Multi-Reference Spectral Library Yields Almost Complete Coverage of Heterogeneous LC-MS/MS Data Sets

Spectral libraries play a central
role in the analysis of data-independent-acquisition
(DIA) proteomics experiments. A main assumption in current spectral
library tools is that a single characteristic intensity pattern (CIP)
suffices to describe the fragmentation of a peptide in a particular
charge state (peptide charge pair). However, we find that this is
often not the case. We carry out a systematic evaluation of spectral
variability over public repositories and in-house data sets. We show
that spectral variability is widespread and partly occurs under fixed
experimental conditions. Using clustering of preprocessed spectra,
we derive a limited number of multiple characteristic intensity patterns
(MCIPs) for each peptide charge pair, which allow almost complete
coverage of our heterogeneous data set without affecting the false
discovery rate. We show that a MCIP library derived from public repositories
performs in most cases similar to a ”custom-made” spectral
library, which has been acquired under identical experimental conditions
as the query spectra. We apply the MCIP approach to a DIA data set
and observe a significant increase in peptide recognition. We propose
the MCIP approach as an easy-to-implement addition to current spectral
library search engines and as a new way to utilize the data stored
in spectral repositories.