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Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections.

ABSTRACT: Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

SUBMITTER: Sounart H

PROVIDER: S-EPMC10588020 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections.

Sounart Hailey H Lázár Enikő E Masarapu Yuvarani Y Wu Jian J Várkonyi Tibor T Glasz Tibor T Kiss András A Borgström Erik E Hill Andrew A Rezene Sefanit S Gupta Soham S Jurek Aleksandra A Niesnerová Anezka A Druid Henrik H Bergmann Olaf O Giacomello Stefania S

Genome biology 20231019 1

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pa ...[more]

PMID: 37858234

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Project description:Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Dataset Information

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections.

Publications

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections.

Similar Datasets

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