Disease models drift. Single-cell analysis sees what bulk sequencing hides.

Disease models drift over time through passaging, engraftment, and culture, and a population-average readout can miss it. This guide is for scientists who rely on patient-derived xenografts, organoids, cell lines, or engineered lines to make drug-program decisions. It explains the difference between two kinds of questions. Catalog questions ask which variants are present and at what frequency, and bulk DNA sequencing answers them efficiently. Structure questions ask which cells carry those variants, which rare subclones exist, and what changes over time. Answering them requires single-cell resolution.

The guide covers confirming model fidelity, confirming lead candidates (which clones a candidate clears and which survive it), and carrying a single clonal profile forward from the bench to trial design. It includes a scored worksheet you can run against your own models today, plus answers to practical questions on sample input, panel coverage, cost, and bioinformatics support. It closes with a walkthrough of a published AML xenograft study with two outcomes, resistance emergence and full eradication. On a bulk average they look identical. At single-cell resolution they turn out to be completely different.