Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scRNA-Seq)
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ABSTRACT:
PROVIDER: PRJNA727005 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14408459_1.fastq.gz | Fastqsanger.gz | |||
| SRR14408459_2.fastq.gz | Fastqsanger.gz | |||
| SRR14408460_1.fastq.gz | Fastqsanger.gz | |||
| SRR14408460_2.fastq.gz | Fastqsanger.gz | |||
| SRR14408461_1.fastq.gz | Fastqsanger.gz |
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Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation
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Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (Spatial scRNA-Seq)
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Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scMARS-Seq)
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Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173776 | GEO
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173774 | GEO
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173773 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-05-05 | GSE173778 | GEO
Project description:Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER removes the systematic difference between the ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimations, SDePER imputes for cell type compositions and gene expression at enhanced resolution. We assessed the performance of SDePER and six existing methods using simulations and four real datasets. All results showed that SDePER achieved significantly more accurate and robust results than the existing methods suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.
2024-08-01 | GSE231385 | GEO
Project description:Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here, we applied single-cell Deep Visual Proteomics to perform spatially-resolved proteome analysis of individual cells in native tissue. We built a robust framework comprising strategic cell selection and continuous protein gradient mapping, allowing the investigation of larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analyzing hundreds of isolated hepatocytes from 18 individuals. Among the 2,500 proteins identified per cell, about half exhibited zonated expression patterns. Cross-species comparison with male mice revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of samples with disrupted liver architecture demonstrated widespread loss of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive and open-access resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.
2025-12-19 | PXD062231 | Pride
Project description:Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly predict the super-resolution cell spatial profiles from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk, which is validated on independent longitudinal cohorts. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.
2025-01-29 | GSE287979 | GEO