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A curated database reveals trends in single-cell transcriptomics.

ABSTRACT: The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. Database URL: www.nxn.se/single-cell-studies/gui.

SUBMITTER: Svensson V

PROVIDER: S-EPMC7698659 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A curated database reveals trends in single-cell transcriptomics.

Svensson Valentine V da Veiga Beltrame Eduardo E Pachter Lior L

Database : the journal of biological databases and curation 20201101

The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhau ...[more]

PMID: 33247933

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Project description:The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

Dataset Information

A curated database reveals trends in single-cell transcriptomics.

Publications

A curated database reveals trends in single-cell transcriptomics.

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