A Curated Database Reveals Trends in Single-cell Transcriptomics
Overview
Affiliations
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.
Luo M, Cao Y, Hong J Physiol Mol Biol Plants. 2025; 31(2):199-209.
PMID: 40070535 PMC: 11890805. DOI: 10.1007/s12298-025-01558-6.
Biases in machine-learning models of human single-cell data.
Willem T, Shitov V, Luecken M, Kilbertus N, Bauer S, Piraud M Nat Cell Biol. 2025; 27(3):384-392.
PMID: 39972066 DOI: 10.1038/s41556-025-01619-8.
Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.
Stock M, Losert C, Zambon M, Popp N, Lubatti G, Hormanseder E Mol Syst Biol. 2025; 21(3):214-230.
PMID: 39939367 PMC: 11876610. DOI: 10.1038/s44320-025-00088-3.
Advances and applications in single-cell and spatial genomics.
Wang J, Ye F, Chai H, Jiang Y, Wang T, Ran X Sci China Life Sci. 2025; .
PMID: 39792333 DOI: 10.1007/s11427-024-2770-x.
De Simone M, Hoover J, Lau J, Bennett H, Wu B, Chen C Nucleic Acids Res. 2024; 53(2.
PMID: 39675380 PMC: 11754665. DOI: 10.1093/nar/gkae1186.