Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging

Single-cell multi-omics technologies have empowered increasingly refined characterisation of the heterogeneity of cell populations. Automated cell type annotation methods have been developed to transfer cell type labels from well-annotated reference datasets to emerging query datasets. However, these methods suffer from some common caveats, including the failure to characterise transitional and novel cell states, sensitivity to batch effects and under-utilisation of phenotypic information other than cell types (e.g. sample source and disease conditions).We developed Φ-Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. In Φ-Space we adopt a highly versatile modelling strategy to continuously characterise query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualisations, clustering and cell type labelling.We demonstrate through three case studies that Φ-Space (i) characterises developing and out-of-reference cell states; (ii) is robust against batch effects in both reference and query; (iii) adapts to annotation tasks involving multiple omics types; (iv) overcomes technical differences between reference and query.The versatility of Φ-Space makes it applicable to a wide range analytical tasks beyond cell type transfer, and its ability to model complex phenotypic variation will facilitate biological discoveries from different omics types.

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Bone Marrow Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

Sudupe,

Muiños-Lopez,

Lopez-Perez

et al. 2024

Preprint

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Understanding the spatial organization of the bone marrow (BM) microenvironment at single-cell resolution constitutes a challenge in multiple myeloma (MM). Using formalin-fixed paraffin-embedded (FFPE) samples from the MIcγ1 mouse model of MM and aged-matched healthy YFPcγ1 mice, we performed spatial transcriptional profiling with Visium Spatial Gene Expression. A custom data-analysis framework that combines spatial with single-cell transcriptomic profiling defined the BM cellular composition and established specific cell relations, visualizing the spatial distribution of transcriptionally heterogeneous MM plasma cells (MM-PC). MM pathogenesis transcriptional programs were spatially delineated within the BM microenvironment. A high-to-low MM-PC density gradient spatially correlated with effector-to-exhausted T cell phenotype abundance. In this context, MM cells in high-density MM-PC areas coexisted with dendritic cells while displaced neutrophils to the tumor border. Increased neutrophil extracellular trap formation, IL-17-driven inflammatory signaling, and osteoclast differentiation were spatially delineated within the BM microenvironment. The spatial identification of different areas of BM and the interaction between malignant cells and their microenvironment were validated in FFPE BM biopsies from MM patients with varying degrees of MM-PC infiltration. In summary, spatial transcriptomics depicts the BM cellular architecture of MM and reveals deregulated mechanisms underlying MM intercellular communication.

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Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging

Cited by 4 publications

References 69 publications

Reconsidering the usual suspects in age-related hematologic disorders: is stem cell dysfunction a root cause of aging?

Reconsidering the usual suspects in age-related hematologic disorders: is stem cell dysfunction a root cause of aging?

Φ-Space: Continuous phenotyping of single-cell multi-omics data

Bone Marrow Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

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