Putting the pieces together: high-performance LC-MS/MS provides network-, pathway-, and protein-level perspectives in Populus

Abstract

High-performance mass spectrometry (MS)-based proteomics enabled the construction of a detailed proteome atlas for Populus, a woody perennial plant model organism. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: (1) network-wide, (2) pathway-specific, and (3) protein-level viewpoints. Together, enhanced protein retrieval through a detergent-based lysis approach and maximized peptide sampling via the dual-pressure linear ion trap mass spectrometer (LTQ Velos), have resulted in the identification of 63,056 tryptic peptides. The technological advancements, specifically spectral-acquisition and sequencing speed, afforded the deepest look into the Populus proteome, with peptide abundances spanning 6 orders of magnitude and mapping to ∼25% of the predicted proteome space. In total, tryptic peptides mapped to 11,689 protein assignments across four organ-types: mature (fully expanded, leaf plastichronic index (LPI) 10-12) leaf, young (juvenile, LPI 4-6) leaf, root, and stem. To resolve protein ambiguity, identified proteins were grouped by sequence similarity (≥ 90%), thereby reducing the protein assignments into 7538 protein groups. In addition, this large-scale data set features the first systems-wide survey of protein expression across different Populus organs. As a demonstration of the precision and comprehensiveness of the semiquantitative analysis, we were able to contrast two stages of leaf development, mature versus young leaf. Statistical comparison through ANOVA analysis revealed 1432 protein groups that exhibited statistically significant (p ≤ 0.01) differences in protein abundance. Experimental validation of the metabolic circuitry expected in mature leaf (characterized by photosynthesis and carbon fixation) compared with young leaf (characterized by rapid growth and moderate photosynthetic activities) strongly testifies to the credibility of the approach. Instead of quantitatively comparing a few proteins, a systems view of all the changes associated with a given cellular perturbation could be made.

Fig. 1.

Peptide and protein dynamic range. Dynamic range of measurement was assessed for each identified peptide and protein across three replicate runs for all four organ types. A, Maximum ion intensity values obtained for each peptide's extracted ion chromatogram (y axis) were ranked by intensity and plotted against cumulative number of assigned MS/MS spectra (x axis). Curves represent individual replicates per organ to identify run-to-run differences in dynamic range. B, Assembled protein intensity (y axis), calculated by summing constituent peptide intensities across all replicates, was plotted against the cumulative number of identified proteins (x axis) to identify the overall protein dynamic range achieved per individual organ. Dynamic range values, represented as magnitudes (base 10), are listed in the figure legend. Light blue, LTQ stem; blue, LTQ-Velos mature leaf; red, LTQ-Velos young leaf; purple, LTQ-Velos root; green, LTQ-Velos stem.

Fig. 2.

Quantitative distribution of detected protein groups by their functional classification. Proteins identified in each organ-type were assigned KOG categories to identify organ-specific functional trends. Category representation was weighted by the sum total of the normalized spectral counts (nSpC) contributed by each protein in the classification. Notable trends include a high proportion of nSpC in mature leaf attributed to chloroplast-based proteins, enrichment of cytoskeletal components in stem, and an increase in translation in young leaf compared with the other tissues. Also noted is the large degree of nSpC representation falling into the unknown category, suggesting a need for improved protein annotation as a whole.

Fig. 3.

Global proteomic view across all four organs. Numbers of identified protein groups, as represented by a 4-tiered Venn diagram (A), indicate the level of proteomic overlap between organ types. Notable regions include protein groups specific to only one organ-type (solid blue, yellow, green and red) as well as groups identified across all organs (central brown region). B, Degree of proteomic overlap as visualized by Pearson's correlation analysis of all protein nSpC values averaged across all replicates for each particular organ. The degree of correlation increases as a function of organ proximity.

Fig. 4.

Hierarchical clustering classifies protein groups by distinct localization trends. Identified protein groups above the determined prevalence value were clustered into groups based on nSpC abundance patterns across all organ types. Abundance values, ranging from –1.56 to +1.56, were calculated by converting nSpC for each protein group, averaged across all replicates, to a value representing the number of standard deviations away from the row mean. Protein groups sharing similar standardized abundance trends were then clustered into distinct families (listed top to bottom - 1, 12, 4, 8, 7, 2, 13, 10, 3, 11, 6, 14, 9, and 5) and denoted in alternate colors. Columns representing each organ-type were then clustered (bottom) based on global data set similarities.

Fig. 5.

Quantitative distribution of detected protein groups by their functional classification for each hierarchical cluster. Protein group clusters were deconvoluted by organ-type (A) to show each organ's nSpC contribution relative to the total nSpC populating each cluster (across all organs). Table cells are color-coded based on percent contribution (green:red::low:high) to quickly visualize each organ's share of the total nSpC. B, To view the functional signature of each cluster (z axis), cluster members were classified into their respective KOG categories (x axis), with each category's representation weighted, based on the sum of nSpC of contributing protein group members (y axis).

Fig. 6.

Differential proteomic analysis of young versus mature leaf by ANOVA. Protein groups identified in young and/or mature leaf above the determined prevalence value were analyzed by ANOVA to compare the functional signature between two distinct developmental stages of leaf. A, Protein group abundances (nSpC), averaged across all replicates (n = 6) per stage, were compared between young leaf (YL, y axis) and mature leaf (ML, x axis) and visualized as a scatterplot. Protein groups that showed a significant (p ≤ 0.01) difference in abundance are colored red. Dotted lines separate “effectively zero” subdistributions from the main distribution in the top right quadrant. Proteins groups in this main distribution were identified in both developmental stages whereas proteins in the subdistributions were likely found in only one stage. To further visualize the statistical metrics of the main distribution, a volcano plot (B) was constructed, comparing the LOG2(nSpC)-based difference between both developmental stages (x axis) to the level of statistical significance, represented as -LOG10(p value) (y axis). As in (A), protein groups that showed a significant (p ≤ 0.01) difference in abundance are colored red.

Fig. 7.

Up-regulated metabolic pathways as dictated by Populus leaf developmental stage. Proteins exhibiting differential abundance patterns (ANOVA; p ≤ 0.01) across both young and mature leaf were mapped to KEGG pathways using iPath v.2.0 and color-coded to indicate the degree of protein abundance differences between each developmental stage. A, Proteins with significantly increased abundance in mature leaf are labeled in red with brighter shades indicative of larger differences. Highlighted pathways (dashed boxes) include photosynthesis (PS), carbon fixation (CF), and photorespiration (PR). B, Proteins with significantly increased abundance in young leaf are labeled in green with brighter shades indicative of larger differences. Highlighted pathways include nucleotide metabolism (NM), flavonoid biosynthesis (FB), fatty acid metabolism (FA), pyruvate metabolism (PM), terpenoid biosynthesis (TB), and TCA cycle (TCA).