Dataset Information

Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

ABSTRACT: Understanding lung immunity requires an unbiased profiling of tissue-resident T cells at their precise anatomical locations within the lung, but such information has not been characterized in the immunized mouse model. In this pilot study, using 10x Genomics Chromium and Visium platform, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using a well-established Klebsiella pneumoniae infection model. We built an optimized deconvolution pipeline to accurately decipher specific cell-type compositions by anatomic location. We discovered that combining scATAC-seq and scRNA-seq data may provide more robust cell-type identification, especially for lineage-specific T helper cells. Combining all three modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines. In summary, our proof-of-principle study demonstrated the power and potential of single-cell multi-omics analysis to uncover spatial- and cell-type-dependent mechanisms of lung immunity.

SUBMITTER: Xu Z

PROVIDER: S-EPMC9418911 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

Xu Zhongli Z Wang Xinjun X Fan Li L Wang Fujing F Lin Becky B Wang Jiebiao J Trevejo-Nuñez Giraldina G Chen Wei W Chen Kong K

iScience 20220809 9

Understanding lung immunity requires an unbiased profiling of tissue-resident T cells at their precise anatomical locations within the lung, but such information has not been characterized in the immunized mouse model. In this pilot study, using 10x Genomics Chromium and Visium platform, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using a well-established <i>Klebsiella pneumoniae</i> in ...[more]

PMID: 36039299

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Project description:Despite intensive studies during the last 3 years, the pathology and underlying molecular mechanism of coronavirus disease 2019 (COVID-19) remain poorly defined. In this study, we investigated the spatial single-cell molecular and cellular features of postmortem COVID-19 lung tissues using in situ sequencing (ISS). We detected 10 414 863 transcripts of 221 genes in whole-slide tissues and segmented them into 1 719 459 cells that were mapped to 18 major parenchymal and immune cell types, all of which were infected by SARS-CoV-2. Compared with the non-COVID-19 control, COVID-19 lungs exhibited reduced alveolar cells (ACs) and increased innate and adaptive immune cells. We also identified 19 differentially expressed genes in both infected and uninfected cells across the tissues, which reflected the altered cellular compositions. Spatial analysis of local infection rates revealed regions with high infection rates that were correlated with high cell densities (HIHD). The HIHD regions expressed high levels of SARS-CoV-2 entry-related factors including ACE2, FURIN, TMPRSS2 and NRP1, and co-localized with organizing pneumonia (OP) and lymphocytic and immune infiltration, which exhibited increased ACs and fibroblasts but decreased vascular endothelial cells and epithelial cells, mirroring the tissue damage and wound healing processes. Sparse nonnegative matrix factorization (SNMF) analysis of niche features identified seven signatures that captured structure and immune niches in COVID-19 tissues. Trajectory inference based on immune niche signatures defined two pathological routes. Trajectory A primarily progressed with increased NK cells and granulocytes, likely reflecting the complication of microbial infections. Trajectory B was marked by increased HIHD and OP, possibly accounting for the increased immune infiltration. The OP regions were marked by high numbers of fibroblasts expressing extremely high levels of COL1A1 and COL1A2. Examination of single-cell RNA-seq data (scRNA-seq) from COVID-19 lung tissues and idiopathic pulmonary fibrosis (IPF) identified similar cell populations consisting mainly of myofibroblasts. Immunofluorescence staining revealed the activation of IL6-STAT3 and TGF-β-SMAD2/3 pathways in these cells, likely mediating the upregulation of COL1A1 and COL1A2 and excessive fibrosis in the lung tissues. Together, this study provides a spatial single-cell atlas of cellular and molecular signatures of fatal COVID-19 lungs, which reveals the complex spatial cellular heterogeneity, organization, and interactions that characterized the COVID-19 lung pathology.

Project description:Monitoring global gene expression provides insight into how genes and regulatory signals work together to guide embryo development. The fields of developmental biology and teratology are now confronted with the need for automated access to a reference library of gene-expression signatures that benchmark programmed (genetic) and adaptive (environmental) regulation of the embryonic transcriptome. Such a library must be constructed from highly-distributed microarray data. Birth Defects Systems Manager (BDSM), an open access knowledge management system, provides custom software to mine public microarray data focused on developmental health and disease. The present study describes tools for seamless data integration in the BDSM library (MetaSample, MetaChip, CIAeasy) using the QueryBDSM module. A field test of the prototype was run using published microarray data series derived from a variety of laboratories, experiments, microarray platforms, organ systems, and developmental stages. The datasets focused on several developing systems in the mouse embryo, including preimplantation stages, heart and nerve development, testis and ovary development, and craniofacial development. Using BDSM data integration tools, a gene-expression signature for 346 genes was resolved that accurately classified samples by organ system and developmental sequence. The module builds a potential for the BDSM approach to decipher a large number developmental processes through comparative bioinformatics analysis of embryological systems at-risk for specific defects, using multiple scenarios to define the range of probabilities leading from molecular phenotype to clinical phenotype. We conclude that an integrative analysis of global gene-expression of the developing embryo can form the foundation for constructing a reference library of signaling pathways and networks for normal and abnormal regulation of the embryonic transcriptome. These tools are available free of charge from the web-site http://systemsanalysis.louisville.edu requiring only a short registration process.

Dataset Information

Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

Publications

Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

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