DIA assay libraries enable large-scale ecological proteomics
DIA is a post-acquisition targeted mass spectrometry-based approach for quantitation of thousands of proteins that was first introduced in 2012 (Gillet et al., 2012). This approach is exceptionally well suited for ecological proteomics because it combines the advantages of not requiring any sample pre-treatment or labeling, a robust sample preparation workflow, the ability to internally normalize samples by iRT standards and normalizing to median abundance of all targets with highly accurate and visually traceable quantitation, direct quantitation based on intensity measurements of peptide fragment ions (rather than indirect methods such as those using antibodies), and robust bioinformatics tools for quantitative analyses and public sharing of data (Skyline, MSstats, mProphet, Panorama). Even though proteome coverage in DIA assay libraries is not complete, assaying over 2000 proteins simultaneously and with very high accuracy significantly expands the capabilities of protein quantitation relative to more traditional antibody-based approaches such as Western blots and ELISAs. Moreover, variability due to antibody batch variation, low affinity, or low specificity of antibodies can be avoided when using DIA assay libraries. Furthermore, DIA assay libraries provide a convenient way for normalization of relative protein abundances against the overall sample median and for large-scale comparisons of transcriptome and proteome dynamics in different ecological contexts. This is only possible because thousands of proteins are included in the library and their mean abundance is much more likely to be comparable amongst samples than the abundance of a single or few normalizer proteins such as beta-actin.
The coverage of DIA assay libraries can be increased by pooling proteomes from multiple tissues and multiple purified subcellular compartments, which can yield DIA assay libraries consisting of more than 10,000 proteins (Blattmann et al., 2019). However, we do not prefer pooling because analysis of DIA data for individual tissues using such pooled DIA libraries would generate many missing values, which represents a problem for statistical analysis. In that regard, tissue-specific DIA assay libraries that were generated from representative training samples are preferable. Thus, generation of DIA assay libraries for key tissues of important ecological model organisms breaks new ground for molecular ecology. To illustrate the usefulness of the O. niloticus kidney DIA assay library for analyzing molecular phenotypes (proteome dynamics) in different ecological contexts we have analyzed kidneys obtained from fish acclimated to different environmental salinities.