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Mutations that compromise mismatch repair (MMR) or DNA polymerase ε or δ exonuclease domains produce mutator phenotypes capable of fueling cancer evolution. Here, we investigate how combined defects in these pathways expands genetic heterogeneity in cells of the budding yeast, Saccharomyces cerevisiae, using a single-cell resolution approach that tallies all mutations arising from individual divisions. The distribution of replication errors present in mother cells after the initial S-phase was broader than expected for a single uniform mutation rate across all cell divisions, consistent with volatility of the mutator phenotype. The number of mismatches that then segregated to the mother and daughter cells co-varied, suggesting that each division is governed by a different underlying genome-wide mutation rate. The distribution of mutations that individual cells inherit after the second S-phase is further broadened by the sequential actions of semiconservative replication and mitotic segregation of chromosomes. Modeling suggests that this asymmetric segregation may diversify mutation burden in mutator-driven tumors., Dowsett et al use a single-cell resolution approach to analyse the distribution of mutations across several divisions in yeast diploid strains mutated in mismatch repair and polymerase delta proofreading. They find that the underlying mutation rate varies from one division to another, and that new mutations segregate unequally between sister chromatids at each division, expanding genetic heterogeneity in the population.

The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.

Mutations that compromise mismatch repair (MMR) or DNA polymerase exonuclease domains produce mutator phenotypes capable of fueling cancer evolution. Tandem defects in these pathways dramatically increase mutation rate. Here, we model how mutator phenotypes expand genetic heterogeneity in budding yeast cells using a single-cell resolution approach that tallies all replication errors arising from individual divisions. The distribution of count data from cells lacking MMR and polymerase proofreading was broader than expected for a single rate, consistent with volatility of the mutator phenotype. The number of mismatches that segregated to the mother and daughter cells after the initial round of replication co-varied, suggesting that mutagenesis in each division is governed by a different underlying rate. The distribution of “fixed” mutation counts that cells inherit is further broadened by an unequal sharing of mutations due to semiconservative replication and Mendelian segregation. Modeling suggests that this asymmetric segregation may diversify mutation burden in mutator-driven tumors.

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.