Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …

No cell lives in a vacuum, and the molecular interactions between cells define most
phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and …

The growing availability of single-cell data, especially transcriptomics, has sparked an
increased interest in the inference of cell-cell communication. Many computational tools …

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing
datasets provide unprecedented opportunities to dissect cell–cell communication (CCC) …

Cell–cell communication is a fundamental process that shapes biological tissue. Historically,
studies of cell–cell communication have been feasible for one or two cell types and a few …

The inference of cell–cell communication (CCC) is crucial for a better understanding of
complex cellular dynamics and regulatory mechanisms in biological systems. However …

Molecular knowledge of biological processes is a cornerstone in omics data analysis.
Applied to single‐cell data, such analyses provide mechanistic insights into individual cells …

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells.
Communication between these cells and their microenvironments induces cancer …

Gene activity defines cell identity, drives intercellular communication, and underlies the
functioning of multicellular organisms. We present the single-cell resolution atlas of gene …

Cell interactions determine phenotypes, and intercellular communication is shaped by
cellular contexts such as disease state, organismal life stage, and tissue microenvironment …