Pompei, S.; Geroldi, A.; Rivetti, P.; Grassi, E.; Vurchio, V.; Tallarico, G.; Corti, G.; Tattini, L.; Liti, G.; Bertotti, A.; Cosentino Lagomarsino, M.

Quantifying mutational processes is crucial for understanding evolution, especially in clinical and experimental settings, but remains challenging. Current methods are labor-intensive and often lack robustness, resulting in inconsistent or biased outcomes, particularly in mammalian cells. We use patient-derived colorectal cancer organoids to introduce Seq, a universal framework for inferring mutation rates in diverse biological systems. Our approach extracts mutation rates from deep sequencing of single clonal expansions, with a time gain of tenfold or more compared to a mutation-accumulation line, at the cost of three billion read whole-genome sequencing. Seq relies on four critical components: (i) a controlled experimental setup enabling validation (ii) a quantitative estimate inspired by the classic Luria Delbruck spectrum for subclonal mutations derived from population dynamics, (iii) robust statistical models accounting for sampling noise and sequencing errors, and (iv) a data-analysis pipeline for subclonal mutation detection that compares endpoint populations to a closely related ancestor. Our models establish precise requirements for sequencing depth, genome size, and mutation frequency detection necessary for accurate mutation rate estimates. Crucially, we show that failing to meet these criteria can lead to errors spanning several orders of magnitude. We validate our approach using parallel mutation-accumulation experiments in colorectal cancer organoids, finding mutation rate estimates consistent with previous Mutation Accumulation studies. In particular, our results confirm the expected differences between MSI and MSS tumors. Finally, to demonstrate the adaptability of Seq, we apply it to yeast, leveraging multiple independent replicates to compensate for its much smaller genome. The robustness and broad applicability of Seq establish it as a powerful and universal tool for mutation rate quantification, spanning applications from cancer research to microbial evolution.