Isobaric labeling: Expanding the breadth, accuracy, depth, and diversity of sample multiplexing

Temporal Proteomic Profiling of Pheromone-Induced Cell Cycle Re-Entry in Saccharomyces cerevisiae

The regulation of cell cycle progression in response to environmental cues is essential for cellular adaptation. In Saccharomyces cerevisiae, the BAR1 gene modulates sensitivity to the mating pheromone α-factor, which induces cell cycle arrest in G1. Here, we investigated the dynamic proteomic response in the bar1 deletion strain using a 27-plex experimental design with TMTproD isobaric labeling. Asynchronous bar1Δ cells were treated with α-factor and then released from the pheromone-induced cell cycle arrest in G1. Using higher-order TMTpro sample multiplexing, we generated global temporal profiles of protein abundance associated with recovery from this arrest, with triplicate samples collected at eight time points from 0 to 165 min after washing out the pheromone. We identify specific proteins involved in cell cycle re-entry and in the attenuation of the pheromone signal, providing insights into the regulatory mechanisms of mating response in yeast. This study also contributes significantly to dynamic proteomic analysis of cell cycle progression. We present a versatile approach for investigating complex cellular processes and showcase cell cycle progression following release from pheromone-induced arrest in yeast.

Achieving a 35-Plex Tandem Mass Tag Reagent Set through Deuterium Incorporation

Mass spectrometry-based sample multiplexing with isobaric tags permits the development of high-throughput and precise quantitative biological assays with proteome-wide coverage and minimal missing values. Here, we nearly doubled the multiplexing capability of the TMTpro reagent set to a 35-plex through the incorporation of one deuterium isotope into the reporter group. Substituting deuterium frequently results in suboptimal peak coelution, which can compromise the accuracy of reporter ion-based quantification. To counteract the deuterium effect on quantitation, we implemented a strategy that necessitated the segregation of nondeuterium and deuterium-containing channels into distinct subplexes during normalization procedures, with reassembly through a common bridge channel. This multiplexing strategy of "design independent sub-plexes but acquire together" (DISAT) was used to compare protein expression differences between human cell lines and in a cysteine-profiling (i.e., chemoproteomics) experiment to identify compounds binding to cysteine-113 of Pin1.

Comparing and integrating TMT-SPS-MS3 and label-free quantitative approaches for proteomics scrutiny in recalcitrant Mango (Mangifera indica L.) peel tissue during postharvest period

Despite substantial advances in the use of proteomic technologies, their widespread application in fruit tissues of non-model and recalcitrant species remains limited. This hampers the understanding of critical molecular events during the postharvest period of fleshy tropical fruits. Therefore, we evaluated label-free quantitation (LFQ) and TMT-SPS-MS3 (TMT) approaches to analyse changes in the protein profile of mango peels during postharvest period. We compared two extraction methods (phenol and chloroform/methanol) and two peptide fractionation schemes (SCX and HPRP). We accurately identified 3065 proteins, of which, 1492 were differentially accumulated over at 6 days after harvesting (DAH). Both LFQ and TMT approaches share 210 differential proteins including cell wall proteins associated with fruit softening, as well as aroma and flavour-related proteins, which were increased during postharvest period. The phenolic protein extraction and the high-pH reverse-phase peptide fractionation was the most effective pipeline for relative quantification. Nevertheless, the information provided by the other tested strategies was significantly complementary. Besides, LFQ spectra allowed us to track down intact N-glycopeptides corroborating N-glycosylations on the surface of a desiccation-related protein. This work represents the largest proteomic comparison of mango peels during postharvest period made so far, shedding light on the molecular foundation of edible fruit during ripening.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.