SpotClean adjusts for spot swapping in spatial transcriptomics data
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE178221 | GEO | 2022/04/21
REPOSITORIES: GEO
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Project description:SpotClean adjusts for spot swapping in spatial transcriptomics data
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Project description:Hexokinase 2 (Hxk2) of Saccharomyces cerevisiae is a dual function hexokinase, acting as a glycolytic enzyme and being involved in the transcriptional regulation of glucose-repressible genes. Relief from glucose repression is accompanied by phosphorylation of Hxk2 at serine 15, which has been attributed to the protein kinase Tda1. To explore the role of Tda1 beyond Hxk2 phosphorylation, the proteomic consequences of TDA1 deficiency were investigated by difference gel electrophoresis (2D-DIGE) comparing a wild type and a Δtda1 deletion mutant. To additionally address possible consequences of glucose repression/derepression, both were grown at 2 % and 0.1 % (w/v) glucose. A total of eight protein spots exhibiting a minimum twofold enhanced or reduced fluorescence upon TDA1 deficiency was detected and identified by mass spectrometry. Among the spot identities are – besides the expected Hxk2 – two proteoforms of hexokinase 1 (Hxk1). Targeted proteomics analyses in conjunction with 2D-DIGE demonstrated that TDA1 is indispensable for Hxk2 and Hxk1 phosphorylation at serine 15. Thirty-six glucose-concentration-dependent protein spots were identified. A simple method to improve spot quantification, approximating spots as rotationally symmetric solids, is presented along with new data on the quantities of Hxk1 and Hxk2 and their serine 15 phosphorylated forms at high and low glucose growth conditions. The Δtda1 deletion mutant exhibited no altered growth under high or low glucose conditions or on alternative carbon sources. Also, invertase activity, serving as a reporter for glucose derepression, was not significantly altered. Instead, an involvement of Tda1 in oxidative stress response is suggested.
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Project description:Visualization of gene expression in lung tissue was performed using Visium spatial gene expression kits (10x Genomics) following the manufacturer`s protocol. The four capture areas in a 10x Genomics Visium Gene Expression slide consist of 5000 spots with DNA oligos for mRNA capture that have a unique spatial barcode and a unique Molecular Identifier (UMI). Each spot has 55 µm diameter and can therefore capture mRNA from 1 to 10 cells. We report the spatially resolved transcriptome of 3 control lung samples from non-COVID-19-related pneumonia donors and 9 COVID-19 lung samples analyzed with the 10x Visium platform.
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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).
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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 | 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:Single cell sequencing studies have characterized the transcriptomic signature of cell types within the kidney. However, the spatial distribution of acute kidney injury (AKI) is regional and affects cells heterogeneously. We first optimized coordination of spatial transcriptomics and single nuclear sequencing datasets, mapping 30 dominant cell types to a human nephrectomy. The predicted cell type spots corresponded with the underlying histopathology. To study the implications of AKI on transcript expression, we then characterized the spatial transcriptomic signature of two murine AKI models: ischemia reperfusion injury (IRI) and cecal ligation puncture (CLP). Localized regions of reduced overall expression were associated with injury pathways. Using single cell sequencing, we deconvoluted the signature of each spatial transcriptomic spot, identifying patterns of colocalization between immune and epithelial cells. Neutrophils infiltrated the renal medulla in the ischemia model. Atf3 was identified as a chemotactic factor in S3 proximal tubules. In the CLP model, infiltrating macrophages dominated the outer cortical signature and Mdk was identified as a corresponding chemotactic factor. The regional distribution of these immune cells was validated with multiplexed CO-Detection by inDEXing (CODEX) immunofluorescence. Spatial transcriptomic sequencing complements single cell sequencing by uncovering mechanisms driving immune cell infiltration and detection of relevant cell subpopulations.
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Project description:The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay format for high-throughput detection of many parallel targetsin medical and biological research. Though commonly applied, microarray technology still suffers limitations arising from problems of data robustness and reproducibility across platforms, stemming in part from an incomplete understanding of the complex processes governing surface hybridization behavior. It has been observed that there are non-random spatial variations within individual microarray hybridizations, but the causative mechanisms of positional bias remain largely unexplained. This study identifies a symptomatic spatial bias in surface hybridization signal intensitywith systematically increased signal intensities of spots located at the boundaries of the spotted areas of the microarray slide and characterizes the underlying mechanistic principle of this bias using a simplified block array format. Experimentally-derived hybridization dynamics are compared with a mathematical modeling analysis, which together showthat the driver of the spatial bias is a position-dependent variation in lateral diffusion. Numerical simulations employing a diffusion-based model are used to demonstrate the strong influence of microarray well geometry on this spatial bias and to determine optimal conditions for which this bias can be minimized or eliminated, resulting in increased uniformity of microarray hybridization. A simplified square array of 900 identical spotted probes was hybridized using different target concentrations and different time periods to analyze systematically spatial biases in microarray hybridizations.
2010-12-22 | E-GEOD-26275 | biostudies-arrayexpress