Tremendous advances in MS-based proteomics has revealed the pervasive nature of protein phosphorylation1. The widespread utility and application of existing approaches to quantifying signalling in vivo and en-masse however remains hampered by poor scalability of enrichment and labelling workflows, large input-material requirements, and inadequate coverage of key signaling networks, particularly in the context of tissue samples. Here, we set out to develop and refine a phosphoproteomics pipeline that was scalable, without compromising performance. Such a pipeline would enable processing of large sample numbers in parallel, and without using proprietary reagents to ensure its widespread applicability.

Here we describe EasyPhos2, a high-performance, scalable and extensible phosphoproteomics workflow that greatly streamlines the study of signalling networks across large sample numbers. We evaluated our platform in cells and tissues, measuring the phosphoproteomes of mouse liver cell lines, where replicate single-shot measurements quantified ~20,000 distinct phosphopeptides in one day. In liver, brain and kidney tissues, half-day measurements together quantified 24,000 phosphopeptides. Deep phosphoproteome coverage in the absence of fractionation was facilitated by high enrichment specificity (>95% of identified peptides phosphorylated). Compared with our previous studies in the same systems we achieved 3x the depth, despite using 10x less material and 1/3rd of the measurement time.

EasyPhos requires minimal input material and measurement time without compromising depth, making large-scale signaling studies much more practical. Just as cheap and rapid sequencing technologies have driven an explosion of data in the genomics fields, we envisage an acceleration of MS-driven signalling studies providing rich and complex insights about cellular network function in physiological and patho-physiological contexts.