(A) Cancer usually begins with a mutation (dark blue shape in top cell) in a single tumor cell that it passes on to its daughter cells following division. A daughter cell can then gain a new mutation (shown in pink) that it passes on to its progeny. These cells also divide and acquire new mutations (shown in different colors). Over time this leads to a population of cells that are genetically distinct from each other: the initial mutation is present in all the cells, whereas mutations that occurred later are present in a smaller number of cells (bottom row). (B) Now, KaramiNejadRanjba et al. have created a sequencing approach called DigiPico that can identify mutations that occur later during tumor evolution. First, cell material is extracted from a small group of 20–30 cells using laser microdissection and diluted down to single molecules of DNA which are plated into 384 individual wells (top panel). The DNA molecule in each well is amplified to create individual libraries, which are then combined and sequenced (bottom panel). After sequencing, an artificial neural network called MutLX is applied to the data to determine which of the genetic variants put into the algorithm (shown in dark red) are mutations that appear later during tumor evolution (shown in dark blue) and which are artefacts generated by the amplification process. Figure created using BioRender (BioRender.com).