Each human is made up of trillions of cells, every with its personal operate, whether or not it’s carrying oxygen, combating infections, or constructing organs. Even inside the similar tissue, no two cells are precisely alike. Single-cell RNA sequencing (scRNA-seq) permits us to measure the gene expression of particular person cells, revealing what every cell is doing at a given second.
However there’s a catch: single-cell information are huge, high-dimensional, and arduous to interpret. Every cell might be represented by 1000’s of numbers — its gene expression measurements — which historically require specialised instruments and fashions to research. This makes single-cell evaluation sluggish, tough to scale, and restricted to professional customers.
What if we may flip these 1000’s of numbers into language that people and language fashions can perceive? That’s, what if we may ask a cell the way it’s feeling, what it’s doing, or the way it may reply to a drug or illness — and get a solution again in plain English? From particular person cells to total tissues, understanding organic techniques at this degree may remodel how we research, diagnose, and deal with illness.
As we speak in “Scaling Giant Language Fashions for Subsequent-Era Single-Cell Evaluation“, we’re excited to introduce Cell2Sentence-Scale (C2S-Scale), a household of highly effective, open-source giant language fashions (LLMs) skilled to “learn” and “write” organic information on the single-cell degree. On this put up, we’ll stroll via the fundamentals of single-cell biology, how we remodel cells into sequences of phrases, and the way C2S-Scale opens up new potentialities for organic discovery.
