Systematic genetics and single‐cell imaging reveal widespread morphological pleiotropy and cell‐to‐cell variability

• Published in: Molecular systems biology Vol. 16; no. 2; pp. e9243 - n/a
• Main Authors: Mattiazzi Usaj, Mojca, Sahin, Nil, Friesen, Helena, Pons, Carles, Usaj, Matej, Masinas, Myra Paz D, Shuteriqi, Ermira, Shkurin, Aleksei, Aloy, Patrick, Morris, Quaid, Boone, Charles, Andrews, Brenda J
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-02-2020; EMBO Press; John Wiley and Sons Inc; Springer Nature
• Subjects: Accuracy; Actin; Alzheimer's disease; Automation; Biology; cell‐to‐cell variability; Classification; Compartments; EMBO10; EMBO57; endocytosis; Endosomes; Genes; Genetic Pleiotropy; Genetic variability; Genetic Variation; Genetics; Genomes; Genotype & phenotype; Genotypes; Heredity; Heterogeneity; high‐content screening; Image analysis; Image processing; Microscopy; Microscopy, Fluorescence; Morphology; Mutants; Mutation; Neural networks; Neural Networks, Computer; Penetrance; Phenotype; phenotype classification; Phenotypes; Pipelines; Plasma; Pleiotropy; Proteins; Quantitative analysis; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - growth & development; Saccharomyces cerevisiae Proteins - genetics; Single-Cell Analysis - methods; single‐cell analysis; Systems Biology; Time-Lapse Imaging; Yeast; Yeasts; high‐content screening; endocytosis; cell‐to‐cell variability; phenotype classification; single‐cell analysis; cell-to-cell variability; high-content screening; single-cell analysis
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.20199243

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.
Synopsis
Automated yeast genetics, high‐content screening and neural network‐based image analysis of single cells are combined to systematically discover genes that influence sub‐cellular morphology and cell‐to‐cell phenotypic variability using four markers of the endocytic pathway.
Unsupervised outlier detection is used to identify 21 subcellular morphologies associated with endocytic compartments.
Neural networks are trained to classify 16.3 million single cells into the 21 phenotypic classes.
Almost 30% of screened genes affect the morphology of at least one endocytic compartment, with more than half of morphology mutants causing multiple phenotypes.
˜90% morphology mutants display incomplete penetrance, and replicative age, organelle inheritance, and response to stress contribute to phenotypic heterogeneity in isogenic populations.
Images, phenotype and penetrance data are available at thecellvision.org/endocytosis.
Graphical Abstract
Automated yeast genetics, high‐content screening and neural network‐based image analysis of single cells are combined to systematically discover genes that influence sub‐cellular morphology and cell‐to‐cell phenotypic variability using four markers of the endocytic pathway.