Each human is made up of trillions of cells, every with its personal operate, whether or not it’s carrying oxygen, preventing infections, or constructing organs. Even throughout the identical 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 knowledge are large, high-dimensional, and onerous to interpret. Every cell could be represented by hundreds of numbers — its gene expression measurements — which historically require specialised instruments and fashions to investigate. This makes single-cell evaluation sluggish, tough to scale, and restricted to professional customers.
What if we might flip these hundreds of numbers into language that people and language fashions can perceive? That’s, what if we might ask a cell the way it’s feeling, what it’s doing, or the way it would possibly reply to a drug or illness — and get a solution again in plain English? From particular person cells to whole tissues, understanding organic techniques at this degree might rework 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) educated to “learn” and “write” organic knowledge on the single-cell degree. On this publish, we’ll stroll via the fundamentals of single-cell biology, how we rework cells into sequences of phrases, and the way C2S-Scale opens up new potentialities for organic discovery.
