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Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [Xenium]

ABSTRACT: Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.

ORGANISM(S): Homo sapiens

PROVIDER: GSE264334 | GEO | 2024/04/23

REPOSITORIES: GEO

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Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics

Project description:Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.

2024-04-24 | GSE265819 | GEO

Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [stereo-seq]

2024-04-23 | GSE264321 | GEO

Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [human snRNA-seq]

2024-04-23 | GSE264393 | GEO

Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [Xenium]

Project description:Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [Xenium]

| PRJNA1101991 | ENA

Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [human snRNA-seq]

Project description:Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [human snRNA-seq]

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Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis [Xenium]

Project description:Pulmonary fibrosis (PF) is a chronic, progressive condition that represents the end-stage of many interstitial lung diseases (ILDs). Single-cell transcriptomic studies have revealed disease-emergent epithelial, fibroblast, and macrophage cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using image-based spatial transcriptomics to profile gene expression changes in-situ across 28 lung samples from control and PF lungs, we characterized the expression of 343 genes in over 1 million nuclei at subcellular resolution. Using both cell-based and cell-agnostic approaches, we observed a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Overlaying these computationally-defined niches with disease-associated histopathologic features, we identified novel patterns of dysregulation in alveoli informed by spatial context. We computationally segmented individual air spaces and using cell composition, we ordered airspaces from homeostatic to most dysregulated. Using this ordering we identified a series of stepwise molecular changes associated with progressive distal lung remodeling. Together, these results advance our understanding of the molecular programs underlying progressive PF.

2024-06-04 | GSE250346 | GEO

Open-ST: High-resolution spatial transcriptomics in 3D [xenium]

Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

2024-06-23 | GSE263498 | GEO

Transcriptomic landscape of the anterior thalamic nuclei of the mouse [Xenium]

Project description:Here, we examined diversity in the cell-type composition of the mouse anterior thalamic nuclei (ATN) using single-cell RNA-seq and single-cell spatial transcriptomics.

2024-02-17 | GSE255953 | GEO

Project description:Subcellular Level Spatial Transcriptomics with PHOTON

| PRJNA1152621 | ENA

5d0fc3d3-e1be-418d-83fe-e59cd943311a - samples

Project description:This dataset contains 55 Whole Genome Sequencing of the study titled Spatial transcriptomics reveal topological immune landscapes of Asian head and neck angiosarcoma.

| EGAD00001010140 | EGA

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