Measuring genetic interactions in human cells by RNAi and imaging

Observation of how genetic interactions modulate phenotypes is a powerful method for dissecting their underlying molecular and functional networks. Whereas in model organisms genetic interaction studies are well established, systematic analysis of genetic interactions in human cells has remained challenging. Here we provide a detailed protocol for large-scale mapping of genetic interactions in human cells using a high-throughput phenotyping approach. Pairwise gene product depletion is induced by siRNA-mediated knockdown, and the resulting phenotypes are quantified by automated imaging and computational analysis to provide the basis for detecting genetic interactions between all pairs of genes tested. The whole workflow, depending on the size of the experiment, takes 3 or more weeks to complete.

[1]  Wolfgang Huber,et al.  Analysis of cell-based RNAi screens , 2006, Genome Biology.

[2]  Gary D Bader,et al.  Quantitative analysis of fitness and genetic interactions in yeast on a genome scale , 2010, Nature Methods.

[3]  Gary D Bader,et al.  The Genetic Landscape of a Cell , 2010, Science.

[4]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[5]  Michael T. McManus,et al.  A Systematic Mammalian Genetic Interaction Map Reveals Pathways Underlying Ricin Susceptibility , 2013, Cell.

[6]  Fergal P. Casey,et al.  Optimal stepwise experimental design for pairwise functional interaction studies , 2008, Bioinform..

[7]  Sourav Bandyopadhyay,et al.  Rewiring of Genetic Networks in Response to DNA Damage , 2010, Science.

[8]  Polina Golland,et al.  Voronoi-Based Segmentation of Cells on Image Manifolds , 2005, CVBIA.

[9]  M. Boutros,et al.  Clustering phenotype populations by genome-wide RNAi and multiparametric imaging , 2010, Molecular systems biology.

[10]  B. Andrews,et al.  Systematic mapping of genetic interaction networks. , 2009, Annual review of genetics.

[11]  Wolfgang Huber,et al.  Mapping of signaling networks through synthetic genetic interaction analysis by RNAi , 2011, Nature Methods.

[12]  N. Krogan,et al.  Phenotypic Landscape of a Bacterial Cell , 2011, Cell.

[13]  Robert P. St.Onge,et al.  Defining genetic interaction , 2008, Proceedings of the National Academy of Sciences.

[14]  Sourav Bandyopadhyay,et al.  Quantitative genetic-interaction mapping in mammalian cells , 2013, Nature Methods.

[15]  Wolfgang Huber,et al.  Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping , 2013, Nature Methods.

[16]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[17]  Wolfgang Huber,et al.  EBImage—an R package for image processing with applications to cellular phenotypes , 2010, Bioinform..

[18]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[19]  J. Moffat,et al.  Scaling up the systematic hunt for mammalian genetic interactions , 2013, Nature Methods.

[20]  Bernd Fischer,et al.  Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi , 2011, BMC Bioinformatics.