Hyperplexing Approaches for up to 45-Plex Quantitative Proteomic Analysis

Quantitative Proteome and Phosphoproteome Profiling across Three Cell Lines Revealed Potential Proteins Relevant to Nasopharyngeal Carcinoma Metastasis

Despite the substantial reduction in the mortality rates of nasopharyngeal carcinoma (NPC), metastasis remains the primary cause of death in NPC cases. To explore metastasis-related proteins, we conducted proteomic and phosphoproteomic analyses of three NPC cell lines: SUNE1 and its subclones, 5-8F (high metastatic potential) and 6-10B (low metastatic potential). Using TMT-based quantification, we identified 1231, 1524, and 166 differentially regulated proteins (DRPs), as well as 177, 270, and 20 differentially regulated phosphoproteins (DRpPs) in 5-8F/SUNE1, 6-10B/SUNE1 and 5-8F/6-10B, respectively. These were enriched in cancer metastasis-related pathways, including cell migration and PPAR and PI3K pathways. Notably, 5-8F and 6-10B showed greater proteomic and phosphoproteomic similarity. To identify key proteins involved in NPC metastasis, we focused on the top 10 DRPs in 5-8F/6-10B. Knockdown experiments revealed that eight of these proteins, CRABP2, DNAJC15, NACAD, MYL9, DPYSL3, MAOA, MCAM, and S100A2, significantly influenced cell migration or invasion, with CRABP2, NACAD, and DPYSL3 dramatically enhancing these processes. Notably, DNAJC15 and NACAD are identified for the first time as novel metastasis-related proteins. Our findings demonstrate the effectiveness of this approach in identifying NPC metastasis biomarker candidates and offer new insights into underlying metastasis mechanisms, thus laying the groundwork for future research endeavors.

Tools and techniques for quantitative glycoproteomic analysis

Recent advances in mass spectrometry (MS)-based methods have significantly expanded the capabilities for quantitative glycoproteomics, enabling highly sensitive and accurate quantitation of glycosylation at intact glycopeptide level. These developments have provided valuable insights into the roles of glycoproteins in various biological processes and diseases. In this short review, we summarize pertinent studies on quantitative techniques and tools for site-specific glycoproteomic analysis published over the past decade. We also highlight state-of-the-art MS-based software that facilitate multi-dimension quantification of the glycoproteome, targeted quantification of specific glycopeptides, and the analysis of glycopeptide isomers. Additionally, we discuss the potential applications of these technologies in clinical biomarker discovery and the functional characterization of glycoproteins in health and disease. The review concludes with a discussion of current challenges and future perspectives in the field, emphasizing the need for more precise, high-throughput and efficient methods to further advance quantitative glycoproteomics and its applications.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

102-Plex Approach for Accurate and Multiplexed Proteome Quantification

Hyperplexing approaches have been aimed to meet the demand for large-scale proteomic analyses. Currently, the analysis capacity has expanded to up to 54 samples within a single experiment by utilizing different isotopic and isobaric reagent combinations. In this report, we propose a super multiplexed approach to enable the analysis of up to 102 samples in a single experiment, by the combination of our recently developed TAG-TMTpro and TAG-IBT16 labeling. We systematically investigated the identification and quantification performance of the 102-plex approach using the mixtures of E. coli and HeLa peptides. Our results revealed that all labeling series demonstrated accurate and reliable quantification performance. The combination of TAG-IBT16 and TAG-TMTpro approaches expands the multiplexing capacity to 102 plexes, providing a more multiplexed quantification method for even larger-scale proteomic analysis. Data are available via ProteomeXchange with the identifier PXD042398.

Identifying drug targets with thermal proteome profiling using IBT-16plex

RATIONALE

Thermal proteome profiling (TPP) has been widely used for the identification of drug targets for several years, and TMTpro-16plex has recently been evaluated for TPP of vehicle- and drug-treated samples in a single labeling process to reduce missing values and save instrument time. A novel isobaric labeling reagent, IBT-16plex, was developed with slightly better performance in protein identification and quantification than the commercially available TMTpro-16plex.

METHODS

In this study, we applied the newly developed IBT-16plex for target identification of methotrexate and panobinostat using TPP.

RESULTS

The known targets of these two drugs were successfully identified with elevated melting temperatures, and some known off-targets and potential new off-targets were also identified.

CONCLUSIONS

IBT-16plex can be a cost-effective replacement for TMTpro-16plex for TPP applications.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Evaluating cPILOT Data toward Quality Control Implementation

Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.