Project description:The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data.
Project description:A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
Project description:Cell fate transition is a spatiotemporal process, however, previous work has largely neglected the spatial dimension. Incorporating space and time into models of cell fate transition would be a key step toward characterizing how interactions among neighboring cells, local niche factors, and cell migration contribute to tissue development. Here, we developed topological velocity inference (TopoVelo), a computational tool to infer spatial and temporal dynamics of cell fate transition from spatial transcriptomic data. We show that TopoVelo significantly improves the accuracy and spatial coherence of inferred cell ordering compared to previous methods. TopoVelo also reveals spatial cell state dependencies of ligand-receptor genes, spatial signatures of mouse neural tubes, and patterns of early differentiation in 3D cell culture.
Project description:<p>Spatial multi-omics inference of diabetes mellitus triggering pancreatic cancer growth through cholesterol-induced neutrophil extracellular traps</p>
Project description:Cellular function is strongly dependent on surrounding cells and environmental factors. Current technologies are limited in characterizing the spatial location and unique gene-programs of cells in less structured and dynamic niches. Here we developed a method (NICHE-seq) that combines photoactivatable fluorescent reporters, two-photon microscopy and single-cell RNA-seq to infer the cellular and molecular composition of niches. We applied NICHE-seq to examine the high-order assembly of immune cell networks. NICHE-seq is highly reproducible in spatial tissue reconstruction, enabling identification of rare niche-specific immune subpopulations and unique gene-programs, including natural killer cells within infected B cell follicles and distinct myeloid states in the marginal zone. This study establishes NICHE-seq as a broadly applicable method for elucidating high-order spatial organization of cell types and their molecular pathways.
Project description:Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related mortality. CRC can be classified into DNA mismatch repair proficient (MMRp) and deficient (MMRd) subtypes, with only ~50% of MMRd tumours responding to immunotherapy. Tumour architecture is spatially heterogeneous, ranging from the necrotic core to the invasive front, accompanied by diverse stromal and immune responses that influence tumour progression and treatment outcomes. To explore the spatial organisation of the tumour-immune microenvironment, we profiled the expression of ~1,000 genes in 846,469 cells from 23 tumour and normal tissue samples using the CosMx Spatial Molecular Imager (SMI). Using a custom bioinformatic pipeline, we performed quality control, cell type annotation, identified 9 spatial niches and provide an interactive web application to explore this data. Spatial niches included normal colonic crypts, tumour masses, neutrophil-rich regions, and lymphoid aggregates. Differential gene expression analysis revealed niche-specific changes to chemokine signalling, T cell infiltration and myeloid cell polarisation could either support or inhibit tumours depending on the niche. To expand the analysis, we integrated public single-cell RNA-Seq datasets, identifying increased tumour stemness in samples with higher neutrophil content that appeared to be driven by inflammation-mediated stromal reprogramming. Additionally, samples enriched in lymphoid aggregates, as defined by a spatial lymphoid aggregate signature, displayed heightened interferon signalling and an increased abundance of proliferating B cells. Finally, we identify gene expression patterns that distinguish tumour cells from normal epithelial cells within tumours. Our analysis reveals that many of the most pronounced changes in tumour gene expression originate from alterations in the tumour microenvironment, while also revealing tumour-intrinsic gene expression. By studying the spatial niches of CRC, we reveal niche-specific dynamics that drive tumours through innate stromal signalling and suggest both tumour-specific and stromally-mediated potential therapeutic targets.