R-spondin 2 in peri-sinusoidal cells. R-spondin 2-positive/Leptin receptor-positive (LEPR) peri-sinusoidal cells: continuous arrows; R- spondin 2-negative/LEPR-positive peri-sinusoidal cells: discontinuous arrows; S, sinusoids; IsoB4, isolectin B4 labeling of endothelial cells and macrophages. Scale bar, 10 mm. (B) Correlation of Rspo2 to Kitl expression values in all studied samples. (C) Effect of R-spondin 2 on Linneg/Sca-1pos/Kitlpos (LSK) cell number. SCF (10 ng/mL) was present in all tested conditions, whereas Wnt-3a (100 ng/mL) and R- spondin 2 (20 ng/mL) were as indicated. N = 60 per condition (12 experiments with 5 wells per condition). Data are presented as mean G SEM. Paired t test ***p < 0.0001, **p < 0.001 in paired comparisons. (D) Effect of R-spondin 2 on LSK cell phenotype (one representative experiment out of 8). Flow cytometry analysis was carried out on cells collected at day 12 and cultured in the presence of 10 ng/mL SCF alone (upper panels), 100 ng/mL SCF + Wnt-3a (middle), or 20 ng/mL SCF + R-spondin 2 (lower). To obtain a more precise evaluation of the Sca-1pos/Kitpos cells we first selected the Ter119neg/Gr-1neg cells before evaluating in this gate the % of Sca-1pos/Kitpos cells. D (E) Effect of R-spondin 2 and Wnt-3a on Wnt downstream targets Tcf7, Bmp4, and Axin2 and on the R-spondin receptor Lgr6. Expression level as 2- Cq, DCq = Cq(gene of interest) - Cq(housekeeping gene), housekeeping gene being Gapdh. N = 5 per condition. Box limits are the 25th and 75th percentages, center lines are mean, and the whiskers are the mean SD values. Data comparison by paired t test. **p < 0.01, *p < 0.05 in paired comparisons. (F) Effect of the ß-catenin signaling inhibitor increasing concentrations of PKF118-310 (ng/mL) in the presence (left) or absence (right) of R-spondin 2onLSK cell number. N = 14 per condition. Legend as in (E). *p < 0.05, **p < 0.01, ***p < 0.001 in paired comparisons. validate the functional consequences of Rspo2 expression, we studied the effect of the recombinant R-spondin 2 on hematopoietic precursors in the presence or not of recombinant SCF and compared the effect with that of the canonical Wnt pathway effector Wnt-3a. Linneg/Sca-1pos/Kitpos (LSK) cells were sorted from the BM of C57BL/6 mice and seeded in methylcellulose for colony-forming unit (CFU) evaluation in the presence of a range of R- spondin 2 concentrations (10, 20, and 50 ng/mL) with and without hematopoietic cytokines and Wnt-3a (100 ng/mL). CFUs were observed only in the presence of cytokines, without additional effect of Wnt-3a or R- spondin 2 (not shown). In a second set of experiments sorted LSK cells were seeded in 96-well plates (200 cells per well) in the presence of 10 ng/mL SCF, 20 ng/mL R-spondin 2, or 100 ng/mL Wnt-3a or a combination of these. After 12 days in culture in the presence of SCF alone the number of cells increased by 19-fold (Figure 5C) and cells differentiated into a majority of Gr-1pos granulocytes and monocytes (z49% of the events), CD41pos megakaryocytes (z2%), and Ter119pos erythroblasts (z12%), whereas the percentage of Sca-1pos/Kitpos drop- ped to z1.9% (Figure 5D). No growth was observed in the presence of R-spondin 2 or Wnt-3a without SCF (not shown). When R-spondin 2 and Wnt-3a were added to SCF the number of cells increased by 32- and 27- fold, respectively (Figure 5C) and the pattern of myeloid differentiation was unchanged (Figure 5D). On the contrary, there was no amplification in cultures supplemented with the three factors (SCF + R-spondin 2 + Wnt-3a) when compared with cultures with SCF alone. When compared with cultures with SCF alone, increases in amplification factor were highly significant in cultures with SCF + R-spondin 2 or SCF + Wnt-3a (Figure 5C). The percentage of Sca-1pos/Kitpos increased by 3- to 4-fold in cultures with SCF + R-spondin 2, but only in five of eight cases (Figure 5D).

To verify that the proliferative effect of R-spondin 2 was mediated through the Wnt canonical pathway we carried out two sets of experiments. First, using qRT-PCR, the levels of the major Wnt downstream targets Axin2, Tcf7, and Bmp4 were measured after 24 h of treatment with R-spondin 2, Wnt-3a, and their combination (Figure 5E). These experiments showed that R-spondin 2 had no detectable effect by itself, contrarily to Wnt-3a that induced, as expected, a significant increase. Importantly, the combination of Wnt-3a and R-spondin 2 significantly increased the effect even more when compared with Wnt-3a alone, confirming the facilitating role of R-spondin 2. Interestingly, in our culture system Wnt-3a significantly increased the expression of the R-spondin receptor Lgr6 without additional effect of R-spondin 2 (Figure 5E), whereas the levels of the other receptors Lgr4 and Lgr5 remained low to nil (not shown). Second, sorted LSK cells were cultured in the presence of R-spondin 2 and with the b-catenin signaling chemical inhibitor PKF118-310 at increasing concentrations (Figure 5F). The inhibitor in the presence of R-spondin 2 induced a significant dose-response inhibition of LSK proliferation, which was not observed in the absence of R-spondin 2.

Taken together, these data indicate that, in the presence of SCF, R-spondin 2 has a direct proliferative effect on LSK cells, but does not modify the myeloid pattern of differentiation. This effect is mediated by the Wnt canonical pathway, which conﬁrms the facilitating role of R-spondin 2 in our experimental setting.

DISCUSSION In this study on the BM microenvironment using a combination of algorithms, one of which recently applied to identify reliable paths and network v-structures in complex systems (Sella et al., 2018; Verny et al., 2017),

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we have identified a gene network characteristic of the HSC-supportive capacity of BM stromal cells and then defined gene networks for the different supportive niche cell populations contributing to that phenotype, i.e., perivascular cells (V), osteoblasts (O), and MSCs including perivascular-primed (MSC-Vp) or osteoblast-primed (MSC-Op) cells. Comparison of the gene networks specific to the niche cell populations with the network characterizing the HSC-supportive capacity as a whole made clear that the supportive phenotype resulted from the coordinated effect of several niche cell populations. The major hematopoietic regulator Kitl was a major hub in the network characterizing the supportive perivascular stromal cells, and the facilitator of the Wnt signaling pathway Rspo2 appeared as the best predictor of the supportive capacity and was directly linked to Kitl in the perivascular cell network. We have therefore validated the biological relevance of R-spondin 2 by showing that this protein enhanced the proliferation of hematopoietic precursors in the presence of SCF. This study by identifying many classifiers and hubs beside Rspo2 and Kitl constitutes a resource to unravel candidate BM stromal mediators.

In recently published analyses of transcriptomes where a large number of stromal cells were studied at the single-cell level the primary purpose has been to identify stromal cell sets, and the secondary purpose to infer possible capacity of support of the cell sets by unraveling their hematopoietic regulator profiles. Bearing in mind the opposite difference of approaches as here we first defined the genes that organize a functional phenotype and then investigate the cell populations contributing to that phenotype, one may attempt to compare how subsets resulting from our analysis relate to subpopulations resulting from single-cell analysis. Comparison of our gene sets with data reported by Tikhonova et al. indicates that the V and O clusters in our analysis broadly relate to adipocyte-biased P1 and P2 and osteo-primed P3 and P4 clusters of the LEPR population, respectively (Tikhonova et al., 2019). Further comparisons indicate that our V cluster is related to the MSC cluster 1 expressing Lepr in Baryawno et al. (Baryawno et al., 2019), to the MSC P3 cluster in Wolock et al. (Wolock et al., 2019), and to the cluster of peri-sinusoidal stromal cells in Balzano et al. (Balzano et al., 2019). In our work, MSCs, prospectively specified using a distinct phenotype or selected by growth in culture, were found to constitute a heterogeneous population including cells expressing genes implicated either in ossification (MSC-Op) or in vascularization (MSC- Vp). Taken together our work and the works using single-cell analysis identify a perivascular cell population with HSC-supportive capacity, in part due to the expression of known hematopoietic regulators. Moreover, whereas single-cell analyses help establish subsets within a mixed population and unravel differentiation trajectories between these subsets, network analysis gives important insight in the organization of gene expression within the population as a whole and indicates how networks in different cell sub-populations are coordinated to orchestrate the global functional phenotype.

As analyses, such as WGCNA, are optimally suited to identify correlated networks, but do not identify direct gene-to-gene interactions or potential causal links, we used miic analysis (Sella et al., 2018; Verny et al., 2017), recently extended to analyze continuous or mixed-type (e.g., continuous-categorical) data (Cabeli et al., 2020). The miic algorithm identifies direct paths with high confidence, and, as such, reveals potential cause-and-effect links as well as latent regulators, which may not show up when looking only at gene expression data. However, to yield significant results the miic algorithm requires a large number of observations. Consequently, we took advantage of the recently published transcriptomes of single LEPR cells (Tikhonova et al., 2019) to apply the algorithm using a subset of genes characterizing the perivascular V population. This approach enabled pruning and remodeling the network defined by WGCNA, thus providing a clearer picture by clearly individualizing the major hubs in different conditions.

The combined WGCNA and miic analysis conﬁrmed that Kitl was a major hub. Kit ligand is a well-established major hematopoietic regulator; its in vivo deletion in perivascular stromal cells leads to dramatic depletion of the BM HSC pool (Asada et al., 2017; Ding et al., 2012). Moreover, in our miic analysis, we found that Kitl was directly linked to Rspo2, Il7, Ebf3, and Snai2, all genes known as hematopoietic regulators (this study and Balzano et al., 2019; Charbord et al., 2014; Seike et al., 2018). Furthermore, we also identiﬁed the C-type lectin Cd302 as another major hub. CD302 has been reported as implicated in dendritic cell migration (Lo et al., 2016), but it remains to be studied how this lectin is involved in the stromal niche regulation of HSCs.

In cells exposed to stress, we observed remodeling of the gene networks with a large increase in the number of direct paths and the appearance of new hubs, such as the cytoskeletal Ca2+ regulator Marcks,suggesting that the cells had modiﬁed their actin cytoskeleton pattern with possible effect on migration and

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adhesion.However,thehubsobservedinbaselinecondition,inparticularCd302 and Kitl, were still present with maintenance of their major connections, suggesting that the gene organization observed at baseline is not replaced, but augmented under condition of stress.

In our analysis Rspo2 appeared as the best predictor of the Sup-BM capacity and was directly linked to Kitl in the perivascular cell network. R-spondin 2 is a facilitator of the Wnt signaling pathway. By binding to one of its Leucine-rich repeat-containing G-protein-coupled Receptors (LGR4-6) it forms a complex that hampers the degradation of the Wnt frizzled receptors through the RNF43 and ZNFR3 transmembrane ubiquitin ligases, thereby boosting Wnt signaling strength (de Lau et al., 2014; Niehrs, 2012). Both Wnt and R-spondin ligands are essential to impede the differentiation of Lgr5-positive intestinal stem cells and maintain their self-renewal capacity (Yan et al., 2017). The reported effects of Wnt signaling and stromal Wnt inhibitors have underscored the dose and environmental context dependency of the different mediators on HSC cycling and their regenerative capacity (Fleming et al., 2008; Renstrom et al., 2009; Ruf et al., 2016; Staal et al., 2016). We found that R-spondin 2 enhanced the proliferation of hematopoietic precursors in the presence of SCF. This effect was Wnt dependent, which is in agreement with similar studies on R- spondininothercelltypes(Kazanskaya et al., 2004; van Andel et al., 2017).Theeffectmightbemediated by the binding of R-spondin 2 to LGR6, which, we found, is the only receptor whose transcript is expressed at a significant level by the hematopoietic precursors. Alternatively, R-spondin 2 may regulate frizzled receptor stability through the RNF43/ZNFR3 ligases independently of the LGRs, through an as yet undefined co-receptor (Lebensohn and Rohatgi, 2018; Szenker-Ravi et al., 2018). By analyzing the recently published transcriptomes of single LEPR cells we found that the R-spondin 2-expressing cell population was a subset of LEPR perivascular cells, which was confirmed by the examination of BM biopsies showing a subset of R- spondin 2-positive cells on the abluminal side of sinusoidal endothelial cells. R-spondin 2 was also expressed in some Leptin receptor-negative hematopoietic cells, which is in agreement with the expression, mostly in the erythroblastic series, reported in Gene Expression Commons (https://gexc.riken.jp). Taken together, our analysis identifies R-spondin 2 as a yet unreported Wnt signaling pathway-associated stromal regulator of hematopoiesis.

Second to Rspo2, the genes coding for the soluble mediators Fam20a and Srpx2 were highly connected best predictors of the Sup-BM capacity. Fam20a has been isolated from a hematopoietic cell line treated with SCF and is expressed in myeloid progenitors (Nalbant et al., 2005). This gene whose mutations are responsible for amelogenesis imperfecta and nephrocalcinosis codes for a secreted pseudokinase implicated in biomineralization (Ohyama et al., 2016). Srpx2, an X-linked gene acting as a ligand for the uroki- nase plasminogen activator surface receptor, plays a role in angiogenesis by inducing endothelial cell migration and the formation of vascular network (Miljkovic-Licina et al., 2009). Srpx2 is upregulated in certain types of telocytes that are interstitial cells probably of mesenchymal origin found in multiple tissues (Zhu et al., 2015). Of note, another gene belonging to the Sup-BM module, Svep1, a ligand for integrin a9b1 that has been isolated from the BM stromal line MS-5 (Gilges et al., 2000), is also upregulated in certain types of telocytes (Song et al., 2019).

In conclusion, this study by combining different bioinformatics algorithms constitutes a resource to identify candidate BM stromal mediators whose effect on the different HSC attributes should be demonstrated in detail by upcoming experimental work.

Limitations of the Study The present analysis of transcriptomes relies on published datasets, and, as such, unpublished transcriptomes of relevant cell types could not be taken into account. In addition, this work indicates that R-spondin 2 ampliﬁes in the presence of SCF the proliferation of murine hematopoietic precursors in vitro. However, in vitro studies do not faithfully recapitulate HSC self-renewal as studied in in vivo transplantation assays. Finally, although Wnt signaling is mostly preserved across species, and the identiﬁed gene network included human samples, the effect of R-spondin 2 on murine cells may not entirely reproduce the response of human precursors to this Wnt mediator.

Resource Availability Lead Contact Further information should be directed to and will be fulﬁlled by the Lead Contact, Pierre Charbord (pierre. [email protected]).

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Materials Availability This study did not generate new unique reagents.

Data and Code Availability This study did not generate new dataset, but analyzed datasets in public repositories. Accession numbers for all datasets analyzed are given in Table S1.

METHODS AllmethodscanbefoundintheaccompanyingTransparent Methods supplemental ﬁle.

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.101222.

ACKNOWLEDGMENTS Work supported by grants of the Fondation pour la Recherche Me´ dicale (DEQ20100318258), Institut national de la sante´ et de la recherche me´ dicale (Inserm), Centre national de la recherche scientiﬁque (CNRS), and German Research Foundation (RO: DFG OO8/16). We thank Sophie Gournet for revising the illustrations.

AUTHOR CONTRIBUTIONS Conceptualization: P.C. and C. Desterke; Methodology: P.C., H.I., and C. Desterke; Software: C. Desterke, N.S., and V.C.; Formal analysis: P.C., H.I., and C. Desterke; Investigation: L.P., L.C., and N.C.; Resources: T.J.; Writing -Original Draft: P.C.; Writing -Review and Editing: P.C., R.A.J.O., T.J., C. Durand; Funding Acquisition: T.J.

DECLARATION OF INTERESTS The authors declare no competing interests.

Received: November 25, 2019 Revised: March 19, 2020 Accepted: May 27, 2020 Published: June 26, 2020

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iScience 23, 101222, June 26, 2020 17 iScience, Volume 23

Supplemental Information

Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations Christophe Desterke, Laurence Petit, Nadir Sella, Nathalie Chevallier, Vincent Cabeli, Laura Coquelin, Charles Durand, Robert A.J. Oostendorp, Hervé Isambert, Thierry Jaffredo, and Pierre Charbord

B 12% a 8% 30% b c d 24% 6% e f 20%

a 11.4 b c 9.5 Before RBE BeforePCA (80.6%) RBE

42 7.6 6262

5635 32 5.7 5009 22 3.8 4383 RBE 12 1.9 3757

Value Value 3131 0

-7

PC2 PC2 PC2 Frequency Frequency 2504

-1.9 -16 Before 1878

-26 -3.9 1252

-36 -5.8 626

0 -46 -7.7 -6.9 -4.5 -2.2 0.2 2.6 5 7.4 9.7 12.1 14.5 16.9 -38-50 Row 1 Row 23 Row 45 Row 67 Row 89 Row 111 Row 133 Row 155 Row 177 Row 199 Row 221 -16-27 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample5-5 7 -56 2716 Value 4938 60-600 -529 -459 -389 -319 -250 -179 -109 -39 30 100 Sample PC3 PC1

9.05

8.45 PCA (29%) After RBE 80 After RBE 7.85 All Rows of 1-removeresult

57 3030 7.25

2727 34

6.65 2424 11 2121 6.04 RBE

-11 Value Value 1818 5.44

1515 -35

PC2 PC2 Frequency Frequency 1212 4.84 -57 After 909 4.24 -80 606

-103 3.64 303

0 -126 3.04 -1.2 0.4 2 3.6 5.3 6.9 8.5 10.1 11.8 13.4 15 -70-90 Row 1 Row 23 Row 45 Row 67 Row 89 Row 111 Row 133 Row 155 Row 177 Row 199 Row 221 -32-51 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample-135 6 Sample 7 Value -150 4324 8162 100-90 -71 -53 -35 -17 0 18 36 54 72 90 Sample PC3 PC1

Supplemental Figure 1 (related to Figure 1): Study flowchart and cell cluster identification A: Venn diagrams leading to the identification of the Set1 gene set. The gene sets obtained after ANOVA comparing transcriptomes of supportive vs. less or non supportive stromal lines from each developmental site were intersected. The resulting Venn diagrams for the genes UP- and DOWN- regulated in the supportive lines are shown on the left and right panels, respectively. Set 1 corresponds to the shaded areas. Abbreviations for the cell lines: 1B6: supportive AGM UG26.1B6; 3B5: less supportive AGM UG26.3B5; AFT: supportive FL AFT024; BFC: non-supportive FL BFC012; B9: supportive BM BMC9; B10: less supportive BM BMC10. B: The relative relevance of the criteria evidencing the supportive capacity of stromal cells in the ensemble of articles corresponding to the datasets analyzed in this work. a) generation of hematopoietic colonies in co-culture of HSPCs with stromal cells, b) hematopoietic differentiation of embryonic stem cells co-cultured with stromal cells, c) in vivo co-localization of stromal cells expressing specific markers with HSCs, d) lineage reconstitution in vivo by HSCs co-cultured with stromal cells for variable time spans, e) reduction of the HSC pool in vivo after depletion of the stromal cell population, and f) characteristic phenotype. C: Effect of Removal Batch Effect on Quality Control parameters using the matrix consisting in the 224 observations (samples) and the 15056 variables (entire transcriptome after dataset merging). Mean value and dispersion of the expression level of all genes in a sample (a), distribution of the gene expression levels (b), and representation of the samples in the PCA space (c).

A B C

Scale independence scale free R^2= 0.84 , slope= −1.98 , trunc.R^2= 0.98 Module−trait relationships

26283032 18 2224 ● 16 20 1 14 MEturquoise 0.98 91012 (2e−155)

8 0.5 0.8 − 7 ● MEgrey −0.017

1.0 (0.8) 6

− 0.5 ● 0.6

●

1.5 −0.23

− MEyellow ● (7e−04) 5 ● 0 log10(p(k)) 0.4 ● Model Fit, R^2 2.0 505254565860 − −0.36 36384042 48 MEgreen 34 ● (3e−08) 4446 4 2.5 − ● 0.2 −0.73 −0.5 MEbrown (3e−39) 1 3.0 − 3 2 ● −0.97

0.0 MEblue (2e−141) −1 0 10 20 30 40 50 60 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Soft Threshold (power) log10(k) Trait Power estimate : 14 Type : signed signed | beta : 14 | pearson

signed | beta : 14 | pearson | average | 30 Mean connectivity

1 800 600

2 400 Mean Connectivity

200 4

5 6 78 91012 0 141618202224262830323436384042444648505254565860

0 10 20 30 40 50 60

Soft Threshold (power) Power estimate : 14 Type : signed

Supplemental Figure 2 (related to Figure 2): WGCNA of the 1869 Set 1 genes A: Choice of the power ß for the weighted adjacencies. Upper panel: scale free index fit (R2) vs. ß power; ß=14 is chosen as the value where saturation is reached and above 0,8. Lower panel: connectivity distribution according to ß power. B: Verification of the scale-free topology index for the chosen ß power value. Model fit for a power law (black) and an exponentially truncated power law (red). C: Module trait relationships. Pearson's correlation coefficient and corresponding p-value are given for each of the identified modules.

Sup-BM Sup-EM Murine Nsup-BM Human Nsup-Other

B Transcription Atp10a Prkar1b Maged2 Soluble mediator

Cell receptor Npc2 ECM Fmn1 Tmed3 Pmp22 Gpr108 Other Ttc12 Ctsh S100a16 Abca5 Rarg Rab3a Ampd3 Cd200 Slc4a4 Swt1 Nenf Fyb Nxph4 Gpc1 Ttyh2 Angptl4 Tspan6 Abca5 Il1r1 Jam2 Sepp1 Ccdc80 Kirrel3 Atxn1 Itga5 Sdc3 Ece1 Suox Wisp20.1940256630774660.1938877521130480.197614769849629Tap2 0.191031862178247 0.189826159280652 0.196918943674317 0.19994850718630.193040486976418Ctsk Mest Adam8 0.191286308240339Lrrc4c 0.194906829043904 0.193572084452989 Kbtbd7 Ell2 Creb3l1 Chst1 0.193525038443210.1958795637481810.216390283739799Chn1 0.192378251020211 Cast Tbx15 0.2093841409026670.2090696167550650.214225530538318 Slc4a8 Grem2 0.2049360779836620.2038039839027190.195553997916620.1964554722747290.2043534593262870.192523938360752 Arhgef60.191653070968350.212243554371670.2000054125979470.201370437317334 0.200234615489053 Sash1 Hmgcll1 Glul 0.2125769016983590.1994889460802530.2011573580717160.196842648767430.2059858247258020.1915008993187520.2148005971891260.1906948572176990.2006398949460970.195068898377377 0.191022887159294 0.195621700027656 0.190244396949121 Adamts120.192988764836529 Aoc3 Rorb 0.1960819087930880.200554705392661Fat4 0.189768194503966Tmem470.1904441477739820.212729975609425 0.206122192133820.1972346630518120.190286885013117Nr1h30.2190709469598310.1895458699548470.19731434192223 0.189552411798592 0.1931105230767450.208529971285366 0.1906432872644230.20317756288681 Dap0.194514366272265 0.191463918592016 0.1911585657969840.2223702431990930.1954720972821820.2081536329743510.2190153232887210.2126287553013820.1909852802397640.1976860775155280.2079734831130110.1895121685880890.2077997300431680.220491268775530.20061853897457 0.1913118373898810.2344245312271020.1970486463430340.193284614960025 0.2359391691950010.19794626593211 0.1931116523273450.190348995415621 0.21469431584647 Hoxc10 Glrx 0.1939750661633290.1915042425219780.1990308330833170.2055897039151280.2327452881510780.2059399776656350.2175402819247210.2388866619984710.2251035801677830.1989597887749250.217338883607750.1908773063449690.2235027044455870.1931341060142840.218553644430215Prune20.1900520201163160.2180622306860930.2183719429047910.191393421253582 0.2089107562208210.1978616194172560.19685920428666 0.2195632364729420.1952762944248440.2098518935072970.195148996941564 0.191616153812048 0.2159678760642 0.204282857079221 0.198936749483846 0.2070628257671520.2261378631441180.2001724271127520.1972628861752170.2030435401393380.2029188572283850.1944991945712580.1906573373150020.2203352527680410.1998093637146650.2162663030598580.1919431715156320.227999205572208 0.2125493910756950.21476743627760.2190969842805550.2223011319904890.204469219473748 0.2075724886190020.1982156577843520.1988227470339480.241772682640664 0.229292076521281 0.198822627189144 0.2194190562911530.1983037499927140.198010925651994 0.201432379697441 0.193018971595377 Ebf3 0.200064033298973 Npc2 Pnpla2 0.202927375969591Reep50.1945233082936350.2208479396683830.2044910488538650.1993589194394120.2391189521865010.192265391262310.203210843529290.1909830520639970.2465817387902660.2191998194659040.190185106172132Eda 0.2068782973516930.1916802731711960.193897564761110.2403193232142930.203708784901690.2127394280653270.1902328117876790.2107350104076260.238470638627561 0.203318010732672 0.204061228801950.2432725027667430.217697993935093 0.2179976030359050.1949438080879650.1998972034096140.202280566164702 Tgfbi 0.1968646873551660.2185104631803750.1895891863118640.20548745184526 Sash1 0.1928885942202990.1913129823344460.2083583429661660.2003228146789240.2064882530165720.2230796329306270.2404013263588890.2138701425245520.1938979733849160.2037860869811350.2384591701185660.2258109458916830.2374856501139240.216234256429634 0.195691776685484 0.213170163490578 0.2108606836093020.1990484931273260.189981517490624 0.2487387330805670.201856452453799Sdr39u1 0.1897183664660480.2083484769246750.1978888935427130.2142422485429560.2049685128546720.2366946190913620.206473807751025 0.224647244388272 0.2032381573452440.206690053001398 0.1942397817313520.1951647057014480.203597671116505 Nfatc10.2074562388928470.1896059444860340.2078264537255730.2357631137564830.2041853536334580.2073129740369280.2088122789210450.2103912841528040.2036990428689170.2126082854796940.2228084606167130.212329496264650.2519447525245340.1915011719025610.1939586341432960.2104264164205650.191634071424710.210427607824170.1938204213837370.1972583794771140.2308392866811980.1986221191912350.2144920047363080.2091184619945290.246152516251447 0.22860243634433 0.2137769384701470.2242216722839540.192865815451520.243909571096576 0.2264342122331070.2088428945648840.19662097303797 0.2443709791232060.2031373962361440.238108788722759 0.2272955744047870.218852603600472 0.2010187860805240.2035710396172070.229220640131269 0.223717290348689 0.216306848679738 0.190282334161684 0.191366314481357 0.206600403332452 0.1991282531174030.1923249428601190.196619852187712 0.204663152977754 0.190139524033420.194567660570315 Mrgprf0.193057476901481 0.2304989395143980.1905630250955490.1998660841738180.2330114844158570.2122939042675220.232302672398144Rgs40.2079344720801510.2254249085528110.1922158109983080.2139570441168250.2155121464296620.1899887737343190.2313160022429390.2270705162365110.1992788185246560.2326633559114890.212499302974658Xbp1 0.1987273106066340.198354155894610.2544738241354250.1919650687683790.222879938192123 0.208604882002470.198177213838091 0.201290109478883 0.237114041781757 0.1949169676040130.2061857606247660.217715245973623 0.201029966718683 0.2344513193892410.238838752622639 0.190053052410539 0.193746039772743 Spp1 Mmp2 Dap 0.2055062595837640.191380896700110.2292384755311360.194337059693880.2145578911270990.1972325565467350.2079310979090950.2062374020294030.2139395771681590.2350788448759140.2143666618435340.2180211684895550.2016824788052110.2013731960395820.2360646045151960.2159242072684090.2284049153113840.1934797447217270.218493922274490.2286233302008030.2209547875626990.238256326741446 0.2279845820998460.212117468390180.227929148188018 0.2318414081376080.23079601579983 0.2125355760650610.2185202406638770.210993624937339 0.2130938727790420.1928088606716670.1903524386401510.2010612317445220.248116042156107 0.195401734446954Ampd30.2188962034603870.2174168773425390.203597420621373 0.2372183404111010.1966737832821680.214447594618431 0.1966071007984090.191240211664679 0.199759432788255 0.208914661580929Pdgfra0.1976462512819160.2107668386674780.2101250345749590.192296776748662 0.216538124997553 0.19370890125959 0.193859319441148 Unc5c 0.1992066087830330.2165181588647950.1916910830517880.2378117495664640.2062701888772140.2129107294473690.1990573340393450.2084265763406160.2007075459069110.2119282382358130.2019778408147750.2243035374558790.1998364699721740.204730579336611 0.2359345093229890.210008795056348Ltbp20.1919733610358350.2106784126730820.232519761837039 0.2050750584089380.1918947225350560.1950271124886260.2052203574035980.2247162693083460.246837778409902 0.2100313616602240.1901505116358130.200208036333213 0.2069764944828590.200223001355550.218026591582297 0.237804749594047 0.215482280486943 0.214788235423484 0.2125109824753540.201126513079141 0.2448133453469030.1938117957324910.2519741092194620.19421606563915 0.1933674630861110.251109203547975 0.199290252359738 Gss 0.1908071767757840.196549128055581Phyh0.2081764905665830.213017617474650.197414761877990.2030544936529650.1934100370736310.2050120312588720.227793348696030.2252395477070120.229161802286901 0.1986434310553810.2435694683215050.1962016567431620.2239311017832960.2194286244565370.2199717732606770.2189494733851290.2295327409558240.1906013823119210.2440720257716620.2190395982626620.2079697768136990.23603549169933 0.19277402867480.1960015700137980.2165361280317630.2063551178585140.22945130593487 0.2385662268786380.2177175504303530.2159067769876470.205584810468373 0.2118166653222630.233684342487261 Eif4e30.199315634829591 0.2130860904914050.198594427356877 0.20433610249494 0.2235881140832520.2427467171782620.2006412504017030.2066968732511970.2241450576833010.2072802363209460.212335026301339 0.198566370032977 0.212907990650289 0.1926866145469220.218656195707236 0.2176707095043610.1995711338325460.208186277706570.1941084676585420.193254794319499 0.1975845792649570.204514738049119 0.1921528657372840.2293659262214910.219658269436532 0.201656360642006 0.192483152518801 Mylip Rspo2 Clip2 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0.18931889324388 0.193186927748637 Chst1 0.1979977180823260.1923152278158840.2189048652535650.21010549288490.1910588709266770.2145718831344690.2105581574224080.2257813067217310.206221007823990.1985268311583260.198149996454470.2269744012449440.1941801202603580.20886584933070.219416330744890.1970460701856410.205498623433090.1952137433820380.21660112176840.2283534033989230.2263110923107530.218978405234173Glrx 0.2061040496292260.2149685276944960.2148828361051820.2392909220285980.2477444933560660.2271100027873170.2126754499026380.199305827548135 Medag 0.2060197919254120.2536894231298020.209665370003451 0.1963400849039750.240560568080394 0.2154322767502860.2326460074357210.222590751421549 0.232314817210566 0.2122114329129550.1928952306775130.2269008600910220.2188950998588660.201611509954879 0.2446355914672370.2199688228797330.2187148090019310.223672958073150.1921755928940390.202154430027128 0.2423864022437310.190188180886533 0.239863754205129 0.21066846875625 0.229342419620343 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0.198489858382111 0.198079428258756 Robo1 Lrrn3 Ptges 0.1993538174475650.2109538041722940.1961589900196910.2289953001876170.2025272893723720.2361558391746390.2329611822230620.2166964727379450.1968672899029960.2108418377913630.217761239120092Pdgfrb0.2264169219429130.1929805497095220.1976718334768250.2075928775540070.2195346664595860.210884294155390.2471024753197470.233524408680409 0.196232705888050.2001547710762930.2492767624555720.2266682066063590.190837792918018 0.198134394927840.232657176141244 0.2720388327353920.2641553013704270.2238747620516990.225644833610339 0.235400510912268 Csgalnact10.2246489611330450.198455164060782 0.190243024132116 0.2490819678023880.2352283210652980.2178204391702560.193159169745108 0.2633715792242180.2101615227787960.201194093219204 0.2589544960574520.2151636355355380.2108400608859860.237334661828657 0.23045619970380.200343720045639 0.2287966208363910.2079593376615680.2417117423206440.2305625467793660.2404419536754620.1901004658655410.193510525866925 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0.2144093821301180.2208100075930150.22403880224130.190210824586430.1975795931208290.2288572752082730.2293612063162350.2312607945642510.2203480070859280.2159630617554680.1959076018121680.2506096262186980.2012074587342890.2457706531550090.212989352859715Prrg10.2475092222755060.2401770510544840.1989588233920430.2106605577539760.2510624396547630.1942077231331240.2251211586006880.229573483914410.206268072257709 0.227840842029841 0.2736870671736380.2021455910721940.1967270224014330.2111328958126160.192546499526601 0.2004552575364440.193096312399535 0.2187849762746270.2489357191104160.208984887112140.217545687195288 0.248985800572795 0.189382475771369 0.2253614875751330.2143232670728830.2388488240258540.204152785157608 0.2184571045146060.1966836030150140.21826765448164 0.258182615589119 0.2158057680164920.2306128298468040.2068586676588330.1995318787100250.190444963428888 0.190114248179166 0.1939825792754720.194977676083186 Suox 0.189459626293761 0.189548852734879 0.2300567587337880.1925103391244510.2107031811972520.2012449309771540.1934136568479650.2098823615313620.2040385742966580.2110116263527870.2237179567250940.1925151724661550.218904854230480.1934576084318790.227776239859451 Tshz30.2195608167800790.2102177675342890.2394976875256870.1928233983437330.2336821593432810.2329159795865890.2410964622794510.2029760122107770.1962437594137940.213758021254681 0.2119299661207240.2392174525353180.220355645703309 0.1982292959147670.1909164634538390.189552872985593 0.1908953531407480.1961028543572860.2365049925348390.1925020646118650.190397570895028 0.226063946509756 0.2207477632513970.194696415761041 0.2120750077749640.2296678063814570.1908248001050330.2180168213458240.2015156796724140.213990344389275A4galt 0.222784334494871 0.2175604004592790.210936696612715 0.191159107344952 0.204852936919106 Emb Arrdc30.190743459413060.2118933028317230.1950028533439040.2128646783858260.194626992959280.2089438710815690.2372682748325020.2124910991241380.2022289856282520.2344433446049490.2280952964667140.2130516098881970.2386070811044510.2079604456073540.2150391420490710.2061296846845410.1985609332953460.1897843731364470.2582976198248920.2408589659771190.2174236767701330.1914482407660180.1929005384964990.2102184195610190.215839868954521 0.1937288963179590.2076187937908280.2200623279090640.206636114098793 0.2152626513877730.196013466084717 0.1942311691662320.2385425539217030.223049203856735 0.1894463004780980.217620657219585 0.189956088472314 0.2121732354888880.195631653204108 0.192626637370697 0.2246212389957160.2373071797321440.2602931109690.192716858866173 0.200837293289055 0.2169432181734690.1924017075321120.189349633124186 0.2125061225242510.196260756435809 0.231303598943451 0.198425841987482 0.21224011498736 0.2374936058732390.205891599182564 0.194089995534414 0.209281396987592 0.205458738421656 Lgals8 Ech1 Tmem45a0.1993293829084120.1905697778215790.194789860861222Pde4d0.2017722641513450.207294883116910.2152130329413620.2061882819717590.2354284689728310.2357073141523890.2298753427467820.1908929381188460.2316380918723940.2238571200248080.2335235495505770.2002694446373120.2378961453370740.212928713552970.240348777728790.211784000199947Rspo20.2172752650086660.239405906084486 0.2297004316303210.2032284203700560.2089789905500350.222107994162978 0.1943488761725480.232142204128732 0.1899565430490430.216341603721650.223334651884377 0.217370660486075 Copz20.200971168425863 0.2110530772007930.2227169204754480.2095300235883830.1995944239123450.2372309330042350.1996166792617960.223945275980926 0.241080389411822 0.237058370871829 0.211569609577067 0.195587485637790.217260413033899 0.209446233713613 0.212536457876592 Ocel1 Tiam2 Ebf3 0.2246142251818110.2343903564367380.1893406925454140.213581127377760.2030844127531790.2129545327587150.1934678930401520.1893220329038490.2055743756103880.232640588553320.1892536730246150.2087274705068630.2230877635850230.191911624922648 0.2322127830816630.2106877117696790.1928582474911740.214876927188345 0.1900590409474930.190623925597726 0.235168038343046 0.234579890497456 0.206266981923069 0.1992734809362090.189592793685988 0.1928816273565880.217338613993492 0.1945334657616790.1963927625629130.216594767455334 0.191649028736335 0.236925186627099 0.208734193810775 0.1930864525692980.200720036232993 Prr13 0.190289271218522 0.199197042126780.1979421400746520.2121081631451610.2289123918283820.2327382314246060.1913219602438190.2105324065081820.2294839288509250.2005825803161260.2480879564603820.2286367133897250.2103936362708620.227424164453393Sh3pxd2a0.1965370217545150.220513820864490.1932604426789410.2095518501414640.1931967744314810.237482786266360.2033481807406750.1995713174768240.2451580573519870.2048021329519630.1972841189664020.2240569963911970.215688939240750.2173978114361960.1926762761504040.1925359633780020.2065297192016670.2373268828403630.241704750875011 0.210059666826272 0.221357503150216 0.2380851593570040.2218952038124650.2148934857888290.2343457141164860.2291288864499140.1906514091099840.228185152115649 0.2164715665083310.2166154377945860.229527119077576 0.1997611700542960.2390741611656690.214097004902756 0.20309324118854 0.191682223356555 0.209418183002989 0.2468829577833660.194627082794308 0.207730381109105 0.209690766336062 0.201408818869863 0.19809307250293 Col1a2 0.194303225198051 0.1962257522728930.2113999912956260.189527454901250.2146966908380270.1958841624063570.2237054625502340.2034721570558260.2349549181567420.228388978004656 0.1904221557964850.2105197366106060.2363198223950610.2006892611370650.2160485742398560.1898394740430530.1956466189607740.2168438481618450.2256109314448520.193242158993331 0.1909728991778630.1896170262678250.1988787491369140.2360582402901070.1916979149278860.211971716284924 0.219429594499940.2074748328214390.2208584283418050.2309983844241340.2213198298852920.214293916295739 0.234155689350303 0.2062653672952840.2294158453110440.194092077662876 0.2021724297529610.226220567374819 0.209197730951010.1980979540753990.229543824473286 0.1960414604598330.1898515336308730.214196119352983 0.2021956991635940.1931080049928180.199274890274269 0.196143099609632 0.213126140590054 0.190027736291874 0.192788638517771 Rgcc Mylip Dmpk 0.1944934941760870.216227376063970.2158783880549460.228831027601647Sepp10.2011398756145610.1979532910422690.2288873832237750.2069545205995310.2024584476740450.2367724879071150.2286249517600130.1908317964620170.1947763461319560.221498943211770.2000736269563960.2108228098458720.2235576139036940.193436084540180.2133427703108510.1969323084079040.1937808370040540.2234283958986320.198257429479345 0.2032509021679370.1992877613535370.2111420339004020.1941666548042490.190694175222543 0.2126165969977730.1943005939938650.223684439571094 0.2437873484281140.2081253117595810.2173371662684520.229926214090030.19921648853417 0.2257222222106510.246292067322054 0.2260627377938630.196211940734345 0.1915835338254530.223205327800488 0.223160690437475 Onecut2 0.207237964245544 0.213112980326410.206661189706845 0.203749967497775 0.200171970591174 Inf2 0.189396754360132Gys10.2251239405789320.2154224250113970.192549411965060.2146647079634910.1958941976992850.1951881555134520.2234910210341950.1999415558538410.2026251745421020.2009221945310970.1918324967290420.1914812216976890.2055152703831530.2525209389752690.208004694455373 0.1927890703708750.2224817876702220.2215069091234250.2167649802912650.227682315895465 0.1912602446694810.2272265015256340.190370791819592Rassf30.225739621824326 0.2474938895635280.1955922808501230.191462646077450.2172574644030430.249938921711999 0.2025105459718190.2057620616873020.2105535290930550.2456691135497970.1986419781962450.210456073527621 0.254498721615349 0.1965651928678780.2007281915166570.1933214752952170.222246769992407 0.2070162491676140.190449227582938 0.197853187472188 0.191029011987167 0.191552327883279 Gas2 0.2260841253979680.1893917926260590.1896596777723080.2100906916656490.1898927330145740.189305710787790.2122293832411680.192135122803650.2104668339368810.2178632472564840.2292961045894030.2523282863075060.1925428296734190.20370511479360.2158139895098190.2300363767094990.1969275202046430.2103391278073850.2039919856077210.2138481666306070.245517649227894 0.206012308759192 0.222029071268520.224472686060948 0.212130676640682 0.2445286342079790.2129512161548270.229051693307351 0.2037801604270250.205967899560699 0.238181775486043 Lpl0.242972907534446 0.2015656996172360.2458383969186670.2161683917428110.20035104003345 0.24377736695214 0.193284077979442 Fam20a Cd248 0.1913226944990080.1900032917216730.1996770562547010.2140446397641290.1936926597175720.1932331485179920.1933362449256340.2135579456954080.2112340524198410.2082622080027780.236551326724666Tcp11l20.2239888342487640.2059716878157530.1983841735023470.2633187416454380.2442968864829280.2286804316968970.206140094287289 0.1970383054946640.2345455903932770.1921320121343850.2342183059994490.2003344670406910.2244084008723430.2084501574959970.239934865863245 0.2089912041605290.191397077144236 0.2544166907478330.218486831490292 0.2067386097751620.202552521289307 0.2578668318122750.1918212562149690.1948843310313020.231645632935506 0.212985664270672 0.2356598069323530.2194697199067450.193840700319097 0.194285135345759 0.215049926208173 0.225939710300741 0.19409909208638 0.195259504707993Fabp70.196322645807015 0.191315775263384 Fam162a 0.2195717825674870.2002599392468530.196534657071860.1953333163492530.2209810123300070.2414908256666810.1907711692399770.2419893266383620.2203245812968620.1962691111425880.2435653664707130.1971451545966450.2243180241894010.2199931335326460.1913701033871970.2002667753919270.2339352717697570.209814012692670.195436921795784 0.2266859280641810.219915443382050.213830481507160.2154911904521520.2272472882568160.2033503534351 0.2158743839038220.2375193107356350.2240618426089380.1938475612676150.201688047591758 0.232987449910529 0.2568725178123570.1990125048821680.225083983678019 0.2174731824979280.197509060670323 0.19901707422060.25353629729766 0.196672127896144 Rtp4 Gdf15 Gda 0.1939176553683420.1970754893977120.19213234506018St3gal10.2344490466862930.1999930676917030.193651386014340.231608160685250.241312489628072 0.1957148942221010.1946705310497860.2465946500218620.1981770189182030.22586104581559 0.203614866553187 0.2353554227957980.2237062863348330.1920673102227550.212396846258870.1919065031330360.20836616530154 0.196918445657157Rundc3b0.1962924870844820.206907980743715 0.237661101154322 0.196004016147047 0.191022239937713 Elfn1 Gnao1 0.2304300017213210.2064329289232110.1898950340199030.2144792360784630.2344895895589940.1958170846203370.2135078623135110.2033944442358170.2085168923315690.2539199122461930.2078479453377740.19170948216080.2216172537475910.2150626541765870.2077918469343510.210747996864988Arhgap280.2155099766732290.1976179149312290.196708565311024 0.1930264282502320.2170870540738670.1896914098812250.2248340713961490.2016411502628430.242532768354798 0.2006057989318990.2312866341143760.209298561612667 0.2146375296193380.211254953761158 Smoc20.24007650571373 0.205468529648275 0.205945626701731 0.2031609082634180.208597281960652 0.207395367665859 0.1897761009683930.2147556632639290.219326513271793 0.2341000111869340.1977785131754030.1912573725966520.2009713344475870.194473095359804 0.217489056088742 0.191854346715982 Pygo1 0.1979289921096220.2105525263627790.1987239610951060.2174411117515920.2071367851654040.1901656329504820.189846266073060.1931276305637380.1962176698112120.1911494304630030.2144539900473830.212669543724041 0.197750561492820.2171043002015340.2176770584952120.201621853119852 0.2065686580637880.1912122267573990.235392812894264 0.209496486205630.19249475221375 0.241740652319566 0.208019556680553 0.191876251790935Hcn20.1895013936947710.1917870366988880.1953168984967240.2048263046630490.2307134521353930.1998870203892050.20044394901763 0.2074191250799230.1908057599709640.2078198160207630.2203644178146410.202120338212065 0.2064155884539810.1991772703423360.2060860966404270.2018088116205220.19223651435256 0.2087865451785160.1921825636456210.224208408842496 0.1976014085004380.2059122693707750.20555531582460.219685410981017 0.21875848773977 0.1947962932786490.1922853119975720.20700923982930.1906103895022120.2205079418116510.1902980370080770.2044288579120390.1967275329305730.1942218964825320.1996813165838640.218263370544631 0.2122305844018210.2278679351345660.1931931653288390.218861386482776 0.2210728403360570.189334900653642 0.193776970298668 0.192824067855228 Caps2 0.196453966471872 S100a40.190754197782054 Hoxc9 Mtss1l A4galt 0.198312663940246Errfi10.1949884079376060.209716486658252Lrrc170.1989407398884990.2025998991546230.2023975331593460.196407049318220.2053954138391270.2160345174239550.191881215735505 0.2111324773265140.200674821622793 0.2254150865025910.1983413658959630.2049662306422040.220801908648460.201142334008229 Rgl1 0.1958032172583520.2064267400148410.205660385483964 0.1894854234469810.20577251767398 0.19438947226489 0.191727161127630.1897740688210450.1900190898986620.1906813480449370.198239543852789Fam110b0.200835775610894Aqp1 0.208150392371343 Il1rl10.196013395076129 Kif3c Gng8 0.1960539411608330.1941965580253110.1893409725019160.1923985961412940.2182686752047450.196954824217973 0.19145746818674 0.207555218272069 0.2150432551077260.190910521351191 0.200087444292579 0.202903988002249 0.1898805423519150.191304510113439 0.1932376058759180.20969569445556 0.189798969864759 Eif4e3 0.1907435747139480.1958897659206520.189998739202664 0.205603567447461 Kitl Csrp2 0.1915543760847540.1904851891825260.199102346679363 0.1953813866813410.1909488943097720.2004709104405370.2069848904491420.1995718486519550.191619892646753 0.204274564818322 0.197119951146794Trim68 0.191321064779536 Il7 0.189915577479557Bcar3 Bcap31 S1pr1 Scn1b 0.1952296884267390.190564258665168Efemp1 Pak1 0.192851621708093 Atp7a Camk1d Ube2l60.1908386708407520.191852584296599 Snai2 Prkag3 Pxmp4 0.189570907370515 Invs Fgf100.19116013736552Rnls Chst2 Tmed3 Tshz3 Igbp1 Lpar4 Prrx1 Dusp23 Acadl Mfap5 Rasl12 Lpl Pbxip1 Tmem121 Ctso Rassf2 Rassf5 Htra3 Commd5 Csgalnact1 Stat1 Nr2f1 Phf8 Galnt13 Plxna2 Trim21 Plec Srpx2 Chpt1 Elk3 Dip2c Wnt10b Fam132a 0,7 0,8 0,9 1,0 Vasn Rabif Connectivity Duoxa1 Glipr1 Grik2

Supplemental Figure 3 (related to Figure 2): A similar gene network characterizes the BM stromal supportive capacity after inclusion of human samples A: PCA using 248 samples as observations and 1619 genes as variables. The 3D score plot (PC1, PC1, PC3) is shown on the left panel (same sample colors as in Figure 1B). The right panel highlights the samples according to species (human vs. murine samples). B: Cytoscape connectivity plot (right) and intra-modular connectivity bar plot of the Top-30 genes (left) for the turquoise module corresponding to the Sup-BM cluster. A B C Scale independence Mean connectivity scale free R^2= 0.87 , slope= −0.83 , trunc.R^2= 0.85 400 1.0 10 22 1 9 161820 2830 48 ● 7 1214 24 32 40424446 5052 565860 8 26 3638 54

34 − 0.7 6 ●

0.8 5 1 − 0.8 300 ● − 0.9 -5.58

0.6 2 ● 4 200 MSC − 1.0 log10(p(k)) Model Fit, R^2 0.4 3 Mean Connectivity − 1.1 3 4 100 5 ● 0.2 6 ● O 7 − 1.2 8 910 ● 1214 161820 ● ● 2224262830323436 ● 2 0 384042444648505254565860 0.0 − 1.3

1.4 1.6 1.8 2.0 2.2

V 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Hierarchical Clustering Soft Threshold (power) Soft Threshold (power) log10(k) Power estimate : 6 Type : signed Power estimate : 6 Type : signed signed | beta : 6 | pearson Site_BM 0.00 D E Module−trait relationships

1 KEGG Pathways 0.93 MEblue (5e−23) Rap1 signaling 0.5 PI3K-Akt signaling 0.9 Axon guidance MEred (5e−19) Ras signaling 0 5.58 Hippo signaling red (V)

MEblack −0.76 Wnt signaling (1e−10) blue (MSC-Vp) Ras signaling −0.5 ECM-receptor interaction

−0.95 PI3K-Akt signaling MEmagenta (4e−26) −1 Focal adhesion 1 2 3 Trait -log(p)

signed | beta : 6 | pearson | complete | 30

F G V-score vs. Gene connectivity Pfdn5 Celf4 Tmem121 Svep1 Ell2 Pou1f1 1,5 Chpt1 Ehd3 Rpl39l Fhod1 Gpc1 Zcchc6 0.3174961100059160.318392393313878 Slc16a13 E230008N13Rik0.3168355891511340.326506759684537 Csgalnact 0.3314572687226290.338279749106310.3180099484034450.3465881706549530.330247567263438 Nol4 0.3131878839257480.3315467100858090.3117157085702160.343208256691512 0.3198609089846140.3193434130000950.3487301373748810.343369302290013Onecut2 0.311577772033081 Loxl1 Grhl1 Dnajc270.3181653456852820.3314214186654720.3387936983208020.346259338162943Cd200 1,0 Adss0.3319392199237280.3375860475529360.3213348882687120.3152069318545030.3509033840084450.3334471438540310.3135241408637960.320937519697825 Cep164 Speg 0.3235348446092650.3442970970374270.3930784432369420.3270701653261830.339252955801120.343797360345550.32162161112934Hif1a0.3334977417999540.376441423507279 0.4005948726219580.3175638096654750.318560500050548 Lpl 0.32058656293302Ldhb0.328712532498590.3782687744673760.3411138633052620.360614981202664Plekhg60.3488299984528340.3575308996979220.3646245777511010.3485636464667340.3913241927347670.3707476766592850.356607306632192 0.380937971494675 0.3149829473774330.3681100865977150.3420644883913250.3413590538634190.352783259687384 0.346878456225372 0.313031780688674 V-score Cbr2 0.3135748083954420.3833128115662650.3401412937472810.3296911551912490.3459297070226080.3516246959977890.3690483238029530.3589280046392330.3194850227908180.3293244347083320.3412071983196990.3241799726613410.318843321305190.3273120807838980.3276738143905710.3364426138579620.3515678924182860.3120508068430220.3472767144267840.3911159921569480.3491807710436520.329793871275411 Fam83h 0.3366357951163550.3233387283585790.329461308664547 0.341842171930133 0.327470382090837 Smoc2 Hlx 0.3496897762546610.3220153596587820.3728380536570480.358260705821550.3568024676400410.3896844590260670.3735033003162240.3166621383647780.3176466040671170.3414716678377320.3445664793712580.3124447846799750.3800573850717640.374368998764071 0.370753277648333 0.377883502206165 0.3137188884029940.3998045218112720.341953191424422 0.335117352227728Alpl 0.3183927850603170.3571435177783520.3266860362783750.352046913457317Tiam20.3215950492860550.3126909091168540.370724848181580.3273086530483570.3580719742499740.3355855098025890.3236851758746860.3228335154517280.3597246590552150.3477275123049110.3575592140433480.31721606843502 0.3760767303637060.359231483049977 0.3223251146474140.3525787689231320.3309632628309430.401661564288998 0.3509612566700030.347570412326243 0.3364809835221430.3394274621094610.337244164256728 0.340109696347217 0.350558564086654 Runx2 Slc37a2 V-score Ltbp2 Btg1 0.313757506118940.318887228216220.33877572311040.3158678242185170.3292187508072150.392734686558530.3170240644826080.3353661284705550.3877634280109060.3868614102168020.3474155725564440.3539233510288480.3241746115456590.3524020649207090.3805994616735120.4026118721264520.3857438342878340.3365040302268720.389674140559940.3537387410137820.358554565432320.3187476729345540.367524287508491 0.330183404740446 Fbln1 0.315371172868588Lgmn0.3179321250390520.3390166375199160.365142214202210.361805365734360.3940440310884550.3230231905629920.3111913533276770.3499829549116470.3577766441992570.3777126145141360.39877933691830.3759139840981950.398226211172896 0.331000020195689Cdr2l0.3893510063251790.3354843579938920.3958163401264160.3194365509578740.430683105883508 Ccdc800.3702894943083480.345748990012487 0.3285128834969540.3515334741401290.322504188402553 0.3795066886044560.3385924177874420.3295864392084150.339527419552383 0.3557923814511110.3135942457751870.355638594153465 Ost4 Mest 0.3388556264062050.3156624557691390.3451350682107720.3705296146788190.3122777354324480.3320006118369130.3400732308059320.3807792683260160.35026065676810.3440109300029130.4028703051456410.3819648593668610.4015972881169890.3471904665342230.395280426948069Cda0.3461796685714010.3944947342428750.3360099405342620.329526337447640.4005797420161310.3222295069901770.3702626491259360.3897157595918940.3726854852423260.4210400271713 0.3227530737539760.405564312248452 0.39597938599134 0.3438437706277440.3683700647816820.3545801464977350.3293109136199070.3665694913097410.342703530991970.352053853395110.343539310734946 0.3601121403499170.403420706127123 0.350299159182811 0.3532748431386690.3427079483289910.3257760849083780.318600079760439 Wisp2 Syn1 0.368718223779850.32013728118930.3374541831037230.330124307012842Larp4b0.3487881471962960.342860048830120.3737567282419570.362985169655660.3841890522687630.3898815232727190.3439196068005940.3388264684683530.3914275041824880.37085860781150.3891330556522230.315999825584370.3675670338375660.4101201975405570.3699772762997860.3328222210603040.3340397389422120.3499641985143040.3491629136144330.3495387923668620.3833599001198170.3182279586230190.3487028975019610.3929144814889480.336245918015030.366491272084820.3347147058734040.4050424003817460.4497475618233880.3789421222404190.349637248241419 0.3691732146748 0.3136975747729750.3753889419457880.336275917213927 0.3783520856426 0.3216450395037510.3298440600863790.36974626980743 0.3400237347117060.3532292137826610.311310203641488 Cbr20.328109826432109 0.328619907611787 0.32475146750233 0.339241163653193 0,5 0.3233307572337560.3785736440979220.3165365173533750.3498447342861080.3145724185137640.4049156280261440.328640285157460.3162305906102760.3821803637532990.3137356061283590.3729733974111750.3322535005263250.3448162270361360.3663369110118740.4277318911837540.343952927455450.3568004298099160.3779898207284040.328520007157240.3893718783526810.3124815238489670.4275626516428 0.3283682588935850.395208411150710.3699177367992660.3767652929424660.409796163178870.4005632562956610.372809717100590.3404669374649180.32157706644997 0.3915346440679720.3272120044305480.347092939476636 0.4482522474831930.4166982823361440.3561105688735230.351725431113567 0.376186998339798 0.3134027930207070.3651510559788310.312809691141497 0.428696269986122 0.3177577860778210.3759324282960490.3296949889503240.389996934925685 0.399473738007695 0.313556047790438 0.328991112775270.375137966153167 0.314887821302425 Tsc22d3 Ccl7 0.4027986903048930.3402867179159440.3647134569155590.3690698021345190.4105147556020190.3596142454447630.3689776783524140.3392796587109410.3705059556998710.3146439175508690.3965134684792080.3662094141304030.4242345653545180.3478346385379460.3323665973645260.3486740085813740.3553683638881420.4032371062535510.3176938151269240.3620248988139860.3700055620932130.3445316493072250.4050563877593670.3783551261318890.3122769719439730.4096406204956210.414902220542968 0.311179161655435 0.3595109399833850.403185117335027Syn10.3746431784164230.3243700558124530.3649543055357430.4202968045003410.3274737017524870.3263274171159190.3651174834599010.3177860567047540.3664621864167320.3473000717996060.3303555587104310.3634475905479510.3787744804860560.4014895275296270.3840787553776890.421995541671842 0.315253876884891 0.3825071191328840.361999995033474 0.3353526310825770.3349620382449110.356522520718311 0.346638105937302 0.3552709680302120.40919732421806 0.3253063249986310.388948340093359 0.330645053892339 0.351285404079601 0.334063605498862 Stac3 Capn6 0.4041470948781030.3328389587901540.3393445570558870.3444547488915380.4052785563080130.3306361804215850.3515652273455660.3527056487534640.3574135044493640.3500579132908320.3847671558096040.365712176077404Gpc10.355575660716070.3488094223470590.3564446951927150.3295108417500540.3406937126175130.344848474231090.3710790068601650.3221279474936430.3928284615105280.4072703287887460.3171317239660330.3725097424163420.3471664795317480.370532629554560.3987766698757680.375764751121143 0.3332768569346480.3394533980440020.3998223263636750.3994039226037850.3572833954265580.3341867780415220.3493758608829960.3584358967049990.3130490406376650.417007061972521 0.3750930347260430.39705508567463 0.3278741848291860.374216768298128 0.4017037944029730.4110736301758860.376224751675405 0.39652705813825 0.389396933240214 0.314267173870239Tnfrsf230.4021046705372740.3503187884266840.3886745232360970.347089676703027 0.3688663638849170.3807666363903390.3738152819837540.337562761633269 0.313146676145306 0.3310049113226030.330019050576473 Csgalnact1 0.369145096599322 0.319496150309497 0.365860771355371 0.31175483683512 Pdfgra Gja1 Sp8 0.3181474544116830.3727718977012370.411925802348182Tgm60.3883532916676530.3521449460088370.3128139822284550.3114015012326940.3994069459351040.3369756748000090.3390596950479910.3584634503923490.3718591526727950.3837090835025110.3157575211323820.3902672218594390.3557065584130270.357609215990640.366796018702560.317916991001950.3451103454948410.3902549183246540.3437141473055610.3154530949924050.320199354641090.3314258968278320.323872408787120.369987469739679Rpl39l0.4015378377592120.4344249810273490.3507358326429270.3729337340851590.3926657766946680.3529723359624150.4322251898703730.3409671654684770.425968085095193 0.4043626107503760.4359216846037530.4120557146306580.3661524935074560.397196281561388 0.3667922313315710.3157981790179510.3965801687403 0.369936930851047 0.3136419900659650.337509055485937 0.3258723925097160.314489914547049 0.387440505388660.41564609171033 0.3783299428753420.322580774000989 0.3506724679239170.3557679686018530.3512604689778890.345744367311195 0.3240141345041050.385265364702472 0.3341205303949710.3514363061876770.3587783548959530.3344951809887030.334569549592177 0.339692499225553 0.334911568626756 Lman1l 0.3266166326889670.3888869112059830.3199497222041170.3123833503406530.3313206019105720.3563367755632310.3830208449272930.361683539718430.3555270588430370.3666593781874480.3644973501631750.3237519297754380.4087130075165540.319596158222780.3758663075105280.3686602645433510.3394571239707230.3469934664925730.3496468713356470.4214348554872970.4344803997577760.3241961516703020.4029315494882850.3302089127027160.3740389825181190.4258553504977290.4009925251631350.3354217281347430.4380592492149580.4074957668806030.4023970700863970.3593844256796790.3828561316164750.3642128920279020.409804652962391 0.4230852362331790.378115297581941 0.3986406920366260.3437020458681080.4323626561338560.4236866818941410.3859953234027090.408899013797170.3649269416668650.340069519025529 0.373567285046517 0.3339068122351480.3732071218048890.4280176717705390.372174011831538 0.3724590737743640.4133410640921160.3913522714595990.3692091566098 0.3802342255844050.3245271130937590.394748177375815 0.343966928306017 0.3264689822334760.335716327624326 0.362824052955793 0.3776420906210330.343422414686859 0.35025843605555 0.367848222079548 0.36508205931372 0.324360445545896 Tor1aip2 Smcp 0.3378874720150670.3198931687847420.3130968601028770.3169317659482360.3416879353883710.3262575120601820.3364195501750440.3221341655152520.386106187327420.327504835425912Elavl40.3654349769160910.385372801146110.3798919369895250.3419280406067240.4214409287323010.4096526634739220.3434006059840360.3868913764408790.3618686232544160.3382903986483530.4137612306411710.3361604620852250.3990681942087060.3255735130021120.3167182558648590.3652443437552150.3193730382392950.39806035729210.3933646058370370.4214258231031260.4139781722311360.3552383409825870.3149560302304110.3464481194605270.3850713659378350.3449210824009340.3922905207491910.45096438996820.411789581723371 0.343136284967276 0.3724370576108050.4228559095258130.449460140855740.429033096689116 0.3981123226782170.378645350823465 0.390655116510114 0.322626145186519 0.3129934499248990.347570142277979 0.3936597996662 0.3257835701737180.351160486416623 0.3962293934910450.3664220675650330.3781699238746790.4172489375700890.436458952852667 0.379292148185579 Phlda20.370129209610387 0.3949050409081010.3493204645524540.362772207664391 0.367462620158343 0.318005467111480.316205469901315 0.3354455077381890.3716803532624220.32236424691966 0.372904939166147 0.3132206162582430.340634478973541 0.3194317860943060.315190261778678 0.322467996322006 Murc Fyb Marcks 0.3420244172996140.314476499895293Fam132a0.3442385507261860.4064330523499590.3656016257464050.4238543815651220.3725921007209560.3962257747046040.4035013946119960.3671614785026690.3630561849080070.3613858800433890.4266001728636920.3268448633446850.3539813506369520.3481715267253630.3184351318526480.3344044641333910.4171712665657770.3319456257959310.4004310351116560.3788403075422860.3149180591887220.3314220253815030.3668079472129780.3263341352796960.3471200376180130.3308735270640990.3738291724825090.4013461871447850.324404057136091 0.3893686664770.3805226182427310.4223329250893030.3474637523871960.336612591634880.4323172173240760.3942478559421380.397948095055683 0.3585344820017770.3474060483823340.4520665541035760.3187791994417140.429047009952705 0.412549345528160.3248311225386320.361670044640720.3778075153316840.3791993830779640.432876811578920.357652629152440.3724658503288120.3906195456268630.3328869043360740.423195126235638 0.371521472856821 0.426857943901377Chn1 0.3440477704607310.4069307426896770.4486561527416980.3842758178420910.3436325694383460.346986846005132 0.331459908437008 0.3376713487380340.3222397449710370.4087377042607570.357194037327503 0.3370651033227450.3557149780096430.320735148667695 0.339703197118249 0.3872767482158680.327062706431676 0.40588280798033 0.352195919299750.341422655825113 0.362900802491932 0.3713267415928980.351133943461032 0.349235012340816 0.356730038350234 0.311173010338966 Ebf3 Cdkn1c 0.3903836717240730.3216606864807730.4018478095080020.3721460717249030.3337080006389760.3434453446646370.3449983871957880.3186569092915760.3773704306856970.3507455847990250.381636055147850.3539611093292560.3846366003354110.3930497931774290.3353278058086190.3343367370929760.3615787124519430.3186261091323120.3958929933159530.368480753287470.4290433339259450.3397578762472740.3281620254317170.3784798453396090.4012295413746590.4301871360041290.3636644921672310.3469776704245010.4365428855710830.3344678913288460.427131031857170.3211086711786510.3800760944084940.3536236309412230.3445392377912120.3314399250191260.340682560975210.416770648042320.3669283713563050.4441182855122720.3247152158806240.3598444100537360.3307227185033010.3523753865497240.416940035057652Bcar30.4259345447259940.3494789861594390.428765727928393 0.3942598995836740.323423693859475 0.407611074007351 0.3328984567140110.382265227804950.369760355828641 0.3838750498966790.3400193286199280.3142025120985130.31381908039961 0.37315446266655 0.342281258120077 0.3943424988554230.3628404578346780.353045455473211 0.383423529025172 0.320275532597053 0.4104119028347350.345451534844858 0.3830707715022010.4210251047959840.365338472743119 0.3584799236855990.331737177535530.329219183432266 0.370299951316089 0.3117164232459080.3487459589758180.369149154203365 0.336326914737567 0.355430278697632 0.3554319828015730.327397804970252 0.311813237435553 Rps6ka6 0.352402031723829 0.324945888919105 0.324425391152251 Swt1 Kirrel3 0.3888440960175230.3406494669887710.4000576703454170.3166030916317410.3539098146085750.3500646534352810.3507345116030940.3233743172126480.3904761267497590.3572503856287720.363847383521070.369113848203530.4335940248281820.3273808189332350.3909949919457950.3826649247093790.4287199131339820.4117092883993730.4204149521874220.3935387179883970.4108676702273560.3712981074693860.3755091618586830.4017245243858540.3262999638719430.4211656275689170.4240095943252190.389156531440488 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0.3161526520210760.3534967503594990.3202457742613410.385604839919240.3865265550122010.3173672361646940.3669089996923640.379382495937542Ppp1r3f0.3317611695281440.3971404412953090.3287067907942130.3877318232661590.4250706672175220.3137527778378290.340802643663457030.3246403995696470.323668016341750.4351510008320040.4149333060584640.3858822931950520.323124752925220.4352177514575380.354310453865883Arc0.373973842366275 0.4097730871765890.3466187126280950.4259570715757730.4163027089193450.4314436008664280.3341189803562820.3183187800921330.3394074896986330.4040728878084450.3224204190664530.3485752886469380.3182287817308190.3844621160506350.4391388954273040.4044909804573210.3406832774338690.337204779561899 0.333218425449831 0.4069308233961060.438931322754253 0.413296090957548 0.3200149960853690.384258005007569 0.4288900818199890.3838694971631490.3744202527198190.419371277253487 0.3772221976111290.3717207994826520.395949875640948 0.406555988429218 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0.3475915292642220.3146160285006090.3882525271694990.3800737229009150.360938744700660.3575304682709710.338732313453790.4026008120616970.4048398339226050.3795939584439320.3179999629109010.4232739793633220.3173047568697650.3125498246706170.3813274833893650.3391889992788650.3748656855254570.3497338775939670.4014240301585680.3828703635505220.3544563481297170.3583949027798340.3892476359435540.396442420379954Trpv40.340690878533080.3158832572390950.3807337109373870.3462845768314970.3421009119608940.3812873051856980.333187618898599 0.368011790774339 0.3864536280232460.391155076953690.411683531898356 0.4034908742664670.3658138573938280.3642213434546170.3824024581401490.3392643000684730.4200132096007440.3516409475998340.357595632502672 0.36231711630190.394074221358995 0.3297341822986190.313530319416081 Immp2l 0.338022156302921 Cd200 0.3407388227209550.3285798751284830.3465422393521570.4004492186726030.4118828797184940.3675453338268010.3366308489459920.382320121645910.3776327381310830.4075405534010940.3434121981994210.3442605533335460.3517892177423690.4216949232857750.4079135220978590.3304972230605870.3690831273346660.3751787982600310.3509546908819470.3613073173360060.3999476318385690.3115461294923440.370518624545991 0.3375951986993040.3578969634277010.4385864397486890.4351300396638170.3708424705229310.380073053791380.388004758364944 0.3525470346622060.430050252110646 0.338471837607922 0.3148550618976640.326384584435260.3555911654423520.347356346644671 0.314384522349234 0.341449370720882 0.325265141595698 0.3516570681801820.3173307421939340.3261835886184520.3110000494790290.3979644942121020.3141213586881610.3688213413700790.324522930710920.3747273665413890.3312601301515970.3988727218303470.3406014193854910.3362527392234580.3364969534775950.4368422891167670.3826110281502610.3142646370170710.3635941393845890.385074921215220.317894679847006 0.3923702460847460.3171738984545120.3884922640577250.3740454647249210.3536744401955380.332221494325873Btg10.4057854066328210.3204484619565310.321495440555441 0.3666813907263320.3889104780307920.331569278354457 0.331934231099286 0.33323162659904 Cdkn1c Trim440.3519670569124660.3750046079283860.3338600731129630.3570552764614710.3693504259745340.3529182051906660.3763582089156180.3386066892202330.4125118651963320.318687331183116Asz10.4254117538128440.3265201515114740.3491424878507030.349184437013787 0.3205064421045950.4300729743558260.37608896047940.382695755414587 0.3140181796123760.381208758570753 0.3340897083950550.368416054684643 0.3246349798739720.31141056535137 Tnfrsf23 0.3411125102416120.3395370527750980.3226826340817310.3780560822857670.3780113950447670.3686409125734310.338973184413050.3477998164181980.4166560844919020.3528428531907420.3772898251088120.3967703412444970.3623134540169310.4113350338227320.416155948324235 0.3774310727881750.3554812876809280.3267856274622030.395688462855189 0.3673376476055690.3794941974579910.3380087623830820.3543127947380650.3300901979593990.3145772254514090.32286312668031 0.3205240591330860.394846447628203 0.3469242052624150.31892532752514 0.317987065346814 Mgarp 0.3412558726933630.332259434458212Chst10 0.4062533858221780.3421210800499660.3176455775227050.3323463944880570.3137426506439840.331506282976380.4169259620197750.3200924489459270.399944744743548 0.37066298394446 0.3217690678174650.3148189003455850.3293193912428080.341104568140175 0.322934205063047 Reep5 Fam83h 0.3177161216083250.3140701554341160.3635983119034340.319275624509750.3244318026334930.3443195789641840.4240953650392180.3482118794806420.3536937106920430.3717755782717210.3704561843575770.336939377801507 0.3489293939577050.3145402429725520.353446695877639 0.361942087453470.3363459723286120.348814530154765 0.328695907078266 Plekhg4 0.3265811329159230.316950942584019Cdkn2b0.3407464290795940.315292053381290.324885996315973Sec23a0.3526690405738970.4043441015635910.3610592228033690.391435287332154 0.333916554575704Speg0.325462695262371 0.3280294705433280.3554451579680550.31394538299158 0.3998583035503830.379336070886090.3241256581494170.3607711658360080.3614176594317330.326604185135901 Sox5 0.3306918019751930.3228421468989270.3169534368526610.3154420982663080.3453679158387040.3205636606422280.338055770906834 Itpk1 Haus7 0.3270476196230340.3241347493635480.351127032129806 Hoxc8 Acsbg1 Alpl 0.3275306285342294932438A13Rik0.3435657390528540.313797657111595Vps13b Cd207 Ogt Isca2 Srl Pdia5 2610524H06Rik Nfatc1 Rbm47 Slc39a8 Zfp69 Pcnx Unc5c Ankrd2 Ankrd42 Rps6ka6 Transcription Trpv4 Scarb1 Soluble mediator

Phlda2 Cell receptor 0,7 0,9 ECM Connectivity Other

Supplemental Figure 4 (related to Figure 3): The supportive capacity is implemented in four BM niche cell populations A: Hierarchical clustering corresponding to O, V and MSC samples. Euclidian distances and Ward's linkage were used. B: Choice of the power ß for the weighted adjacencies. Left panel: scale free index fit (R2) vs. ß power; ß=6 is chosen as the value where saturation is reached and above 0,8. Right panel: connectivity distribution according to ß power. C: Verification of the scale-free topology index for the chosen ß power value. Model fit for a power law (black) and an exponentially truncated power law (red). D: Module trait relationships. Pearson's correlation coefficient and corresponding p-value are given for each of the identified modules. E: Significant KEGG pathways corresponding to genes in V (red) and MSC-Vp (blue) populations. F: Connectivity plots of genes belonging the O module. Right panel: Intra-modular connectivity bar plot of the Top-30 genes. Left panel: Cytoscape connectivity plot of the 115 most connected genes; genes were selected for representation above a threshold weight ≥ 0,3110. G: Gene connectivity vs. PAM score scatter plot for the V and MSC-Vp modules. The genes belonging to the V module are shown as stars.

Transparent Methods

Bioinformatics Data sets: The different samples used in this study are described in Supplemental Table 1. The retrieved transcriptomes were studied using Affymetrix microarrays (different platforms: Mouse 430 2.0, Mouse Gene 1.0 ST, Mouse Gene 2.0 ST, Mouse Exon 1.0 ST) or Illumina RNAseq. The data sets were merged in a single matrix. A normalization step was necessary to get rid of the covariate series (the different experiments), using the Partek 'Remove batch effect' (RBE) algorithm. Using the matrix consisting in the 224 observations (samples) and the 15056 variables (entire transcriptome after datasets merging) the results of this RBE are shown on three QC parameters: the mean value and dispersion of the expression level of all genes in a sample (A), the distribution of the gene expression levels (B), and the representation of the samples in the PCA space is shown on Supplemental Fig 1A. The RBE resulted in the alignment of mean value and dispersion of the expression level of all genes in the different samples, the normalization of the gene distributions, and the splitting of the different samples belonging to one dataset to the whole space.

PCA: It was performed on matrices indicated in the text using Partek and factominer R-package. P- value of group discrimination was evaluated by correlation on the first principal component axis (Le et al., 2008).

Discriminant analysis: To discriminate the best classifiers for the Sup-BM cluster, the learning machine algorithm 'Prediction Analysis for Microarrays' (PAMr) was applied using the 4 cell clusters as defined (Tibshirani et al., 2002). Positive score probes obtained by leave-one-out learning with cross validation for the Sup-BM cluster were retained and applied to a random forest algorithm (Breiman, 2001). Before starting the Random Forest analysis the dataset was split randomly in training set and validation set comprising 75% and 25% of the samples, respectively. The Random Forest “mtry” parameter was tuned with Caret R package on the training set in order to achieve the learning step with a minimum error of bagging. The Random Forest model was built on training, and supervised by implementing the Sup-BM cluster against all others pooled in one class. Subsequently, validation of the Random Forest model was performed with accurate prediction on the validation set. Rspo2 expression probe, found highly predictive for the Sup-BM cluster, was used as quantitative predictor in LIMMA algorithm (Ritchie et al., 2015) in order to find correlated gene expression profile. Conjointly, the supervised Significance Analysis for Microarray (SAM) algorithm (Tusher et al., 2001) was applied, comparing the Sup-BM vs. other clusters: the threshold was fixed for a positive value of fold change superior to 2 and a minimal false discovery rate of 0,05. To perform unsupervised classification with Euclidean distances we retained the genes in the intersection of two gene sets, one corresponding to genes whose expression was correlated to that of Rspo2, and the other corresponding to genes with positive fold change in Sup-BM cluster according to SAM. Microarray expression heat-plot was drawn with made4 R-package (Culhane et al., 2005). Statistical significance tested for each gene was performed with Fisher one-way ANOVA followed by post-Hoc Tukey test. The PAMr algorithm was also applied to identify the genes that were predominantly expressed in each population.

Weighted Gene Correlation Network Analysis (WGCNA): It was performed according to the algorithm developed by Horvath and co-workers (Horvath, 2011; Langfelder and Horvath, 2008), using different gene sets as indicated in the text. The chosen values for essential input parameters for each gene set were chosen as giving in each case the simplest module definition defined by clustering using the Topological Overlap Matrix (TOM) and merged dynamic tree cut method. They were: 1) the power ß obtained from the scale-free fit model curve (r2 > 0,8); 2) the correlation, in all cases Pearson's; 3) the adjacency, in all cases signed; 4) the linkage, average or complete; 5) the minimum module size of 30. For the Cytoscape connectivity plot edges were selected above a given weight (intra-modular topological overlap measure) threshold. Graph layout was always force-directed.

Gene Set Enrichment Analysis (GSEA): GSEA aims at identifying the significant genes within a gene expression set contrasting two phenotypes. The expression set is compared to pre-established datasets corresponding to divers biological process. Datasets are retained if the distribution of their genes is shifted toward either phenotype. The statistical relevance of the shift is established by random permutations of the phenotypes (Liberzon et al., 2015; Subramanian et al., 2005). In this work we used the Sup-BM gene set as reference dataset.

Gene Ontology (GO): Selected genes from the gene sets were processed using DAVID (database for annotation, visualization and integrated discovery) (Huang da et al., 2009a, b).

Multivariate information-based inductive causation (miic) analysis We used the miic algorithm which combines constraint-based and information-theoretic frameworks to reconstruct causal and non-causal networks from large scale datasets (Verny et al., 2017; Sella et al., 2018). Miic network predictions belong to the broad class of 'ancestral graphs' that include undirected, directed as well as bi-directed edges originating from latent common causes unobserved in the available dataset and represented by dashed edges in the networks of Figure 4C and Figure 4D. In brief, starting from a fully connected graph, miic iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths. This amounts to progressively uncover the best supported conditional independencies (i.e. I(X;Y/(Ai)) = 0 implying no XY link in the underlying network) by iteratively 'taking off' the most significant indirect contributions of positive conditional 3-point information, I(X;Y;Ak|k-1) > 0, from every 2-point (mutual) information, I(X;Y), as,

I(X;Y|{Ai}n) = I(X;Y) - I(X;Y;A1) - I(X;Y;A2|A1) - ... - I(X;Y;An|{Ai}n-1) Miic also provides an edge-specific confidence assessment of retained edges, which are oriented based on the signature of causality in observational data. This has long been known to be associated to 'v-structures', X → Z ← Y, between two mutually (or conditionally) independent variables, X and Y, connected to a third variable Z. Indeed, this entails the orientations of the v-structure as the edges XZ and YZ cannot be undirected, nor Z be a cause of X and Y, since these alternative graphical models would imply correlations in contradiction with the independence between X and Y.

Single cell analysis Single cell analyses were performed in R software environment version 3.5.3 with package Seurat_2.3.4 that uses dependencies of packages Matrix_1.2-17, cowplot_0.9.4 and ggplot2_3.1.1. Digital matrix was built with LEPR principal cluster from GSE108891 stromal data comprising 1712 cells. Data were scaled and normalized on Unique Molecular Identifiers and gene counts before performing dimensional reduction with t-SNE analysis on the first forty principal axes. Expression of selected markers was plotted on the dimension 1 vs. dimension 2 t-SNE map.

Hematopoietic cell isolation and culture Mice: Adult C57Bl/6 female mice (10-16 weeks old) were provided from Janvier Labs and maintained in the animal facility of the Laboratory of Developmental Biology according to institutional guidelines (experiment approved by the 'Charles. Darwin' ethical committee of the Sorbonne University). Femurs and tibias were flushed with DMEM (Gibco) supplemented with 10% FCS (Eurobio).

Cell sorting: BM lineage-negative cells were first isolated by depletion of hematopoietic lineage marker (Ter119, Gr1, B220, CD4, CD8, CD11b)-expressing cells using MACS columns (Miltenyi Biotec). Cells were then stained with PE-conjugated anti-Sca-1 and APC-conjugated anti-CD117 (c- kit). LSK cells were isolated using a FACS AriaIII (BD) cell sorter.

Colony-forming units: 250 LSK sorted cells were plated in 1 mL methyl-cellulose in IMDM (Gibco) supplemented with 30% FCS (Eurobio), 1% crystallized BSA (Sigma), and 10-4M ß-mercaptoethanol (Sigma), in the presence of 20 ng/mL SCF, 10 ng/mL IL-3, 2 U/mL EPO and 20 ng/mL TPO (all from PromoKine). Tested RSPO2 (R&D) concentrations and/or 100 ng/mL WNT3A (Peprotech) were added. Samples were plated in duplicate 35-mm Petri dishes and incubated for 7 days at 37°C.

Culture of LSK cells in liquid medium: 200 LSK sorted cells were plated in U-bottom 96 well-plates in 100 µL X-Vivo 15 medium (Lonza) supplemented with 10% FCS, 5.10-5 M ß-mercaptoethanol (Sigma) and 10-4 M Methyl-ß-cyclodextrin (Sigma), in the presence of 20 ng/mL SCF, 20 ng/mL RSPO2and/or 100 ng/mL WNT3A. Five wells were seeded per condition and incubated for 12 days at 37°C, cells in each well were then counted using a Mallassez hemocytometer. qRT-PCR: RNA from 40000 LSK sorted cells or incubated for 24h in conditions described above was isolated using RNeasy Micro Plus Kit (Qiagen). Reverse transcription was performed using iScript cDNA synthesis kit (Biorad). Relative quantitative PCR was performed using the SsoAdvanced Universal SYBR Green Supermix (Biorad) with gene-specific PCR primers on a PikoReal instrument (Thermo Scientific). After one step at 9°C for 3 minutes the samples were cycled 50 times (denaturation at 95°C for 25 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 20 seconds). Cq values were measured using the PikoReal software. Expression level as 2-∆Cq, ∆Cq = Cq(gene of interest) - Cq(housekeeping gene), housekeeping gene being Gapdh. The following primer sequences (5' to 3') were used: Gapdh fw : ATGGTGAAGGTCGGTGTGAA Gapdh rv : AATGAAGGGGTCGTTGATGG Axin2 fw: TGACTCTCCTTCCAGATCCCA Axin2 rv: TGCCCACACTAGGCTGACA Bmp4 fw: TTCCTGGTAACCGAATGCTGA Bmp4 rv: CCTGAATCTCGGCGACTTTTT Lgr6 fw: GAGGACGGCATCATGCTGTC Lgr6 rv: GCTCCGTGAGGTTGTTCATACT Tcf7 fw: AGCTTTCTCCACTCTACGAACA Tcf7 rv: AATCCAGAGAGATCGGGGGTC

Immunohistochemistry Femurs were incubated for 5h in formol before incubation in sucrose 30% at 4°C overnight. Bones embedded in OCT were cryosectioned on Superfrost+ slides and immediately fixed in acetone for

5min at -20°C. After drying, section were washed in distilled water (dH2O) then antigen retrieval was performed by incubating sections in boiling 10 mM citrate buffer pH6 (Dako). After cooling down for 30 min at room temperature (RT), sections were washed in PBS then in PBS-0.1%Tween and blocked for 1h at RT in PBS-5%. Sections were then incubated with primary antibody (rabbit anti-RSPO2 (1/100, NBP2-38688 Novus); goat anti-LEPR (1/40, AF497 R&D systems); biotinylated Isolectin-b4 (1/200, B-1205 Vector)) in a humidified chamber overnight at 4°C. After 3 washes in PBS, slides were incubated with the anti-goat AF555 (1/500, Thermofisher A21432) for 30 min at RT then washed 3 times before adding the anti-rabbit AF488 (1/500, Jackson 111-545-144) and the streptavidin 647 (1/1000, Thermofisher S32357) for 30 min at RT. After washing, slides were incubated for 10min with

DAPI (1/5000, Sigma), washed in dH2O and mounted in Fluoromount (Thermofisher). Images were acquired using LSM800 confocal microscope (Zeiss) and processed with Fiji (version 1.47 v; National Institutes of Health, USA, https://imagej-nih-gov.gate2.inist.fr/ij/).

Statistical analysis For experiments comparing one condition to another (e.g. LSK cells cultured in presence of Rspo2 + SCF vs. cultures with SCF only) results were analyzed using paired t-tests and expressed as means ± SEM.

Supplemental References Culhane, A.C., Thioulouse, J., Perriere, G., and Higgins, D.G. (2005). MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics 21, 2789-2790. Le, S., Josse, J., and Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software 25, 1-18.