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.