Proteins control and catalyze most of the biochemical functions of cells through their activities and intricate interactions. Generally, neither the abundance nor the interactions of proteins are precisely predictable from e.g. genomic or transcriptomic information. Defining a comprehensive picture of the abundance and subunit composition of protein complexes for a given physiological/pathological state is therefore fundamental for basic and translational research, opening new possibilities for the definition of new clinical biomarkers and/or therapeutic targets.
Our group published an experimental/computational pipeline for the complex-centric analysis of protein complexes. The methods consists of the combination of size exclusion chromatographic (SEC) fractionation of native complexes, the mass spectrometric analysis of sequential fractions by nanoLC- DIA/SWATH MS analysis and a computational framework for the analysis of the resulting data .
Here we present further improvements of this pipeline for exploring protein complexes in clinically relevant samples, at unprecedented speed, accuracy and proteome coverage. The Evosep One chromatography system  allows to characterize with high reproducibility and robustness ~3500 proteins for 21 min-long gradient run with DIA/SWATH acquisition and ~4000 proteins with diaPASEF mode . This new workflow consents to quantify and determine the stoichiometry of hundreds of complexes in a sample within one day of data acquisition.
We applied the pipeline to different biological sample types such as mouse livers, human plasma, and monocityc cells where proteins are extracted in native conditions for preserving their tridimensional structure and interactions.
We are able to identify across the SEC elution profile ~ 5000 proteins for THP-1 cells, ~ 3400 proteins for mouse liver tissues, and ~700 proteins for human plasma across the SEC elution profile. From these analyses we isolated more than ~200 protein complex from CORUM database for liver tissues and ~300 for THP-1 cells, and we could predict 300 protein-protein interactions for human plasma.