Journal
CELL
Volume 145, Issue 3, Pages 470-482Publisher
CELL PRESS
DOI: 10.1016/j.cell.2011.03.037
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Funding
- American Cancer Society [PF-06-254-01-CCG]
- Helen Hay Whitney Foundation
- NIH [R01 GM074207, R01 HD046236, R01 GM085503, R01 GM085150, R01 GM088151, R01 GM073874]
- Ludwig Institute for Cancer Research
- NIH National Center for Research Resources, for strains
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High-content screening for gene profiling has generally been limited to single cells. Here, we explore an alternative approach-profiling gene function by analyzing effects of gene knockdowns on the architecture of a complex tissue in a multicellular organism. We profile 554 essential C. elegans genes by imaging gonad architecture and scoring 94 phenotypic features. To generate a reference for evaluating methods for network construction, genes were manually partitioned into 102 phenotypic classes, predicting functions for uncharacterized genes across diverse cellular processes. Using this classification as a benchmark, we developed a robust computational method for constructing gene networks from high-content profiles based on a network context-dependent measure that ranks the significance of links between genes. Our analysis reveals that multi-parametric profiling in a complex tissue yields functional maps with a resolution similar to genetic interaction-based profiling in unicellular eukaryotes-pinpointing subunits of macromolecular complexes and components functioning in common cellular processes.
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