Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization


The bone marrow constitutes the primary site for life-long blood production and skeletal regeneration. However, its cellular and spatial organization remains controversial. Here, we combine single-cell and spatially resolved transcriptomics to systematically map the molecular, cellular and spatial composition of distinct bone marrow niches. This allowed us to transcriptionally profile all major bone-marrow-resident cell types, determine their localization and clarify sources of pro-haematopoietic factors. Our data demonstrate that Cxcl12-abundant-reticular (CAR) cell subsets (Adipo-CAR and Osteo-CAR) differentially localize to sinusoidal and arteriolar surfaces, act locally as ‘professional cytokine-secreting cells’ and thereby establish peri-vascular micro-niches. Importantly, the three-dimensional bone-marrow organization can be accurately inferred from single-cell transcriptome data using the RNA-Magnet algorithm described here. Together, our study reveals the cellular and spatial organization of bone marrow niches and offers a systematic approach to dissect the complex organization of whole organs.

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Fig. 1: Identification of BM-resident cell types by scRNAseq.
Fig. 2: Characterization of BM-resident cell types.
Fig. 3: Spatial allocation of BM-resident cell types by integrated single-cell and spatial transcriptomics.
Fig. 4: Localization of CAR-cell subtypes determined using immunofluorescence.
Fig. 5: Localization of additional mesenchymal cell types determined using immunofluorescence.
Fig. 6: Inference of cellular interactions from single-cell gene expression data by RNA-Magnet.
Fig. 7: Cellular and spatial sources of key cytokines in the BM.
Fig. 8: Systems-level analysis of signalling potential in the BM.

Data availability

The LCM-seq and scRNAseq that support the findings of this study have been deposited in the GEO under accession code GSE122467. Data are further available in processed form for download and interactive browsing at Previously published sequencing data that were reanalysed here are available under the accession codes GSE89811, GSE48764, GSE109125, GSE33158, GSE43613 and GSE57729 (bulk transcriptome studies reanalysed in Fig. 2c and Extended Data Fig. 4a,b), or GSE128423 and GSE108892 (single-cell transcriptome studies reanalysed in Extended Data Fig. 5). Source Data for Figs. 16 and 8 and Extended Data Figs. 15, 7 and 10 are available online. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Our implementation of RNA-Magnet and CIBERSORT, as well as vignettes for re-creating key analysis steps are available at


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We would like to thank M. Milsom, A. Grozhik, J. Velten, I. Lohmann, B. Velten and the members of the Steinmetz, Haas and Trumpp labs for their helpful discussions and critical proofreading of the manuscript. The Cxcl12–GFP mice were originally from T. Nagasawa. We thank K. Bauer and J. Mallm from the DKFZ Single-cell open lab, D. Krunic from the DKFZ microscopy core, M. Paulsen from the EMBL flow cytometry core facility, M. Eich from the DKFZ flow cytometry, the EMBL genomics core facility, S. Terjung from the EMBL Advanced Light Microscopy Facility (ALMF), the Carl Zeiss AG, J. Schnell, L. Becker, S. Renders, P. Werner and S. Sood for technical support. This work was supported by the SFB873, FOR2674 and FOR2033 funded by the Deutsche Forschungsgemeinschaft (DFG), the SyTASC consortium (Deutsche Krebshilfe) and the Dietmar Hopp Foundation (all to A.T.); the US National Institutes of Health (grant no. P01HG00020527) and the ERC (grant no. 742804) to L.M.S; and the José Carreras Foundation for Leukemia Research (grant no. DCJLS 20R/2017 to L.V., A.T., L.M.S. and S.H.).

Author information

C.B. and J.A.-S. developed the experimental methods and performed the majority of the experiments, with conceptual input from S.H., L.V., A.T. and L.M.S. L.V. analysed the data with conceptual input from S.H. and the other authors. L.V., C.B. and L.M.S. developed RNA-Magnet. S.H. and L.V. conceived the study, supervised the experimental work and wrote the manuscript, with contributions from C.B., J.A.-S., A.T. and L.M.S. F.G. and P.H.-M. provided experimental support for this work. P.M.H. and C.N.-A. performed the whole-mount imaging. All authors have carefully read the manuscript.

Correspondence to Lars Velten or Lars M. Steinmetz or Andreas Trumpp or Simon Haas.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Cellular composition of populations defined by flow cytometry.

a, Abundance of different gates as fraction of total BM. b, Quantification of cell type composition for each FACS gate shown in main Fig. 1a. Source data

Extended Data Fig. 2 Expression of marker genes for haematopoietic populations highlighted on t-SNE.

For full lists of marker genes, see Supplementary Table 1. n=7497 cells are shown. Source data

Extended Data Fig. 3 Expression of marker genes for non-haematopoietic populations highlighted on t-SNE.

For full lists of marker genes, see Supplementary Table 1. n=7497 cells are shown. Source data

Extended Data Fig. 4 Comparison of cell-type transcriptomes determined by scRNAseq to data from bulk populations described in literature.

a, Enrichment of gene expression signatures of haematopoietic populations in immune cell transcriptomes published by the ImmGen consortium (data source: GEO GSE109125)58. Data points in the same row correspond to experimental replicates. b, Enrichment of gene expression signatures of non-haematopoietic populations in published transcriptomes of populations defined by genetic markers5,7,8,9,59; see methods for specification of data sources, and see the Supplementary Note 1 for a detailed evaluation of the algorithm used. c, The optimal number of mesenchymal cell clusters was determined using the SOUP method15, a semi-soft clustering algorithm designed to distinguish between distinct cell types and transition states between cell types. Error bars indicate 90% confidence intervals across 10 independent SOUP runs, central values correspond to medians. d, Main cluster identity from SOUP highlighted on the t-SNE from Fig. 1b (mesenchymal cell types only, n=2223 cells). e, Comparison of clusters identified by Seurat (Fig. 1b) to clusters identified by SOUP (panel d) demonstrates strong overlap between both methods. Source data

Extended Data Fig. 5 Comparison of cell isolation methods and reference datasets.

a,b, Additional scRNAseq data was generated as described, except that BM was derived by flushing bones and subjected or not subjected to enzymatic digestion. Data was projected to two dimensions using t-SNE and cell type labels were assigned using the anchoring approach implemented in seurat353. N=2411 cells are shown. c,d, Single-cell RNA-seq data from a recent study of different genetically labelled populations from flushed BM26 was projected to two dimensions using t-SNE and cell type labels were assigned using the anchoring approach implemented in seurat353. N=6617 cells are shown. e, Comparison of cell type frequencies between various published datasets. For Tikhonova et al.26, n=6617 cells, Baryawno et al.25, n=25933 cells, Baccin et al., n=7497 cells. Source data

Extended Data Fig. 6 Technical properties of the LCM-seq dataset.

a, Boxplots comparing the number of genes observed per sample in different protocols. All samples were down-sampled to 1 million reads for comparison. For the dataset presented in main Fig. 3, the protocol relying on random priming was used. N represent numbers of samples or cells as indicated on the graph. Boxplot elements are defined in the Methods section (section on data visualization). Smart-Seq2 data is from ref. 21, bulk transcriptome data is from ref. 7. b, Representative images of samples collected for LCM-seq; scale bar: 100µm. c, Immunofluorescence staining of a BM arteriole stained for Col1a1, Pdpn and CD31. Scale bar: 20 µm. The experiment was repeated independently 4 times with similar results. d, Schwann cell markers were lowly expressed across all niches e, haematopoietic markers were highly expressed across all niches. In panel d and e, sample sizes are as follows: Arteriolar niches, n=28, endosteal niches, n=12, sinusoidal niches, n=14, non-vascular niches, n=11, sub-endosteal niche, n=11. Boxplot elements are defined in the Methods section (section on data visualization). Source data

Extended Data Fig. 7 Evaluation of the CIBERSORT algorithm, see also Supplementary Note 1.

a, Heatmap of population-specific marker genes used for the algorithm. b,c, Simulations to assess the ability of CIBERSORT to decompose individual samples; see Supplementary Note 1 for detail. n=15 sampling runs were performed. Boxplot elements are defined in the Methods section (section on data visualization). d, CIBERSORT estimates of cell type composition of total BM, compared to the cell type composition estimate from 10x genomics (see Extended Data Fig. 1b). Central values indicate means, error bars correspond to standard error of the mean of n=3 samples. e,f, Simulations to assess the ability of CIBERSORT to identify changes in population frequencies across multiple samples; see Supplementary Note 1 for detail. n=15 samples runs were performed. g, FACS was used to assemble N=8 different pools of B220+ B-cells, CD3+ T/NK-T cells and Gr1+SSChigh neutrophils. Each pool contained a total of 100 cells at predefined ratios of B cells, T cells and neutrophils. Pools were then fixed and processed using the LCM-seq protocol, and CIBERSORT was used to decompose their composition. Estimates for T and NK cells, as well as different B-cell subpopulations, were summed for the display. Pearson correlation coefficients are shown. h, Simulations to assess the ability of CIBERSORT to discriminate between similar cell types; see Supplementary Note 1 for detail. Red squares highlight pairs of similar cell types. Pearson correlation coefficients across N=100 simulated samples are shown. i, Stability of the CIBERSORT estimates from main Fig. 3e with regard to re-sampling of the marker gene lists used; see Supplementary Note 1 for detail. Source data

Extended Data Fig. 8 Whole-mount imaging and data analysis.

a, Whole-mount imaging data of a Cxcl12–GFP bone section stained for Alpl and Emcn was segmented in 3D using the imaris software. Large Alpl+ surfaces (red, corresponding to endosteum and arteries) were identified and any GFP+ spots with <20µm proximity to these structures were excluded from further analysis (yellow spots). The strong Alpl signal in these regions prevents a quantitative assessment. Remaining GFP+ spots were classified as within 15µm of sinusoidal vessels (purple dots), of away from sinusoidal vessels (cyan dots). GFP+ spots were further classified as Alpl+ (right panels, red spots) or Alpl- (right panels, green spots). Scale bar left panel: 200µm; right panels: 50µm. b, Additional whole-mount imaging data of a femur from a Cxcl12–GFP mouse, stained for Alpl and the arteriolar marker Sca1. Arrowheads point to Alpl+Cxcl12+ cells near, but not overlapping with, Sca1+ arteriolar endothelial cells. See also Fig. 4f. In ROI 3, asterisk correspond to GFP+Alpl+ protrusions on, but clearly distinct from, Sca1+ arteriolar endothelial cells. In ROI 4, various z-sections of a highly reticulate Cxcl12–GFP+Alpl+ cell are shown. Scale bars in ROIs: 20µm; scale bar in main panel: 300µm.

Extended Data Fig. 9 Analyses using RNA-Magnet, see also Supplementary Note 2.

a, Overview of the receptor–ligand list used. See methods for data sources, and Supplementary Table 3 for the complete list. b, Stability of the RNA-Magnet location estimate for different choices of the fuzzification parameters k and x0. For each parameter set, RNA-Magnet location estimates were summarized per cell type, and compared to the summarized location estimate displayed in Fig. 6c. The asterisk indicates the parameter set used in Fig. 6c. c, Choice of local neighbourhoods. As detailed in the methods section, RNA-Magnet works by identifying interactions specific to a single cell compared to similar cells. The figure displays the size of local neighbourhoods for four representative cells demarked by a black triangle. d, Detailed comparison of location estimates obtained from LCM-seq and RNA-Magnet. See also main Fig. 6c. Sample sizes are as follows: Arteriolar niches, n=28, endosteal niches, n=12, sinusoidal niches. Mean values and standard error of the mean are shown. e, Fully labelled display of the network from main Fig. 8a. f, Expression of selected cytokines and growth factors involved in bone remodelling.

Extended Data Fig. 10 Index-sorting analysis of LinVcam1+ cells.

a, Sorting scheme used. b, Expression of key marker genes confirm the cell type assignment obtained by scmap, cf. main Fig. 7c. Source data

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2. Supplementary notes on the computational methods used. Supplementary Note 1 describes computational and experimental validations of the CIBERSORT algorithm. Supplementary Note 2 describes the RNA-Magnet algorithm.

Reporting Summary

Supplementary Table

Supplemetary Table 1. Marker genes and gene ontology analysis. Supplementary Table 2. Differential expression between niches. Supplementary Table 3. List of receptor–ligand and receptor–receptor pairs used by RNA-Magnet. Supplementary Table 4. Antibodies used in this study. Supplementary Table 5. Antibody combinations used in whole-mount imaging.

Supplementary Data

Processed genomics data as R and Seurat objects. Single-cell RNA-seq data is stored as a Seurat v3 object and additionally contains all metadata, as well as clustering and dimensionality reduction results. LCM-seq data is stored as a data matrix and metadata is made available as a data frame. See Readme file for detailed description.

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Baccin, C., Al-Sabah, J., Velten, L. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat Cell Biol 22, 38–48 (2020).

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