Shen, Wenjun.; Hu, Yunfei.; Lei, Yuanfang.; Wong, Hau-San.; Liu, Cheng.; Wu, Si.; Zhou, Xin Maizie. (2026).听.听Nucleic Acids Research, 54(8).听
Measuring which cell types are present in a tissue sample is important for understanding how tissues are organized and how diseases develop, but this is difficult when the data come from mixed samples. The problem is even harder because different cell types contain different amounts of RNA, the molecule used to read gene activity, and because data collected from different platforms do not always line up well. To address this, the researchers developed CSsingle, a new method for 鈥渄econvolution,鈥� meaning it separates the mixed gene-expression signal from bulk or spatial transcriptomic data into the contributions from different cell types. CSsingle corrects for differences in cell size or RNA content, using either built-in spike-in controls or a computational estimate, and it is designed to work robustly across different data sources. Using single-cell reference data, the method estimates cell-type proportions more accurately than existing approaches. In tests on bulk data, it corrected known errors such as underestimating neutrophils in blood and tumor purity in breast cancer. When applied to spatial transcriptomics, which shows where genes are active within tissue sections, it helped map cell organization in the developing human pancreas and revealed distinct functional neighborhoods in colon cancer. Overall, CSsingle improves the analysis of complex tissues by accounting for cell-size differences and making results more reliable across platforms.
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