ArticlePLoS genetics2014
Noise genetics: inferring protein function by correlating phenotype with protein levels and localization in individual human cells.
Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning.Cancer discovery · 2022Article
- Inferring the structures of signaling motifs from paired dynamic traces of single cells.PLoS computational biology · 2021Article
- The fitness cost and benefit of phase-separated protein deposits.Molecular systems biology · 2019Article
- Discovery and characterization of variance QTLs in human induced pluripotent stem cells.PLoS genetics · 2019Article
- Image-based spatiotemporal causality inference for protein signaling networks.Bioinformatics (Oxford, England) · 2017Article
- Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.G3 (Bethesda, Md.) · 2017Article
- A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.Methods (San Diego, Calif.) · 2016Article
- Surviving apoptosis: life-death signaling in single cells.Trends in cell biology · 2015Review
- On comparing heterogeneity across biomarkers.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2015Article
- Computational analysis of signaling patterns in single cells.Seminars in cell & developmental biology · 2015Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.
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