The majority of studies summarized here employ label-free 2DGE approaches (25 out of 34), with only a few employing DIGE technology (6) or gel-free approaches, including iTRAQ (4) or LFQP (1). The preference for 2DGE is a reflection of the fact that the technique is relatively economically more attractive than the other more technologically advanced approaches, requiring significantly less mass spectrometry hours when compared to complex shot-gun type proteomics techniques, and does not require any specialized equipment or complex data analysis [
60]. Furthermore, quantitative analysis can be easily carried out on a large series of multiple biological replicates, although this necessitates the running of a considerable number of gels, which could increase the technical variability inherent in this approach. Nevertheless, the possibilities are well beyond the multiplexing capacities of mass spectrometry label-based quantification, which in the case of iTRAQ is limited to eight in commercial kits, although with hyperplexing able to expand this to 18 in special experimental cases using triplex metabolic labelling and six-plex isobaric tags [
61]. Additionally, this technique is effective in identifying post-translational modifications and alternative splicing of proteins [
62,
63]. Despite these advantages, 2DGE is restrictive in its depth of analysis as it is able to resolve only a limited collection of highly abundant and mostly soluble proteins with hydrophobic proteins poorly represented [
62]. Many of the proteins that are identified using the 2DGE technique appear to be shared between studies regardless of the stress, tissue or species. This is highlighted in cross view analysis of proteomic data from multiple species which compare differentially responsive proteins in humans, rats, mice worms, and even fruit fly which demonstrate that specific proteins were over-represented in 2DGE quantitative analysis studies including glycolytic enzymes, heat shock proteins, and ROS scavenging enzymes [
64,
65], including enolase, HSP70, elongation factors, and superoxide dismutase. Remarkably, this list matches many of the proteins identified in the plant studies reviewed here. This is not to say that these proteins are not valid stress sensors, however, to be used as biomarkers, candidate proteins should be specific to a particular stress condition [
65,
66]. To identify more specific biomarkers we need to go beyond identifying highly abundant proteins. This implies that there is a need to focus on low abundant proteins via fractionation of samples to reduce complexity, yet in the studies reviewed here only three out of a total of 30 used samples other than total protein extracts. These included soluble proteins isolated by pelleting membrane fractions [
36], proteins from purified mitochondria [
20], and nuclear proteins [
44].
An important factor in quantitative proteomics analysis, which determines the robustness of the results, is the number of technical and biological replicates employed. This is an important aspect of the experimental design and contributes to the statistical power of the analysis [
67]. A number of the studies reported in this review failed to specify the number of replicates, and several used pooled samples rather than replicates (
Table 1). It remains the responsibility of editors and reviewers to ensure studies that are published are statistically sound.
Of the 30 proteomics data sets examined, approximately two thirds (25) compared only one treatment condition or time point with one control condition or time point. Only a single study compared both multiple stress conditions with various time points, including both short and long-term stress [
31]. The majority of the studies also involved analysis of proteins from plants at a very young developmental stage, which make it difficult to extrapolate the actual stress tolerance to that of the crops that are more dependent on longer term plant performance and cyclic stress conditions.
Finally, essential to biomarker discovery is the ability to systematically validate the quantitative proteomics study as it depends on complex computational and statistical analysis that while attempting to limit false discovery rate cannot unequivocally exclude it [
66]. Therefore, to ensure the robustness of the data and for meaningful biomarker discovery, independent testing is crucial; yet only six studies mentioned here attempt to validate their proteomic results (
Table 1). The most common form of validation is Western blot analysis, and was used by Jangpromma et al. [
40] to confirm changes in protein amount for an unknown protein, p18, in different genotypes under drought stress. Good correlation between 2DGE and Western blot analysis was also observed for increases in β-actin protein and decreases in histone H4 after UV-B irradiation in corn [
14]. Kottapalli et al. [
39], and Alvarez et al. [
18], use PCR to confirm the change in abundance of select proteins in their studies. However, mRNA levels did not always correlate well with protein levels. Witzel et al. [
30,
31], also reported a high degree of correlation between 2DGE and Western blot analysis for salinity stress responsive proteins in barley, showing results for catalase [
30], HMGB2, HDS, and FQR1 [
31] proteins.