https://news.mit.edu/2026/ai-model-decodes-language-cells-use-communicate-1015

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 cells structure and function, enabling it to take on a
specific role in the body.

But this decision isnt 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. 

But this has been difficult to achieve because scientists have long assumed
that the effects of signaling pathways vary widely across cell types, meaning
they would need to map each pathway separately in each cell type  an arduous
and painstaking process.

Now, in a new study led by MIT assistant professor of biology and Whitehead
Institute for Biomedical Research member Pulin Li and graduate student
Nicholas Hutchins, researchers have discovered that each signaling pathway
leaves behind a unique fingerprint  a distinctive pattern of gene activity
that reflects the particular signals the cell has encountered.