Application Of Proteomics Research Articles

Single-Cell Chemical Proteomics (SCCP) Interrogates the Timing and Heterogeneity of Cancer Cell Commitment to Death.

Chemical proteomics studies the effects of drugs upon a cellular proteome. Due to the complexity and diversity of tumors, the response of cancer cells to drugs is also heterogeneous, and thus, proteome analysis at the single-cell level is needed. Here, we demonstrate that single-cell proteomics techniques have become quantitative enough to tackle the drug effects on target proteins, enabling single-cell chemical proteomics (SCCP). Using SCCP, we studied here the time-resolved response of individual adenocarcinoma A549 cells to anticancer drugs methotrexate, camptothecin, and tomudex, revealing the early emergence of cellular subpopulations committed and uncommitted to death. As a novel and useful approach to exploring the heterogeneous response to drugs of cancer cells, SCCP may prove to be a breakthrough application for single-cell proteomics.

Recent advances in isobaric labeling and applications in quantitative proteomics.

Mass spectrometry (MS) has emerged at the forefront of quantitative proteomic techniques. Liquid chromatography-mass spectrometry (LC-MS) can be used to determine abundances of proteins and peptides in complex biological samples. Several methods have been developed and adapted for accurate quantification based on chemical isotopic labeling. Among various chemical isotopic labeling techniques, isobaric tagging approaches rely on the analysis of peptides from MS2-based quantification rather than MS1-based quantification. In this review, we will provide an overview of several isobaric tags along with some recent developments including complementary ion tags, improvements in sensitive quantitation of analytes with lower abundance, strategies to increase multiplexing capabilities, and targeted analysis strategies. We will also discuss limitations of isobaric tags and approaches to alleviate these restrictions through bioinformatic tools and data acquisition methods. This review will highlight several applications of isobaric tags, including biomarker discovery and validation, thermal proteome profiling, cross-linking for structural investigations, single-cell analysis, top-down proteomics, along with applications to different molecules including neuropeptides, glycans, metabolites, and lipids, while providing considerations and evaluations to each application.

A proteomic atlas of atherosclerosis: regional proteomic signatures for plaque inflammation and calcification

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): This study was mainly supported from the British Heart Foundation (BHF) supporting Prof. Manuel Mayr as a Chair Holder (CH/16/3/32406) with BHF programme grant support (RG/16/14/32397) and Dr. Theofilatos with BHF programm grant support (G/20/10387). Background Using proteomics, we strove to reveal novel molecular subtypes of human atherosclerotic lesions, study their associations with histology and imaging and relate them to long-term cardiovascular outcomes. Methods 219 samples were obtained from 120 patients undergoing carotid endarterectomy. Sequential protein extraction was combined with multiplexed, discovery proteomics. Parallel reaction monitoring for 135 proteins was deployed for targeted validation. A combination of statistical, bioinformatics and machine learning methods was used to perform differential expression, network, pathway enrichment analysis and train and evaluate prognostic models. Results Our extensive proteomics analysis from the core and periphery of plaques doubled the coverage of the plaque proteome compared to the largest proteomics study on atherosclerosis thus far. Plaque inflammation and calcification signatures were inversely correlated and validated with targeted proteomics. The inflammation signature was enriched with neutrophil-derived proteins, including calprotectin (S100A8/9) and myeloperoxidase. The calcification signature contained fetuin-A, osteopontin, and gamma-carboxylated proteins. Sex differences in the proteome of atherosclerosis were explained by a higher proportion of calcified plaques in women. Single-cell RNA sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages and the calcification signature to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in the plaque, i.e., fetuin-A. Echogenic lesions reflect the collagen content and calcification of plaque but carotid Duplex ultrasound fails to capture the extent of inflammatory protein changes in symptomatic plaques. Applying dimensionality reduction and machine learning on the proteomics data defined 4 distinct plaque phenotypes and revealed key protein signatures linked to smooth muscle cell content, plaque calcification and structural extracellular matrix, which improved the 9-year prognostic AUC by 25% compared to ultrasound and histology. A biosignature of four proteins (CNN1, PROC, SERPH, and CSPG2) independently predicted the progression of atherosclerosis and cardiovascular mortality with an AUC of 75% Conclusion We combined discovery and targeted proteomics with network reconstruction and clustering techniques to provide molecular insights into protein changes in atherosclerotic plaques. The application of proteomics and machine learning techniques revealed distinct clusters of plaques that inform on disease progression and future adverse cardiovascular events.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

Understanding the Role of Surfactants in the Interaction and Hydrolysis of Myoglobin by Zr‐MOF‐808

AbstractControlled protein hydrolysis is an important procedure in proteomics applications and is used to aid the understanding of protein structure and function. The hydrolysis of hydrophobic proteins is particularly challenging, as due to their poor solubility the use of surfactants, which typically inactivate natural enzymes, is often required. Such limitations of natural enzymes prompted the development of chemical catalysts for the selective hydrolysis of proteins. In this study, the nanozymatic potential of MOF‐808, a Zr6O8 based metal organic framework, has been investigated towards protein hydrolysis in the presence of several surfactants which differ in structure and polarity. The influence of ionic SDS (sodium dodecyl sulfate), neutral TX‐100 (Triton X‐100) and zwitterionic Zw3‐12 (n‐dodecyl‐N,N‐dimethyl‐3‐ammonio‐1‐propanesulfonate) and CHAPS (3‐[(3‐cholamidopropyl)dimethylammonio]‐1‐propanesulfonate) surfactants on the hydrolysis of horse heart myoglobin in the presence of MOF‐808 has been examined. The hydrolysis of horse heart myoglobin by MOF‐808 was followed using sodium dodecyl sulfate poly(acrylamide) gel electrophoresis (SDS‐PAGE), which showed that nanozymatic activity of MOF‐808 can be tuned by using appropriate surfactants. To understand the observed reactivity patterns, the interactions between surfactant, MOF and protein were further investigated using a range of spectroscopic methods, which included Dynamic Light Scattering (DLS), 1H NMR, UV‐Vis, Circular Dichroism and UV‐Vis Spectroscopy. While the presence of SDS increased the number of observed peptide fragments due to protein unfolding and increased protein‐MOF interaction, the use of zwitterionic and neutral surfactant reduced the hydrolytic efficiency, most likely by hindering the efficient protein‐MOF interaction.

Dual roles of drug or its metabolite-protein conjugate: Cutting-edge strategy of drug discovery using shotgun proteomics.

Many drugs can bind directlyto proteins or be bioactivated by metabolizing enzymes to form reactive metabolites (RMs) that rapidly bind to proteins to form drug-protein conjugates or metabolite-protein conjugates (DMPCs). The close relationship between DMPCs and idiosyncratic adverse drug reactions (IADRs) has been recognized; drug discovery teams tend to avoid covalent interactions in drug discovery projects. Covalent interactions in DMPCs can provide high potency and long action duration and conquer the intractable targets, inspiring drug design, and development. This forms the dual role feature of DMPCs. Understanding the functional implications of DMPCs in IADR control and therapeutic applications requires precise identification of these conjugates from complex biological samples. While classical biochemical methods have contributed significantly to DMPC detection in the past decades, the low abundance and low coverage of DMPCs have become a bottleneck in this field. An emerging transformation towardshotgun proteomics is on the rise. The evolving shotgun proteomics techniques offer improved reproducibility, throughput, specificity, operability, and standardization. Here, we review recent progress in the systematic discovery of DMPCs using shotgun proteomics. Furthermore, the applications of shotgun proteomics supporting drug development, toxicity mechanism investigation, and drug repurposing processes are also reviewed and prospected.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine.

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

One-Pot Exosome Proteomics Enabled by a Photocleavable Surfactant

Exosomes are small extracellular vesicles (EVs) secreted by all cells and found in biological fluids, which can serve as minimally invasive liquid biopsies with extremely high therapeutic and diagnostic potential. Mass spectrometry (MS)-based proteomics is a powerful technique to profile and quantify the protein content in exosomes, but the current methods require laborious and time-consuming multistep sample preparation that significantly limit throughput. Herein, we report a one-pot exosome proteomics method enabled by a photocleavable surfactant, Azo, to simplify exosomal lysis, effectively extract proteins, and expedite digestion. We have applied this method to exosomes derived from isolated mammary fibroblasts and confidently identified 3466 proteins and quantified 2288 proteins using a reversed-phase liquid chromatography coupled to trapped ion mobility spectrometry (TIMS) quadrupole time-of-flight mass spectrometer. Here, 3166 (91%) of the identified proteins are annotated in the exosome/EVs databases, ExoCarta and Vesiclepedia, including important exosomal markers, CD63, PDCD6IP, and SDCBP. This method is fast, simple, and highly effective at extracting exosomal proteins with high reproducibility for deep exosomal proteome coverage. We envision that this method could be generally applicable for exosome proteomics applications in biomedical research, therapeutic interventions, and clinical diagnostics.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era.

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Ion-Based Proteome-Integrated Solubility Alteration Assays for Systemwide Profiling of Protein-Molecule Interactions.

Unbiased drug target engagement deconvolution and mechanism of action elucidation are major challenges in drug development. Modification-free target engagement methods, such as thermal proteome profiling, have gained increasing popularity in the last several years. However, these methods have limitations, and, in any case, new orthogonal approaches are needed. Here, we present a novel isothermal method for comprehensive characterization of protein solubility alterations using the effect on protein solubility of cations and anions in the Hofmeister series. We combine the ion-based protein precipitation approach with Proteome-Integrated Solubility Alteration (PISA) analysis and use this I-PISA assay to delineate the targets of several anticancer drugs both in cell lysates and intact cells. Finally, we demonstrate that I-PISA can detect solubility changes in minute amounts of sample, opening chemical proteomics applications to small and rare biological material.

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.

Preconcentration of Hemoglobin by a Nickel-Based Metal–Organic Framework (MOF) with Biphenyl Ligands

Adsorbents for protein enrichment underline the most important foundations in proteomic application. The construction of tunable metal-organic framework (MOF) through rational selection of metal nodes and organic linkers with designed properties has attracted special interest in the field of protein enrichment. In this work, a nickel-based MOF (NiBPDC) composed of biphenyl-based organic ligand and Ni clusters was prepared which exhibits attractive adsorption efficiency and selectivity for hemoglobin (Hb) by taking advantage of the strong metal affinity and π stacking interaction between Hb and MOF. The adsorption maximum capacity of NiBPDC for Hb is 400.2 mg g−1, and the adsorbed Hb is easily recovered by 0.5% (m/v) sodium dodecyl sulfate achieving a recovery of 98.2%. An enrichment factor up to 79.8 for Hb from an extremely 300:1 mass ratio of a human serum albumin/Hb mixture was achieved. Circular dichroism (CD) spectra reveal that the recovered Hb maintained its original structure following the enrichment. The practicability of NiBPDC is confirmed by selective enrichment of Hb from complex biological samples followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis.

Human peripheral blood mononuclear cells: A review of recent proteomic applications.

Human peripheral blood mononuclear cells (PBMCs) represent a sentinel blood sample which reacts to different pathophysiological stimuli in the form of immunological responses/immunophenotypic changes. The study of molecular content of PBMCs can provide better understanding of immune processes giving the possibility of monitoring the health conditions of the host organism. Proteomic analysis of PBMCs can achieve mentioned goal as important immune-related biomarkers are easily accessible for analysis. PBMCs have been gaining attention in different research areas including preclinical or clinical investigations. In this review, recent applications of proteomic analysis of PBMCs are described and discussed. Approaches are divided based on different proteomic workflows such as in-gel, in-solution and on-filter modes. The effect of various diseases such as autoimmune, cancer, neurodegenerative, viral, metabolic, and various immune stimulations such as radiation, vaccine, corticosteroids over PBMCs proteome, are described with emphasis on promising protein biomarker candidates.

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments.

We present the first detailed study of chromatographic behavior of peptides labeled with tandem mass tags (TMT and TMTpro) in 2D LC for proteomic applications. Carefully designed experimental procedures have permitted generating data sets of over 100,000 nonlabeled and TMT-labeled peptide pairs for the low pH RP in the second separation dimension and data sets of over 10,000 peptide pairs for high-pH RP, HILIC (amide and silica), and SCX separations in the first separation dimension. The average increase in peptide RPLC (0.1% formic acid) retention upon TMT labeling was found to be 3.3% acetonitrile (linear water/acetonitrile gradients), spanning a range of -4 to 10.3%. In addition to the bulk peptide properties such as length, hydrophobicity, and the number of labeled residues, we found several sequence-dependent features mostly associated with differences in N-terminal chemistry. The behavior of TMTpro-labeled peptides was found to be very similar except for a slightly higher hydrophobicity: an average retention shift of 3.7% acetonitrile. The respective versions of the sequence-specific retention calculator (SSRCalc) model have been developed to accommodate both TMT chemistries, showing identical prediction accuracy (R2 ∼ 0.98) for labeled and nonlabeled peptides. Higher retention for TMT-labeled peptides was observed for high-pH RP and HILIC separations, while SCX selectivity remained virtually unchanged.

ADH1C inhibits progression of colorectal cancer through the ADH1C/PHGDH /PSAT1/serine metabolic pathway.

Colorectal cancer (CRC) is the third most common cancer in men and the second most common cancer in women worldwide. CRC is the second leading cause of cancer-related deaths. Although some progress in the treatment of CRC has been achieved, the molecular mechanism of CRC is still unclear. In this study, alcohol dehydrogenase 1C(ADH1C) was first identified as a target gene closely associated with the development of CRC by the comprehensive application of transcriptomics, proteomics, metabonomics and insilico analysis. The ADH1C mRNA and protein expression in CRC cell lines and tumor tissues was lower than that in normal intestinal epithelial cell lines and healthy tissues. Overexpression of ADH1C inhibited the growth, migration, invasion and colony formation of CRC cell lines and prevented the growth of xenograft tumors in nude mice. The inhibitory effects of ADH1C on CRC cells in vitro were exerted by reducing the expression of PHGDH/PSAT1 and the serine level. This inhibition could be partially reversed by adding serine to the culture medium. These results showed that ADH1C is a potential drug target in CRC.

Advances in proteomic profiling of pediatric kidney diseases.

Chronic kidney disease(CKD) can progress to kidney failure and require dialysis or transplantation, while early diagnosis can alter the course of disease and lead to better outcomes in both pediatric and adult patients. Significant CKD comorbidities include the manifestation of cardiovascular disease, heart failure, coronary disease, and hypertension. The pathogenesis of chronic kidney diseases can present as subtle and especially difficult to distinguish between different glomerular pathologies. Early detection of adult and pediatric CKD and detailed mechanistic understanding of the kidney damage can be helpful in delaying or curtailing disease progression via precise intervention toward diagnosis and prognosis. Clinically, serum creatinine and albumin levels can be indicative of CKD, but often are a lagging indicator only significantly affected once kidney function has severely diminished. The evolution of proteomics and mass spectrometry technologies has begun to provide a powerful research tool in defining these mechanisms and identifying novel biomarkers of CKD. Many of the same challenges and advances in proteomics apply to adult and pediatric patient populations. Additionally, proteomic analysis of adult CKD patients can be transferred directly toward advancing our knowledge of pediatric CKD as well. In this review, we highlight applications of proteomics that have yielded such biomarkers as PLA2R, SEMA3B, and other markers of membranous nephropathy as well as KIM-1, MCP-1, and NGAL in lupus nephritis among other potential diagnostic and prognostic markers. The potential for improving the clinical toolkit toward better treatment of pediatric kidney diseases is significantly aided by current and future development of proteomic applications.

A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.

Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning.

Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers.

Radiation therapy remains an important component of cancer treatment. Gene-encoded proteins were the actual executors of cellular functions. Proteomic was a novel technology that can systematically analysis protein composition and measure their levels of change, this was a high throughput method, and were the import tools in the post genomic era. In recent years, rapid progress of proteomic have been made in the study of cancer mechanism, diagnosis, and treatment. This article elaborates current advances and future directions of proteomics in the discovery of radiosensitive cancer biomarkers.

Optical Sensors and Actuators for Probing Proximity-Dependent Biotinylation in Living Cells.

Proximity-dependent biotinylation techniques have been gaining wide applications in the systematic analysis of protein-protein interactions (PPIs) on a proteome-wide scale in living cells. The engineered biotin ligase TurboID is among the most widely adopted given its enhanced biotinylation efficiency, but it faces the background biotinylation complication that might confound proteomic data interpretation. To address this issue, we report herein a set of split TurboID variants that can be reversibly assembled by using light (designated “OptoID”), which enable optogenetic control of biotinylation based proximity labeling in living cells. OptoID could be further coupled with an engineered monomeric streptavidin that permits real-time monitoring of biotinylation with high temporal precision. These optical actuators and sensors will likely find broad applications in precise proximity proteomics and rapid detection of biotinylation in living cells.

An Integrative Proteome-Based Pharmacologic Characterization and Therapeutic Strategy Exploration of SAHA in Solid Malignancies.

Targeting histone epigenetic modification is an important strategy for anticancer therapy. Histone deacetylase inhibitors (HDACis) have been clinically approved in the treatment of diverse hematological cancers, but mechanisms of drug resistance and poor therapeutic efficacy in solid malignancies remain largely unknown. In this study, we applied a mass spectrometry-based quantitative proteomic strategy to investigate the molecular differences in HDACi vorinostat (SAHA) sensitive and resistant cell lines. The proteomic results revealed that the glycolysis pathway was highly enriched after vorinostat treatment in the resistant cell line, leading to the prediction of a new drug combination, SAHA and hexokinase inhibitor (2-deoxyglucose). The efficacy of this combination was further verified in several solid tumor cell lines. Quantitative proteomics revealed that alterations in the transcription process and protein homeostasis could play roles in the synergetic utilization of these two compounds. Our study showed the application of proteomics in elucidating the drug mechanism and predicting drug combination and the potential of expanding the utilization of HDACi.