Each human is made up of trillions of cells, every with its personal perform, whether or not it’s carrying oxygen, preventing infections, or constructing organs. Even throughout 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 knowledge are huge, high-dimensional, and exhausting to interpret. Every cell will 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, troublesome to scale, and restricted to knowledgeable customers.
What if we may flip these hundreds 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 would possibly reply to a drug or illness — and get a solution again in plain English? From particular person cells to complete tissues, understanding organic methods at this stage may remodel how we examine, diagnose, and deal with illness.
As we speak in “Scaling Massive Language Fashions for Subsequent-Era Single-Cell Evaluation“, we’re excited to introduce Cell2Sentence-Scale (C2S-Scale), a household of highly effective, open-source massive language fashions (LLMs) skilled to “learn” and “write” organic knowledge on the single-cell stage. On this submit, we’ll stroll by means of the fundamentals of single-cell biology, how we remodel cells into sequences of phrases, and the way C2S-Scale opens up new prospects for organic discovery.