SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering

ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer

Multibatch isobaric labeling experiments are frequently applied for clinical and pharmaceutical studies of large sample cohorts. To tackle the critical issue of missing values in such studies, we introduce the ProSIMSIt pipeline. It combines the advantages of tandem mass spectrum clustering via SIMSI-Transfer and data-driven rescoring via Prosit and Oktoberfest. We demonstrate that these two tools are complementary and mutually beneficial. On large-scale cancer cohort data, ProSIMSIt increased the number of peptide spectrum matches (PSMs) by 40% on both global and phosphoproteome data sets. Furthermore, on data from proteome-wide drug-response profiling of post-translational modifications (decryptM), our pipeline substantially increased drug-PTM relations and revealed previously unseen downstream effects of drug target inhibition. ProSIMSIt is available as an open-source Python package with a simple command line interface that allows easy application to MaxQuant result files.

Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response

The DNA-damaging agent Gemcitabine (GEM) is a first-line treatment for pancreatic cancer, but chemoresistance is frequently observed. Several clinical trials investigate the efficacy of GEM in combination with targeted drugs, including kinase inhibitors, but the experimental evidence for such rationale is often unclear. Here, we phenotypically screened 13 human pancreatic adenocarcinoma (PDAC) cell lines against GEM in combination with 146 clinical inhibitors and observed strong synergy for the ATR kinase inhibitor Elimusertib in most cell lines. Dose-dependent phosphoproteome profiling of four ATR inhibitors following DNA damage induction by GEM revealed a strong block of the DNA damage response pathway, including phosphorylated pS468 of CHEK1, as the underlying mechanism of drug synergy. The current work provides a strong rationale for why the combination of GEM and ATR inhibition may be useful for the treatment of PDAC patients and constitutes a rich phenotypic and molecular resource for further investigating effective drug combinations.

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

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.

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.

Hyperplexing Approaches for up to 45-Plex Quantitative Proteomic Analysis

Isobaric labeling has emerged as an indispensable quantitative proteomic approach for its unprecedented multiplexing capacity in a single analysis. Currently, different hyperplexing approaches have been developed to meet the demand for the increasing sample size in large-scale cohort analysis. In this report, we present a tribrid hyperplexing approach by the combinatorial use of three types of isobaric reagents, a novel isobaric tag 16-plex (IBT16) reagent and the widely used tandem mass tag (TMT; TMT11) and TMTpro (TMT18) reagents. After the determination of labeling efficiency and the optimization of testing conditions, we systematically evaluated the identification and quantification performance of the three labeling reagents in both independent and combinatorial manners using the mixtures of E. coli and HeLa peptides with different ratios. Our results reveal that the three reagents are quite similar in all testing aspects despite some differences, and the combination use of the three reagents could expand the multiplexing capacity to up to 45-plex. Furthermore, we conclude the advantages of IBT16 in the combination use and the preferred combinations for different practical applications. Data are available via ProteomeXchange with identifier PXD037498.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics

There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

TAG-TMTpro, a Hyperplexing Quantitative Approach for High-Throughput Proteomic Studies

Isobaric labeling is the most widely used multiplexing quantitative approach in proteomic studies, enabling the comparison of up to 18 samples in a single MS analysis. Expanding the multiplexing capacity is of great necessity for high-throughput proteomic studies. Herein, we establish a novel TAG-TMTpro approach by introducing Ala or Gly residues to peptides prior to TMTpro labeling, which is able to triple the quantitative capacity of TMTpro. We systematically evaluated the Boc-Ala-OSu and Boc-Gly-OSu reaction and optimized the conditions for labeling, side-product elimination, and Boc deprotection. We validated the identification and quantification performance using E. coli and HeLa cell lysates. We demonstrated that the TAG-TMTpro approach resulted in good identification reproducibility and reliable quantitative accuracy. The TAG-TMTpro is able to triple the multiplexing capacity of TMTpro reagents and is a versatile quantitative approach for high-throughput proteomic studies. Data are available via ProteomeXchange with identifier PXD033711.