RNA-seq

A genome is the same in (almost) every cell of your body, yet a neuron and a liver cell look and behave completely differently. The difference is which genes each cell is actively using — which genes are being transcribed into RNA, and how much. RNA-seq is the method that captures this: it sequences the RNA in a sample to read out, gene by gene, what the cell is actually doing right now.

Because sequencers read DNA, RNA-seq first converts the cell's RNA into DNA copies (cDNA) using reverse transcriptase, then sequences those by next-generation sequencing. The result is millions of reads coming from the RNA molecules present. Software maps each read back to the gene it came from and counts how many reads landed on each gene; the more reads from a gene, the more of its RNA was present, which serves as a measure of how strongly that gene was expressed. Beyond just counting, RNA-seq can reveal which exons were used (catching alternative splicing), find RNAs no one knew about, and detect fusion transcripts — far more than older methods that only measured a pre-chosen list of genes.

RNA-seq is the standard way to take a snapshot of a cell's transcriptome — the full set of RNA it is making — and to compare states: healthy versus diseased tissue, treated versus untreated cells, one cell type versus another. Honest caveats matter: RNA-seq measures RNA abundance, which is a proxy for, not a direct measurement of, protein levels (RNA and protein amounts do not always track each other), and turning read counts into reliable expression differences depends heavily on careful normalization, replicates, and statistics.