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

Baccin, Chiara, Al-Sabah, Jude, Velten, Lars, Helbling, Patrick M., Grünschläger, Florian, Hernández-Malmierca, Pablo, Nombela-Arrieta, César, Steinmetz, Lars M., Trumpp, Andreas, Haas, Simon·2020·Nature Cell Biology
DOI 10.1038/s41556-019-0439-6PMC PMC7610809

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Figure 1 Identification of BM resident cell types by scRNA-seq. a, Overview of the FACS sorting strategy. In total, 5 consecutive single-cell RNA sequencing runs were performed. Right panel: t-SNE projection of all cells with respective experiment colour-coded. b, t-SNE projection of all cells with clusters colour-coded. Abbreviations used: T: T cells, NK: Natural killer cells, s. pre-B: small pre-B cells, l. pre-B: large pre-B cells, DC: Dendritic cells, Prog.: progenitor. * Lin-Kit+Sca1high HSCs reside at the interface of LMPPs and Megakaryocyte progenitors, see also figure S3g.
Figure 1 Identification of BM resident cell types by scRNA-seq. a, Overview of the FACS sorting strategy. In total, 5 consecutive single-cell RNA sequencing runs were performed. Right panel: t-SNE projection of all cells with respective experiment colour-coded. b, t-SNE projection of all cells with clusters colour-coded. Abbreviations used: T: T cells, NK: Natural killer cells, s. pre-B: small pre-B cells, l. pre-B: large pre-B cells, DC: Dendritic cells, Prog.: progenitor. * Lin-Kit+Sca1high HSCs reside at the interface of LMPPs and Megakaryocyte progenitors, see also figure S3g.CC BY-NC · PubMed Central
Figure 2 Characterization of BM resident cell types. a, Gene expression levels of Sca1, Endomucin (Emcn), Nestin, and Ng2, with relevant populations colour-coded. MAGIC47 was used for imputation of drop-out values. b, Deep imaging of a BM section immunostained with antibodies against Sca-1 and Emcn. Scale bar = 20 μm. c, Enrichment of gene expression signatures of Ng2+ MSCs, Adipo-CAR and Osteo-CAR cells in previously published transcriptomes of relevant genetically labelled populations5,7–9. See Figure S4b for further populations, and the supplementary note for a detailed evaluation of the algorithm used (CIBERSORT). Error bars indicate the standard error of the mean across n=3 bulk transcriptome samples per class. d, Boxplots of the scaled expression level of selected genes in single cells from the Adipo-CAR, Osteo-CAR and Osteoblast populations. For the right panel, the mean expression of all genes annotated with the gene ontology term ‘ossification’ was computed for each cell. e, Projection of all mesenchymal cell types using PHATE48 with time derivatives of gene expression state, as determined by RNA velocity22, highlighted as arrows. f, Comparison of cell type frequencies between distinct cell isolation methods used for scRNAseq.
Figure 2 Characterization of BM resident cell types. a, Gene expression levels of Sca1, Endomucin (Emcn), Nestin, and Ng2, with relevant populations colour-coded. MAGIC47 was used for imputation of drop-out values. b, Deep imaging of a BM section immunostained with antibodies against Sca-1 and Emcn. Scale bar = 20 μm. c, Enrichment of gene expression signatures of Ng2+ MSCs, Adipo-CAR and Osteo-CAR cells in previously published transcriptomes of relevant genetically labelled populations5,7–9. See Figure S4b for further populations, and the supplementary note for a detailed evaluation of the algorithm used (CIBERSORT). Error bars indicate the standard error of the mean across n=3 bulk transcriptome samples per class. d, Boxplots of the scaled expression level of selected genes in single cells from the Adipo-CAR, Osteo-CAR and Osteoblast populations. For the right panel, the mean expression of all genes annotated with the gene ontology term ‘ossification’ was computed for each cell. e, Projection of all mesenchymal cell types using PHATE48 with time derivatives of gene expression state, as determined by RNA velocity22, highlighted as arrows. f, Comparison of cell type frequencies between distinct cell isolation methods used for scRNAseq.CC BY-NC · PubMed Central
Figure 3 Spatial allocation of BM resident cell types by integrated single-cell and spatial transcriptomics. a, Scheme of experimental design. 12 μm bone sections were stained for arterioles (Sca1) and sinusoids (Emcn), respectively. Areas of approximately 14.500 μm2 surrounding arteries (dark yellow box), sinusoids (yellow box) and the endosteum (blue box), as well as areas with no vessels (grey box) and sub-endosteal areas (dotted blue box) were collected by laser capture dissection and subjected to RNA-seq. A confocal image is shown for illustrative purposes. For images acquired under the laser capture dissection microscope and selected areas see Figure S7b. b, Expression of osteoblast-, sinusoid- and arteriole-specific genes in scRNA-seq data (10x) and spatial transcriptomics from different niches (LCM: LCM-seq data). c, Enrichment of population marker genes (Table S1) among genes with differential expression between niches (Table S2). d, Schematic outline of the computational data analysis strategy used. e,f, Estimated abundance of different cell types in microscopically distinct niches. Error bars indicate standard error of the mean of the estimate across n=11 to n=28 samples per class.
Figure 3 Spatial allocation of BM resident cell types by integrated single-cell and spatial transcriptomics. a, Scheme of experimental design. 12 μm bone sections were stained for arterioles (Sca1) and sinusoids (Emcn), respectively. Areas of approximately 14.500 μm2 surrounding arteries (dark yellow box), sinusoids (yellow box) and the endosteum (blue box), as well as areas with no vessels (grey box) and sub-endosteal areas (dotted blue box) were collected by laser capture dissection and subjected to RNA-seq. A confocal image is shown for illustrative purposes. For images acquired under the laser capture dissection microscope and selected areas see Figure S7b. b, Expression of osteoblast-, sinusoid- and arteriole-specific genes in scRNA-seq data (10x) and spatial transcriptomics from different niches (LCM: LCM-seq data). c, Enrichment of population marker genes (Table S1) among genes with differential expression between niches (Table S2). d, Schematic outline of the computational data analysis strategy used. e,f, Estimated abundance of different cell types in microscopically distinct niches. Error bars indicate standard error of the mean of the estimate across n=11 to n=28 samples per class.CC BY-NC · PubMed Central
Figure 4 Localization of key mesenchymal cell types by immunofluorescence. a, Left panel: Single-cell gene expression levels of Cxcl12 and Alpl with relevant cluster identity colour-coded. MAGIC47 was used to impute drop-out values. Right panel: Sample high-resolution ROI from a whole-mount image of a femur from a Cxcl12-GFP mouse, stained for Alpl and the sinusoidal marker Emcn. An Alpl+Cxcl12-GFP+ cell distant from sinusoids is highlighted by an arrowhead. b, Quantitative analysis of a full whole-mount image, see also Figure S9a. Left panel: Sample ROI, scale bar: 50μm. Central panel: 3D segmentation of the same ROI. Cxcl12-GFP+ cells were classified as Osteo- or Adipo-CAR cells based on the Alpl signal. Right panel: Quantitative assessment of Alpl+ Osteo-CAR cells (red) and Alplneg Adipo-CAR cells (green) between sinusoidal and non-sinusoidal niches in central BM. c, Whole-mount imaging 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. Scale bars in ROIs: 10μm. See figure S9b for a second whole-mount image. d, Left panel: Single-cell gene expression levels of SM22 (Tagln) and Pdpn with relevant cluster identity colour-coded. Central panel: Immunofluorescence staining of a BM arteriole stained for SM22, Pdpn and CD31. Right panel: Immunofluorescence staining of a BM arteriole stained for Pdpn, Sca1 and Pdgfra. Scale bar: 20 μm. e, Left panel: Single-cell gene expression levels of Col1a1 and Pdpn with relevant cluster identity colour-coded. Right panel: Immunofluorescence staining of Col1a1, Pdpn and DAPI at the endosteal surface. Scale bar: 20 μm. f, Immunofluorescence staining of Pdpn at the endoestum, cortical bone and periosteum.
Figure 4 Localization of key mesenchymal cell types by immunofluorescence. a, Left panel: Single-cell gene expression levels of Cxcl12 and Alpl with relevant cluster identity colour-coded. MAGIC47 was used to impute drop-out values. Right panel: Sample high-resolution ROI from a whole-mount image of a femur from a Cxcl12-GFP mouse, stained for Alpl and the sinusoidal marker Emcn. An Alpl+Cxcl12-GFP+ cell distant from sinusoids is highlighted by an arrowhead. b, Quantitative analysis of a full whole-mount image, see also Figure S9a. Left panel: Sample ROI, scale bar: 50μm. Central panel: 3D segmentation of the same ROI. Cxcl12-GFP+ cells were classified as Osteo- or Adipo-CAR cells based on the Alpl signal. Right panel: Quantitative assessment of Alpl+ Osteo-CAR cells (red) and Alplneg Adipo-CAR cells (green) between sinusoidal and non-sinusoidal niches in central BM. c, Whole-mount imaging 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. Scale bars in ROIs: 10μm. See figure S9b for a second whole-mount image. d, Left panel: Single-cell gene expression levels of SM22 (Tagln) and Pdpn with relevant cluster identity colour-coded. Central panel: Immunofluorescence staining of a BM arteriole stained for SM22, Pdpn and CD31. Right panel: Immunofluorescence staining of a BM arteriole stained for Pdpn, Sca1 and Pdgfra. Scale bar: 20 μm. e, Left panel: Single-cell gene expression levels of Col1a1 and Pdpn with relevant cluster identity colour-coded. Right panel: Immunofluorescence staining of Col1a1, Pdpn and DAPI at the endosteal surface. Scale bar: 20 μm. f, Immunofluorescence staining of Pdpn at the endoestum, cortical bone and periosteum.CC BY-NC · PubMed Central
Figure 5 Inference of cellular interactions from single-cell gene expression data by RNA-Magnet. a, t-SNE highlighting the cell type each single cell is most likely to physically interact with (RNA-Magnet: Location, indicated by colour), and the estimated strength of adhesion (RNA-Magnet: Adhesiveness, indicated by opacity). b, Scatter plot comparing the estimated strength of adhesion (RNA-Magnet score) to the degree to which each cell type is differentially localised between niches (spatial transcriptomics, see also Figure 3c). c, Left panel: Heatmap depicting a summary of inferred localisation based on RNA-Magnet. Fraction of cells assigned to a certain niche is colour-coded. Right panel: Bar chart indicating the correlation between the RNA-Magnet estimate of localization and the LCM-seq estimate of localization.
Figure 5 Inference of cellular interactions from single-cell gene expression data by RNA-Magnet. a, t-SNE highlighting the cell type each single cell is most likely to physically interact with (RNA-Magnet: Location, indicated by colour), and the estimated strength of adhesion (RNA-Magnet: Adhesiveness, indicated by opacity). b, Scatter plot comparing the estimated strength of adhesion (RNA-Magnet score) to the degree to which each cell type is differentially localised between niches (spatial transcriptomics, see also Figure 3c). c, Left panel: Heatmap depicting a summary of inferred localisation based on RNA-Magnet. Fraction of cells assigned to a certain niche is colour-coded. Right panel: Bar chart indicating the correlation between the RNA-Magnet estimate of localization and the LCM-seq estimate of localization.CC BY-NC · PubMed Central
Figure 6 Cellular and spatial sources of key cytokines in the bone marrow. a, Contribution of cell types to distinct cytokine pools. Mean gene expression across all cells constituting each cell type is compared. b, Single-cell gene expression levels of Cd200 and Itgb3 (CD61) from 10x genomics data in CD45negLinnegCD71negVcam1+ cells. MAGIC47 was used to impute drop-out values. Relevant populations are colour-coded. c, Surface marker levels of CD200 and CD61 from indexed scRNAseq data in CD45negLinnegCD71negVcam1+ cells. FACS index values for n=91 cells subjected to indexed scRNAseq (see methods). The colour indicates the most similar cell type from the main data set as identified by scmap49. d, Intra-cellular FACS analyses of Cxcl12 expression in total BM, lineage-negative BM, Lin-CD31+ endothelial cells and CD61/CD200 subpopulations of CD45negLinnegCD71negCD51+Vcam1+ CAR cells. Statistics were performed using an unpaired t test. ***: p < 0.001, **: p<0.01*,: p<0.05 e, Quantification of the number of growth factors (GFs) and cytokines expressed by each cell type, and the fraction of total mRNA devoted to producing growth factors and cytokines. For a list of growth factors and cytokines used, see Table S3. f, Relative expression of cytokines and growth factors in Adipo-CAR cells and Fibroblasts. g, Expression of Cxcl12, Kitl, and summed expression of all cytokines and growth factors in arteriolar, sinusoidal and non-vascular niches from spatial transcriptomics data. P-values for differential expression relative to non-vascular niches are from limma/voom50 (Cxcl12, Kitl) or a Wilcoxon ranksum test (sum); ***: p < 0.001, *: p<0.05. h, Expression of cytokines, chemokines and growth factors in the different niches measured by LCM-seq. Only factors with significant differences between niches are included.
Figure 6 Cellular and spatial sources of key cytokines in the bone marrow. a, Contribution of cell types to distinct cytokine pools. Mean gene expression across all cells constituting each cell type is compared. b, Single-cell gene expression levels of Cd200 and Itgb3 (CD61) from 10x genomics data in CD45negLinnegCD71negVcam1+ cells. MAGIC47 was used to impute drop-out values. Relevant populations are colour-coded. c, Surface marker levels of CD200 and CD61 from indexed scRNAseq data in CD45negLinnegCD71negVcam1+ cells. FACS index values for n=91 cells subjected to indexed scRNAseq (see methods). The colour indicates the most similar cell type from the main data set as identified by scmap49. d, Intra-cellular FACS analyses of Cxcl12 expression in total BM, lineage-negative BM, Lin-CD31+ endothelial cells and CD61/CD200 subpopulations of CD45negLinnegCD71negCD51+Vcam1+ CAR cells. Statistics were performed using an unpaired t test. ***: p < 0.001, **: p<0.01*,: p<0.05 e, Quantification of the number of growth factors (GFs) and cytokines expressed by each cell type, and the fraction of total mRNA devoted to producing growth factors and cytokines. For a list of growth factors and cytokines used, see Table S3. f, Relative expression of cytokines and growth factors in Adipo-CAR cells and Fibroblasts. g, Expression of Cxcl12, Kitl, and summed expression of all cytokines and growth factors in arteriolar, sinusoidal and non-vascular niches from spatial transcriptomics data. P-values for differential expression relative to non-vascular niches are from limma/voom50 (Cxcl12, Kitl) or a Wilcoxon ranksum test (sum); ***: p < 0.001, *: p<0.05. h, Expression of cytokines, chemokines and growth factors in the different niches measured by LCM-seq. Only factors with significant differences between niches are included.CC BY-NC · PubMed Central
Figure 7 Systems-level analysis of signaling potential in the BM. a, Inference of signaling interactions between cell types by RNA-Magnet. If a cell type is enriched in expression of ligands for receptors expressed by a second cell type, a line is drawn between these cell types, with colour indicating the ligand-producing cell type and line width indicating the strength of enrichment. For details, see methods and figure S9e for a fully labelled version of the figure. b, Summary of niche composition, as estimated from LCM-seq (see also figure 3f). c, Inference of signalling interactions between niches and cell types. Line width indicates the strength of enrichment for expression of ligand-receptors pairs. Cell types are arranged as in subfigure b.
Figure 7 Systems-level analysis of signaling potential in the BM. a, Inference of signaling interactions between cell types by RNA-Magnet. If a cell type is enriched in expression of ligands for receptors expressed by a second cell type, a line is drawn between these cell types, with colour indicating the ligand-producing cell type and line width indicating the strength of enrichment. For details, see methods and figure S9e for a fully labelled version of the figure. b, Summary of niche composition, as estimated from LCM-seq (see also figure 3f). c, Inference of signalling interactions between niches and cell types. Line width indicates the strength of enrichment for expression of ligand-receptors pairs. Cell types are arranged as in subfigure b.CC BY-NC · PubMed Central

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Research works printed but not linked: 44

  1. Ramasamy, S. K. et al. Regulation of hematopoiesis and osteogenesis by blood vessel-derived signals. Annu. Rev. Cell Dev. Biol. 32, 649–675 (2016). · DOI
  2. Wei, Q. & Frenette, P. S. Niches for hematopoietic stem cells and their progeny. Immunity 48, 632–648 (2018). · DOI
  3. Sugiyama, T., Kohara, H., Noda, M. & Nagasawa, T. Maintenance of the hematopoietic stem cell pool by CXCL12–CXCR4 chemokine signaling in bone marrow stromal cell niches. Immunity 25, 977–988 (2006). · DOI
  4. Ding, L., Saunders, T. L., Enikolopov, G. & Morrison, S. J. Endothelial and perivascular cells maintain haematopoietic stem cells. Nature 481, 457–462 (2012). · DOI
  5. Ding, L. & Morrison, S. J. Haematopoietic stem cells and early lymphoid progenitors occupy distinct bone marrow niches. Nature 495, 231–235 (2013). · DOI
  6. Kunisaki, Y. et al. Arteriolar niches maintain haematopoietic stem cell quiescence. Nature 502, 637–643 (2013). · DOI
  7. Asada, N. et al. Differential cytokine contributions of perivascular haematopoietic stem cell niches. Nat. Cell Biol. 19, 214–223 (2017). · DOI
  8. Greenbaum, A. et al. CXCL12 in early mesenchymal progenitors is required for haematopoietic stem-cell maintenance. Nature 495, 227–230 (2013). · DOI
  9. Méndez-Ferrer, S. et al. Mesenchymal and haematopoietic stem cells form a unique bone marrow niche. Nature 466, 829–834 (2010). · DOI
  10. Gomariz, A. et al. Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy. Nat. Commun. 9, 2532 (2018). · DOI
  11. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015). · DOI · Paper page
  12. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016). · DOI · Paper page
  13. Zhu, L., Lei, J., Klei, L., Devlin, B. & Roeder, K. Semisoft clustering of single-cell data. Proc. Natl Acad. Sci. USA 116, 466–471 (2018). · DOI
  14. Boulais, P. E. et al. The majority of CD45– Ter119– CD31– bone marrow cell fraction is of hematopoietic origin and contains erythroid and lymphoid progenitors. Immunity 49, 627–639 (2018). · DOI
  15. Coutu, D. L., Kokkaliaris, K. D., Kunz, L. & Schroeder, T. Three-dimensional map of nonhematopoietic bone and bone-marrow cells and molecules. Nat. Biotechnol. 35, 1202–1210 (2017). · DOI
  16. Omatsu, Y. et al. The essential functions of adipo-osteogenic progenitors as the hematopoietic stem and progenitor cell niche. Immunity 33, 387–399 (2010). · DOI
  17. Zhou, B. O. et al. Bone marrow adipocytes promote the regeneration of stem cells and haematopoiesis by secreting SCF. Nat. Cell Biol. 19, 891–903 (2017). · DOI
  18. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). · DOI · Paper page
  19. Karamitros, D. et al. Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells. Nat. Immunol. 19, 85–97 (2018). · DOI
  20. Baryawno, N. et al. A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia. Cell 177, 1915–1932 (2019). · DOI
  21. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). · DOI · Paper page
  22. Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017). · DOI
  23. Silberstein, L. et al. Proximity-based differential single-cell analysis of the niche to identify stem/progenitor cell regulators. Cell Stem Cell 19, 530–543 (2016). · DOI
  24. Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 (LCM-seq) for robust and efficient transcriptomic profiling of mouse and human cells. Nat. Commun. 7, 12139 (2016). · DOI
  25. Tamura, S. et al. Podoplanin-positive periarteriolar stromal cells promote megakaryocyte growth and proplatelet formation in mice by CLEC-2. Blood 127, 1701–1710 (2016). · DOI
  26. Gumbiner, B. M. Cell adhesion: the molecular basis of tissue architecture and morphogenesis. Cell 84, 345–357 (1996). · DOI · Paper page
  27. Potente, M. & Mäkinen, T. Vascular heterogeneity and specialization in development and disease. Nat. Rev. Mol. Cell Biol. 18, 477–494 (2017). · DOI · Paper page
  28. Xu, C. et al. Stem cell factor is selectively secreted by arterial endothelial cells in bone marrow. Nat. Commun. 9, 2449 (2018). · DOI
  29. Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017). · DOI · Paper page
  30. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044 (2018). · DOI
  31. Siddiqui, J. A. & Partridge, N. C. Physiological bone remodeling: systemic regulation and growth factor involvement. Physiology 31, 233–245 (2016). · DOI
  32. Pinho, S. et al. Lineage-biased hematopoietic stem cells are regulated by distinct niches. Dev. Cell 44, 634–641.e4 (2018). · DOI
  33. Itkin, T. et al. Distinct bone marrow blood vessels differentially regulate haematopoiesis. Nature 532, 323–328 (2016). · DOI
  34. Cordeiro Gomes, A. et al. Hematopoietic stem cell niches produce lineage-instructive signals to control multipotent progenitor differentiation. Immunity 45, 1219–1231 (2016). · DOI
  35. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716-729 (2018). · DOI · Paper page
  36. Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018). · DOI
  37. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). · DOI · Paper page
  38. Hennig, B. P. et al. Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol. G3 8, 79–89 (2018). · DOI
  39. Nichterwitz, S., Benitez, J. A., Hoogstraaten, R., Deng, Q. & Hedlund, E. In RNA Detection. Methods in Molecular Biology, vol 1649 (ed Gaspar, I.) (Humana Press, New York, NY, 2018).
  40. Thomsen, E. R. et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nat. Methods 13, 87–93 (2016). · DOI
  41. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019). · DOI · Paper page
  42. Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. in Cancer Systems Biology, vol. 1711 (ed. von Stechow, L.) 243–259 (Humana Press, New York, 2018). · DOI
  43. Shay, T. & Kang, J. Immunological genome project and systems immunology. Trends Immunol. 34, 602–609 (2013). · DOI
  44. Worthley, D. L. et al. Gremlin 1 identifies a skeletal stem cell with bone, cartilage, and reticular stromal potential. Cell 160, 269–284 (2015). · DOI

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Metadata

Authors
Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela-Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S.
Venue
Nature Cell Biology
Publisher
Springer Science and Business Media LLC
Published
2019-12-23
Volume
22
Issue
1
Pages
38-48
ISSN
  • 1465-7392
  • 1476-4679
DOI
10.1038/s41556-019-0439-6
PMC
PMC7610809
Cited by
904
References
17
Language
en
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