In this Protocol contribution, we describe an optimized Phosphoproteomic-DIA workflow that enables precise quantification of thousands of phosphosites based on our recent experience, studies, and developed methods. Like with any data generation efforts for MS-based proteomics, we found that sample quality (
e.
g., phosphopeptide enrichment efficiency), liquid chromatography conditions (
e.
g., analytical column efficiency for DIA-MS), and stable MS running status are crucial factors for satisfactory phosphoproteome coverage. For example, sharp chromatographic peaks can increase the phosphopeptide resolution on the retention time dimension and improve identification rates, similar to the scenario of analyzing non-PTM peptides. However, the successful phosphoproteomic analysis never lies in expensive MS instruments, not even in the analytical depth of the entire phosphoproteome, but in the biological insights and evidence acquired that support new discoveries in systems biology and systems medicine. In fact, our described protocol ends at the initial NA imputation, motif extraction, and differential phosphosite extraction. Practically, the further phosphoproteomic data analysis tailored to particular projects can be much more challenging and exciting after these initial steps. This step is much less mature and cannot be described as a general and simple protocol. For example, using Phos-DIA and DeltaSILAC, we have measured the steady-state phosphoproteomes of two HeLa cell strains (CCL2 vs. Kyoto) with 25,000 phosphosites (Wu
et al.
2021). The further Circos analysis of this dataset enables a phosphoproteome-centric relative-scale correlation analysis between different molecular layers, underscoring both functional heterogeneities between the two HeLa stains and the underlying determining factors of such heterogeneity via gene expression and post-translational regulation (
Fig. 5). Moreover, a certain longitudinal phosphoproteomic dataset might benefit from
e.
g., fuzzy c-clustering analysis (Conesa
et al.
2006) and other signaling reconstruction algorithms (Gjerga
et al.
2021; Kim
et al.
2021). Due to the current sparse understanding of all phosphosites discovered by MS, the PTM site-specific annotation databases (Krug
et al.
2019) and bioinformatic framework incorporating new phosphoproteomic results will be highly appreciated in the future (Liu
2022; Ochoa
et al.
2020; Xiao
et al.
2022; Yang
et al.
2015). Finally, we hope this Protocol contribution could be helpful, particularly for new phosphoproteomic users in designing and implementing their experiments.