Researchers at Tongji University, Shanghai, have developed a large-scale curated database that integrates tumour microenvironment (TME) single-cell transcriptomic data, known as TISCH – Tumour Immune Single Cell Hub.
In the UK, 1 in 2 people will develop cancer in their lifetime. While immunotherapies have revolutionised cancer treatment, their benefits are limited to a small fraction of individuals. This is due to the complexity and heterogeneity of the TME.
Single-cell RNA sequencing (scRNA-seq) has provided unprecedented resolution to decipher the heterogeneity within the TME. It has enabled identification of novel cell types and clinical implications. Nonetheless, the ongoing accumulation of tumour scRNA-seq data has posed great computational challenges for data integration and reuse.
In a preprint article, researchers present TISCH, a comprehensive and curated web resource. TISCH aims to determine the complex components of the TME at single-cell resolution. Moreover, it builds a scRNA-seq atlas of 76 high-quality tumour datasets across 28 cancer types (collected from Gene Expression Omnibus and ArrayExpress).
- Contains a total of 2,045,746 cells from 79 datasets involving 28 cancer types, with 378,392 malignant cells and 1,667,354 non-malignant cells
- There are 76 tumour-related datasets, including 17 tumour datasets with immunotherapy treatment (12 human and 5 mouse)
- The database includes three PBMC datasets from healthy donors to provide baseline expression levels for immune cells
- Covered 68,287 genes for human datasets and 18,789 genes for mouse datasets
Utility of TISCH:
- Presents analysis results, including clustering, differential gene identification, cell-type annotations and gene set enrichment analysis in a user-friendly interface for public use
- Provides two modules for users to visualise datasets:
- The dataset module – supports detailed exploration of individual datasets and multiple gene expression visualisations across multiple datasets at the single-cell level
- The gene module – allows single gene visualisation across multiple different scRNA-seq datasets at the cell-type level
TISCH provides a user-friendly interface for scientists to explore and visualise gene expression in the TME across multiple datasets at both single-cell and annotated cluster levels. The authors anticipate that TISCH will be valuable for scientists to study gene regulation and immune signalling in the TME. Hopefully, this will help identify potential novel drug targets. The team aim to continue maintaining and updating TISCH with new datasets and new functions such as inferring cell-cell interactions. They hope that this web resource will be of great utility to the cancer research community.
Image credit: Image by mcmurryjulie from Pixabay