High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics

• Published in: Nature biotechnology Vol. 33; no. 9; pp. 990 - 995
• Main Authors: Humphrey, Sean J, Azimifar, S Babak, Mann, Matthias
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2015; Nature Publishing Group
• Subjects: 631/45/275; 631/45/475; 631/80/458/1733; 82/58; 96/95; Agriculture; Animals; Bioinformatics; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cells, Cultured; Gene Expression Profiling - methods; High-Throughput Screening Assays - methods; Identification and classification; Insulin; Insulin - metabolism; letter; Life Sciences; Liver - metabolism; Male; Mass spectrometry; Mass Spectrometry - methods; Methods; Mice; Mice, Inbred C57BL; Phosphoproteins - metabolism; Protein Interaction Mapping - methods; Proteome - metabolism; Proteomics; Signal Transduction - physiology
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt.3327

Phosphoproteomes are rapidly measured in parallel to track the dynamics of insulin signaling in the liver. Mass spectrometry has enabled the study of cellular signaling on a systems-wide scale, through the quantification of post-translational modifications, such as protein phosphorylation 1 . Here we describe EasyPhos, a scalable phosphoproteomics platform that now allows rapid quantification of hundreds of phosphoproteomes in diverse cells and tissues at a depth of >10,000 sites. We apply this technology to generate time-resolved maps of insulin signaling in the mouse liver. Our results reveal that insulin affects ∼10% of the liver phosphoproteome and that many known functional phosphorylation sites, and an even larger number of unknown sites, are modified at very early time points (<15 s after insulin delivery). Our kinetic data suggest that the flow of signaling information from the cell surface to the nucleus can occur on very rapid timescales of less than 1 min in vivo . EasyPhos facilitates high-throughput phosphoproteomics studies, which should improve our understanding of dynamic cell signaling networks and how they are regulated and dysregulated in disease.