MIT's IRIS model decodes cell-signaling fingerprints across cell types
Researchers led by MIT assistant professor Pulin Li and graduate student Nicholas Hutchins report that each cell-signaling pathway leaves a distinctive gene-activity fingerprint that is consistent across cell types. Their neural-network model, IRIS, uses those fingerprints to estimate which signaling pathways were active at specific times in a cell’s development; it was trained on data from thousands of human embryonic stem cells exposed to combinations of six major pathways. Tested on mouse embryo cells during gastrulation, IRIS predicted pathway activation in cells destined for heart, gut, muscle and spinal cord tissue, and a predicted lung-development signal was confirmed in mouse experiments, according to the open-access study published in Nature Methods on Sept. 8.
The approach could let researchers reconstruct signaling histories across many cell types without mapping each pathway separately in every cell type, which the authors say could aid stem cell engineering and organoid work.
As an embryo develops from a small cluster of stem cells, those once “blank slate” cells begin to take on more specialized roles like brain, liver, or muscle cells, and organize themselves into three-dimensional structures such as tissues and organs.
The fate of each cell — what type of specialized cell it will become — depends on which genes are turned on or off in the cell. These patterns of gene activity shape the cell’s structure and function, enabling it to take on a specific role in the body.
But this decision isn’t up to individual cells. They constantly send and receive chemical signals to and from neighboring cells, which help them understand where they are, what stage of development they are in, and what they should become. These messages spread through multi-step sequences called signaling pathways, which translate external signals into specific changes in gene activity in the cell.
For researchers, being able to retrace the sequence of instructions a cell has received would offer a powerful way to understand how tissues develop and how these processes go awry in disease.
#MIT#Whitehead Institute for Biomedical Research#IRIS#Nature Methods#Pulin Li#Nicholas Hutchins