Bio: Xianwen Ren focuses on developing algorithms to resolve immunological questions based on single-cell RNA sequencing and spatial transcriptomics. Now he develops a series of algorithms including CSOmap, Redeconve, RedeFISH, RedeHist, RedeVIZ, and RedeStiff for single-cell spatiotemporal reconstruction. Applications of these algorithms on cancers and infectious diseases were published in Cell, Cancer Cell, Cell Research, Gut, Blood, etc.

Abstract: A high-resolution immune map of human bodies across ages and sexes is pivotal to understanding the cellular mechanisms underlying human health and diseases but is still missing now. Here, we apply a single-cell deconvolution strategy and construct a high-resolution immune map of human body based on the big data of Tabula Sapiens and GTEx. The map reveals organ-specific distributions of >1,000 different cell types/states across all the major immune cell types, and thus enables in silico charting the body distribution of different immune cell types/states in a user-defined manner akin to cell sorting. As 17,382 healthy samples derived from 30 major tissue types are mapped, it is also feasible to systematically interrogation of the organ-specific impacts of sex and age to the different immune compositions. This comprehensive immune map provides an important foundation dataset and deepens our understanding of human immune system from a holistic perspective.