![]() Moreover, using population-level measurements as a phenotypic read-out precludes a quantitative analysis of single-cell phenotypes and thus an analysis of cell-to-cell variability, which is a key consideration for prediction of the consequences of genetic perturbation. While measuring growth phenotypes in cell populations has enabled inference of gene function, biological pathways and networks, the mechanistic underpinnings of a particular phenotype are typically difficult to infer from bulk population measurements. Pioneering work in yeast and other model systems has made use of quantifiable phenotypes, such as cell growth, to systematically survey the consequences of single, double and higher-order genetic perturbations in populations of mutant cells (Costanzo et al, 2019 Domingo et al, 2019). SynopsisĪlthough we understand that most phenotypes including diseases are influenced by the genetic variation encoded in individual genomes, our ability to predict when a genetic lesion will cause a specific phenotype remains limited. 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. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. 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). 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 ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. ‡ These authors contributed equally to this work.6 RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan.5 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.4 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.3 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.2 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.1 The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
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