Simple and Effective Affinity Purification Procedures for Mass Spectrometry-Based Identification of Protein-Protein Interactions in Cell Signaling Pathways.

Identification of protein-protein interactions can be a critical step in understanding the function and regulation of a particular protein and for exploring intracellular signaling cascades. Affinity purification coupled to mass spectrometry (APMS) is a powerful method for isolating and characterizing protein complexes. This approach involves the tagging and subsequent enrichment of a protein of interest along with any stably associated proteins that bind to it, followed by the identification of the interacting proteins using mass spectrometry. The protocol described here offers a quick and simple method for routine sample preparation for APMS analysis of suitably tagged human cell lines.

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities.

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN(-/-) DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model.