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Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments—endocytic patch, actin patch, late endosome and vacuole—identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

Genes have been historically classified as either essential or non-essential based on their requirement for viability. However, some genes are essential in some genetic backgrounds but non-essential in others, thus challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations that is often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability inSaccharomyces cerevisiaeto comprehensively characterize these genes. In analyses spanning different evolutionary time-scales, ranging fromS. cerevisiaestrains to human cell lines, dispensable essential genes exhibited distinct phylogenetic properties compared to other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape ofS. cerevisiaestrains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressor genes reflect evolutionary outcomes.

Genetic suppression occurs when the deleterious effects of a primary “query” mutation, such as a disease-causing mutation, are rescued by a suppressor mutation elsewhere in the genome. To capture existing knowledge on suppression relationships between human genes, we examined 2,400 published papers for potential interactions identified through either genetic modification of cultured human cells or through association studies in patients. The resulting network encompassed 476 unique suppression interactions that frequently linked genes that function in the same biological process. Suppressors were strongly enriched for genes with a role in stress response or signaling, suggesting that deleterious mutations can often be buffered by modulating signaling cascades or immune responses. Suppressor mutations tended to be deleterious when they occurred in absence of the query mutation, in apparent contrast with their protective role in the presence of the query. We formulated and quantified mechanisms of genetic suppression that could explain 71% of interactions and provided mechanistic insight into disease pathology. Finally, we used these observations to accurately predict suppressor genes in the human genome. The emerging frequency of suppression interactions and range of underlying mechanisms suggest that compensatory mutations may exist for the majority of genetic diseases.

The toroidal nucleus is a novel chromosomal instability (CIN) biomarker which complements the micronucleus. Understanding the specific biological stresses leading to the formation of each CIN-associated phenotype requires the evaluation of large panels of biological images collected from different genetic backgrounds and environmental conditions. However, the quantification of toroidal nuclei is currently a manual process which is unviable on a large scale. Here, we present PIQUE (Processing of Images for the QUantification of toroidal nuclEi), a tool that automates the identification of toroidal nuclei, minimizing false positives while highly agreeing with the manual quantifications. Additionally, PIQUE identifies micronuclei for a convenient comparison of both CIN biomarkers. PIQUE is an open-source ImageJ plugin with a user-friendly interface that enables a wide scientific community to easily study the novel toroidal nucleus for the analysis of CIN levels in cellular models.AvailabilityThe plugin, the user manual, and the images used for the evaluation of PIQUE are available upon request from the authors.Contactcarles.pons@irbbarcelona.organdcaroline.mauvezin@ub.edu

Chromosomal instability (CIN) is a hallmark of cancer, which is characterized by the gain or loss of chromosomes as well as the rearrangement of the genetic material during cell division. Detection of mitotic errors such as misaligned chromosomes or chromosomal bridges (also known as lagging chromosomes) is challenging as it requires the analysis and manual discrimination of chromosomal aberrations in mitotic cells by molecular techniques. In interphase cells, more frequent in the cell population than mitotic cells, two distinct nuclear phenotypes are associated with CIN: the micronucleus and the toroidal nucleus. Several methods are available for the detection of micronuclei, but none for toroidal nuclei. Here, we provide a method to quantify the presence of both nuclear biomarkers for the evaluation of CIN status in non-mitotic cells particularly suited for genotoxicity screens.