Researchers at the University of California, San Francisco (UCSF) have developed an algorithm that can determine how cellular neighborhoods function in tissues. The algorithm, called "TissueMapper," uses spatial transcriptomics data to infer the interactions between cells and their neighbors. This information can be used to understand how tissues are organized and how they respond to different stimuli.

Spatial transcriptomics is a new technology that allows researchers to measure the gene expression of cells in a tissue sample while preserving their spatial relationships. This data can be used to create a map of the tissue, showing which genes are expressed in each cell and how they are organized relative to each other.

TissueMapper uses this spatial information to infer the interactions between cells and their neighbors. The algorithm first identifies clusters of cells that are expressing similar genes. These clusters are then used to create a network of interactions, showing how the cells are connected to each other.

The researchers tested TissueMapper on several different tissue samples, including skin, brain, and heart. The algorithm was able to accurately identify the interactions between cells in each tissue and to predict how the tissues would respond to different stimuli.

"TissueMapper is a powerful new tool that can be used to understand how tissues are organized and how they function," said Alex Pollen, PhD, a UCSF researcher and one of the developers of TissueMapper. "This information could be used to develop new drugs and therapies that target specific cell types or interactions."

The study was published in the journal Nature Methods.