As the next generation of spatial transcriptomics tools hits the market, researchers are uncovering previously unknown interactions that could transform clinical research.

A transcriptomic atlas of the endometrium of women with polycystic ovary syndrome uncovers both disease signatures and potential therapeutic targets.

Nature Protocols - CellPhoneDB combines a curated database of receptor–ligand interactions with methods to infer cell–cell communication from single-cell expression data. This protocol...

Tumor-reactive T cell receptors are predicted based on single-cell RNA sequencing and single-cell T cell receptor sequencing.

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironm

19-21 May 2025 To foster international participation, this course will be held online