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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.
Highlights
To understand which proteins contribute to a biological phenomenon, current approaches use perturbations such as gene knockdown, over-expression or knockout
Inferring the function of proteins and the role they play in cellular processes is essential for our understanding of cell biology, genetics and biology in general
We use the motility of human cancer cells as a model system that is highly important for understanding metastasis in cancer
Summary
To understand which proteins contribute to a biological phenomenon, current approaches use perturbations such as gene knockdown, over-expression or knockout. These approaches have provided the basis for much of what we know about cell biology. Perturbations currently used are usually large - a protein expression is either markedly reduced or increased, and the measurement is far from the cells normal working condition. This can lead to artificial off-target effects or to masking of the perturbation by changes in the cell that compensate for the loss of a protein. It is possible that some of the information about protein function has remained hidden due to these features of current methods
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