The quantification of peptides using targeted analysis of data-independent acquisition MS (DIA-MS) is dependent on the size and characteristics of the assay library. We addressed several important questions on how library composition influences: (1) the number of peptides extracted from DIA-MS datasets, (2) the quality of these peptides and proteins, and (3) the biological conclusions inferred. To answer these questions we constructed five libraries from mouse vascular smooth muscle cell (VSMC) lysate, each unique in depth, input sample complexity, data acquisition mode (DDA-MS or DIA-MS), and precursor fragmentation mode (TOF-CID or Orbitrap HCD) and extracted them against the same eight DIA-MS files of VSMCs treated with vehicle or transforming growth factor β-1 (TGF-β1). We found that along with differences in peptide and protein composition, the fragments representing a given peptide differed between the libraries. Collectively these differences impacted both peak group score profile and protein abundance estimates. Surprisingly, there was little overlap in the TGF-β1 response proteome between libraries. We conclude that additional work is needed to optimize peptide assay library building for DIA-MS applications, particularly in terms of selecting optimal peptides and their respective fragments for protein quantification.
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