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:Like hematopoiesis, erythropoiesis requires erythroid niches nurturing and support. We performed spatial transcriptomics to reveal the erythroid niches in human. Our results identified novel erythroid niches in human during development and stress.
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:Like hematopoiesis, erythropoiesis requires erythroid niches nurturing and support. We performed subcellular spatial transcriptomics to reveal the erythroid niches in human. Our results identified novel erythroid niches in human during development and stress.
Project description:Like hematopoiesis, erythropoiesis requires erythroid niches nurturing and support. We performed spatial transcriptomics to reveal the erythroid niches in mice. Our results identified novel erythroblastic island macrophage marker in mouse during development and stress.
Project description:Like hematopoiesis, erythropoiesis requires erythroid niches nurturing and support. We performed subcellular spatial transcriptomics to reveal the erythroid niches in mice. Our results identified novel erythroblastic island macrophage marker in mouse during development and stress.
Project description:<p>Spatial multi-omics inference of diabetes mellitus triggering pancreatic cancer growth through cholesterol-induced neutrophil extracellular traps</p>