Mass Spectrometry Applied to Bottom-Up Proteomics: Entering the High-Throughput Era for Hypothesis Testing

Surrogate peptide selection and internal standardization for accurate quantification of endogenous proteins

Relative quantification techniques have dominated the field of proteomics. However, biomarker discovery, mathematical model development and studies on transporter-mediated drug disposition still need absolute quantification of proteins. The quality of data of trace-level protein quantification is solely dependent on the specific selection of surrogate peptides. Selection of surrogate peptides has a major impact on the accuracy of the method. In this article, the advanced approaches for selection of surrogate peptides, which can provide absolute quantification of the proteins are discussed. In addition, internal standardization, which accounts for variations in the quantitation process to achieve absolute protein quantification is discussed.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

The crosstalk signals of Sodium Tanshinone ⅡA Sulfonate in rats with cerebral ischemic stroke: Insights from proteomics

BACKGROUND

Stroke could cause long-term disability, even mortality around the world. Recently, Sodium tanshinone IIA sulfonate (STS), identified from Salvia miltiorrhiza Bunge and was found to have unique efficiency in clinical practice as a potential therapeutic agent for ischemic cerebral infarction. However, systematic investigation about the biological mechanism is still lacking. Herein, we utilized high-throughput proteomics approach to identify the underlying targets for the treatment of STS in stroke.

METHODS

We investigated the effect of STS on stroke outcomes on rat model of the Middle Cerebral Artery Occlusion and Reperfusion (MCAO/R), assessing by Z-Longa score, infarct volume and HE staining. Pharmacoproteomic profiling of ischemic penumbra in cortical (IPC) was performed using DIA-based label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) technique. Bioinformatics analysis was processed for further investigation. The expression of core proteins was semi-quantified by DIA, and the major protein correlating with stroke was examined using parallel reaction monitoring (PRM).

RESULTS

Rats in the MCAO/R group showed neurological function deterioration, which was improved by STS. There were 423 differentially expressed proteins (DEPs) in IPC being detected and quantified in both the sham group and the MCAO/R group. Meanwhile, 285 proteins were significantly changed in the STS treated group, compared to the MCAO/R model. Protein-protein interaction (PPI) network, pathway and biological function enrichment were processed for the DEPs across each two groups, the results of which were integrated for analysis. Alb, mTOR, Dync1h1, Stxbp1, Cltc, and Sptan1 were contained as the core proteins. Altered molecules were discovered to be enriched in 18 signal pathways such as phosphatidylinositol signaling system, PI3K/AKT signal pathway and HIF-1 signal pathway. The results also showed the correlation with sleep disturbances and depression post-stroke.

CONCLUSIONS

We concluded that STS could prevent penumbra from progressively ongoing damage and improve neurological deficits in MCAO/R model rats. The intersected pathways and protein networks predicted by proteomics might provide much more detailed information for the therapeutic mechanisms of STS in the treatment of CIS.

Infrared Laser Ablation Microsampling for Small Volume Proteomics

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm², respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm² regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

Formation of Gaseous Peptide Ions from Electrospray Droplets: Competition between the Ion Evaporation Mechanism and Charged Residue Mechanism

The transfer of peptide ions from solution into the gas phase by electrospray ionization (ESI) is an integral component of mass spectrometry (MS)-based proteomics. The mechanisms whereby gaseous peptide ions are released from charged ESI nanodroplets remain unclear. This is in contrast to intact protein ESI, which has been the focus of detailed investigations using molecular dynamics (MD) simulations and other methods. Under acidic liquid chromatography/MS conditions, many peptides carry a solution charge of 3+ or 2+. Because of this pre-existing charge and their relatively small size, prevailing views suggest that peptides follow the ion evaporation mechanism (IEM). The IEM entails analyte ejection from ESI droplets, driven by electrostatic repulsion between the analyte and droplet. Surprisingly, recent peptide MD investigations reported a different behavior, that is, the release of peptide ions via droplet evaporation to dryness which represents the hallmark of the charged residue mechanism (CRM). Here, we resolved this conundrum by performing MD simulations on a common model peptide (bradykinin) in Rayleigh-charged aqueous droplets. The primary focus was on pH 2 conditions (bradykinin solution charge = 3+), but we also verified that our MD strategy captured pH-dependent charge state shifts seen in ESI-MS experiments. In agreement with earlier simulations, we found that droplets with initial radii of 1.5-3 nm predominantly release peptide ions via the CRM. In contrast, somewhat larger radii (4-5 nm) favor IEM behavior. It appears that these are the first MD data to unequivocally demonstrate the viability of peptide IEM events. Electrostatic arguments can account for the observed droplet size dependence. In summary, both CRM and IEM can be operative in peptide ESI-MS. The prevalence of one over the other mechanism depends on the droplet size distribution in the ESI plume.

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.

Pytheas: a software package for the automated analysis of RNA sequences and modifications via tandem mass spectrometry

Mass spectrometry is an important method for analysis of modified nucleosides ubiquitously present in cellular RNAs, in particular for ribosomal and transfer RNAs that play crucial roles in mRNA translation and decoding. Furthermore, modifications have effect on the lifetimes of nucleic acids in plasma and cells and are consequently incorporated into RNA therapeutics. To provide an analytical tool for sequence characterization of modified RNAs, we developed Pytheas, an open-source software package for automated analysis of tandem MS data for RNA. The main features of Pytheas are flexible handling of isotope labeling and RNA modifications, with false discovery rate statistical validation based on sequence decoys. We demonstrate bottom-up mass spectrometry characterization of diverse RNA sequences, with broad applications in the biology of stable RNAs, and quality control of RNA therapeutics and mRNA vaccines.

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

An Immunoproteomic Survey of the Antibody Landscape: Insights and Opportunities Revealed by Serological Repertoire Profiling

Immunoproteomics has emerged as a versatile tool for analyzing the antibody repertoire in various disease contexts. Until recently, characterization of antibody molecules in biological fluids was limited to bulk serology, which identifies clinically relevant features of polyclonal antibody responses. The past decade, however, has seen the rise of mass-spectrometry-enabled proteomics methods that have allowed profiling of the antibody response at the molecular level, with the disease-specific serological repertoire elucidated in unprecedented detail. In this review, we present an up-to-date survey of insights into the disease-specific immunological repertoire by examining how quantitative proteomics-based approaches have shed light on the humoral immune response to infection and vaccination in pathogenic illnesses, the molecular basis of autoimmune disease, and the tumor-specific repertoire in cancer. We address limitations of this technology with a focus on emerging potential solutions and discuss the promise of high-resolution immunoproteomics in therapeutic discovery and novel vaccine design.

A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry

Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry

Single-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.

Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot

Cell signaling governs the basic functions of cells by molecular interactions that involve of many proteins. The abundance of signaling proteins can directly influence cellular responses to external signal, contributing to cellular heterogeneity. Absolute quantification of proteins is important for modeling and understanding the complex signaling network. Here, we introduce how to measure the amount of TGF-β signaling proteins using quantitative immunoblotting. In addition, we discuss how to convert the measurements of protein abundance to the quantities of absolute molecules per cell. This method is generally applicable to the absolute quantification of other proteins.

Automating Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi

Redox proteomics plays an increasingly important role characterizing the cellular redox state and redox signaling networks. As these datasets grow larger and identify more redox regulated sites in proteins, they provide a systems-wide characterization of redox regulation across cellular organelles and regulatory networks. However, these large proteomic datasets require substantial data processing and analysis in order to fully interpret and comprehend the biological impact of oxidative posttranslational modifications. We therefore developed ProteoSushi, a software tool to biologically annotate and quantify redox proteomics and other modification-specific proteomics datasets. ProteoSushi can be applied to differentially alkylated samples to assay overall cysteine oxidation, chemically labeled samples such as those used to profile the cysteine sulfenome, or any oxidative posttranslational modification on any residue.Here we demonstrate how to use ProteoSushi to analyze a large, public cysteine redox proteomics dataset. ProteoSushi assigns each modified peptide to shared proteins and genes, sums or averages signal intensities for each modified site of interest, and annotates each modified site with the most up-to-date biological information available from UniProt. These biological annotations include known functional roles or modifications of the site, the protein domain(s) that the site resides in, the protein's subcellular location and function, and more.

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters

Data-independent acquisition (DIA) proteomics is a recently-developed global mass spectrometry (MS)-based proteomics strategy. In a DIA method, precursor ions are isolated into pre-defined isolation windows and fragmented; all fragmented ions in each window are then analyzed by a high-resolution mass spectrometer. DIA proteomics analysis is characterized by a broad protein coverage, high reproducibility, and accuracy, and its combination with advances in other techniques such as sample preparation and computational data analysis could lead to further improvements in assay performances. DIA technology has been increasingly utilized in various proteomics studies, including quantifying drug-metabolizing enzymes and transporters. Quantitative proteomics study of drug-metabolizing enzymes and transporters could lead to a better understanding of pharmacokinetics and pharmacodynamics and facilitate drug development. This review summarizes the application of DIA technology in proteomic analysis of drug-metabolizing enzymes and transporters.

Mass Spectrometry-Based Phosphoproteomics and Systems Biology: Approaches to Study T Lymphocyte Activation and Exhaustion

T lymphocytes respond to extracellular cues and recognize and clear foreign bodies. These functions are tightly regulated by receptor-mediated intracellular signal transduction pathways and phosphorylation cascades resulting in rewiring of transcription, cell adhesion, and metabolic pathways, which leads to changes in downstream effector functions including cytokine secretion and target-cell killing. Given that these pathways become dysregulated in chronic diseases such as cancer, auto-immunity, diabetes, and persistent infections, mapping T cell signaling dynamics in normal and pathological states is central to understanding and modulating immune system behavior. Despite recent advances, there remains much to be learned from the study of T cell signaling at a systems level. The application of global phospho-proteomic profiling technology has the potential to provide unprecedented insights into the molecular networks that govern T cell function. These include capturing the spatiotemporal dynamics of the T cell responses as an ensemble of interacting components, rather than a static view at a single point in time. In this review, we describe innovative experimental approaches to study signaling mechanisms in the TCR, co-stimulatory receptors, synthetic signaling molecules such as chimeric antigen receptors, inhibitory receptors, and T cell exhaustion. Technical advances in mass spectrometry and systems biology frameworks are emphasized as these are poised to identify currently unknown functional relationships and dependencies to create causal predictive models that expand from the traditional narrow reductionist lens of singular components in isolation.

A protein-centric view of in vitro biological model systems for schizophrenia

Schizophrenia (SCZ) is a severe brain disorder, characterized by psychotic, negative, and cognitive symptoms, affecting 1% of the population worldwide. The precise etiology of SCZ is still unknown; however, SCZ has a high heritability and is associated with genetic, environmental, and social risk factors. Even though the genetic contribution is indisputable, the discrepancies between transcriptomics and proteomics in brain tissues are consistently challenging the field to decipher the disease pathology. Here we provide an overview of the state of the art of neuronal two-dimensional and three-dimensional model systems that can be combined with proteomics analyses to decipher specific brain pathology and detection of alternative entry points for drug development.

A prostate cancer tissue specific spectral library for targeted proteomic analysis

Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.

Innovation in drug toxicology: Application of mass spectrometry imaging technology

Mass spectrometry imaging (MSI) is a powerful molecular imaging technology that can obtain qualitative, quantitative, and location information by simultaneously detecting and mapping endogenous or exogenous molecules in biological tissue slices without specific chemical labeling or complex sample pretreatment. This article reviews the progress made in MSI and its application in drug toxicology research, including the tissue distribution of toxic drugs and their metabolites, the target organs (liver, kidney, lung, eye, and central nervous system) of toxic drugs, the discovery of toxicity-associated biomarkers, and explanations of the mechanisms of drug toxicity when MSI is combined with the cutting-edge omics methodologies. The unique advantages and broad prospects of this technology have been fully demonstrated to further promote its wider use in the field of pharmaceutical toxicology.

Serum KLKB1 as a Potential Prognostic Biomarker for Hepatocellular Carcinoma Based on Data-Independent Acquisition and Parallel Reaction Monitoring

Purpose

With the advancement of minimally invasive surgery and catheters for hepatocellular carcinoma (HCC), it is becoming more and more inconvenient to get tissues or the tissues gained are insufficient for testing. Screening of blood-derived markers is of great significance for prognosis assessment.

Patients and Methods

Data-independent acquisition (DIA) and parallel reaction monitoring (PRM) were implemented to identify valuable prognostic HCC biomarkers in 48 patients with different prognosis. The potential candidate biomarkers were examined in 205 HCC patients using enzyme-linked immunosorbent assay (ELISA) and then validated in The Cancer Genome Atlas (TCGA) HCC cohort.

Results

DIA screened 86 significantly differentially regulated proteins between patients with poor prognosis and those with good prognosis. Eight proteins from the DIA proteomic analyses were quantified by PRM, and six of them (KLKB1, IGFBP3, SHBG, SAA1, C7, and CD44) presented consistent expression trends between DIA and PRM. Then, the results of ELISA indicated that KLKB1 was abnormally expressed in HCC patients, and the serum level of KLKB1 also exhibited significant changes before and after treatment (P = 0.016). Patients with higher KLKB1 serum levels had significantly superior overall survival (P = 0.035) and progression-free survival (P = 0.027) than those with lower KLKB1 expression. In the TCGA-HCC cohort, Cox regression analysis suggested that KLKB1 was an independent prognostic factor for overall survival (P = 0.032) of HCC patients.

Conclusion

Aberrant expression of KLKB1 was strongly associated with the prognosis of HCC patients. KLKB1 may be used to evaluate the prognosis and guide the treatment for HCC.

Data-independent acquisition mass spectrometry for site-specific glycoproteomics characterization of SARS-CoV-2 spike protein

The spike protein of SARS-CoV-2, the virus responsible for the global pandemic of COVID-19, is an abundant, heavily glycosylated surface protein that plays a key role in receptor binding and host cell fusion, and is the focus of all current vaccine development efforts. Variants of concern are now circulating worldwide that exhibit mutations in the spike protein. Protein sequence and glycosylation variations of the spike may affect viral fitness, antigenicity, and immune evasion. Global surveillance of the virus currently involves genome sequencing, but tracking emerging variants should include quantitative measurement of changes in site-specific glycosylation as well. In this work, we used data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry to quantitatively characterize the five N-linked glycosylation sites of the glycoprotein standard alpha-1-acid glycoprotein (AGP), as well as the 22 sites of the SARS-CoV-2 spike protein. We found that DIA compared favorably to DDA in sensitivity, resulting in more assignments of low-abundance glycopeptides. However, the reproducibility across replicates of DIA-identified glycopeptides was lower than that of DDA, possibly due to the difficulty of reliably assigning low-abundance glycopeptides confidently. The differences in the data acquired between the two methods suggest that DIA outperforms DDA in terms of glycoprotein coverage but that overall performance is a balance of sensitivity, selectivity, and statistical confidence in glycoproteomics. We assert that these analytical and bioinformatics methods for assigning and quantifying glycoforms would benefit the process of tracking viral variants as well as for vaccine development.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1-Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

Protein complexes and neighborhoods driving autophagy

Autophagy summarizes evolutionarily conserved, intracellular degradation processes targeting cytoplasmic material for lysosomal degradation. These encompass constitutive processes as well as stress responses, which are often found dysregulated in diseases. Autophagy pathways help in the clearance of damaged organelles, protein aggregates and macromolecules, mediating their recycling and maintaining cellular homeostasis. Protein-protein interaction networks contribute to autophagosome biogenesis, substrate loading, vesicular trafficking and fusion, protein translocations across membranes and degradation in lysosomes. Hypothesis-free proteomic approaches tremendously helped in the functional characterization of protein-protein interactions to uncover molecular mechanisms regulating autophagy. In this review, we elaborate on the importance of understanding protein-protein-interactions of varying affinities and on the strengths of mass spectrometry-based proteomic approaches to study these, generating new mechanistic insights into autophagy regulation. We discuss in detail affinity purification approaches and recent developments in proximity labeling coupled to mass spectrometry, which uncovered molecular principles of autophagy mechanisms.Abbreviations: AMPK: AMP-activated protein kinase; AP-MS: affinity purification-mass spectrometry; APEX2: ascorbate peroxidase-2; ATG: autophagy related; BioID: proximity-dependent biotin identification; ER: endoplasmic reticulum; GFP: green fluorescent protein; iTRAQ: isobaric tag for relative and absolute quantification; MS: mass spectrometry; PCA: protein-fragment complementation assay; PL-MS: proximity labeling-mass spectrometry; PtdIns3P: phosphatidylinositol-3-phosphate; PTM: posttranslational modification; PUP-IT: pupylation-based interaction tagging; RFP: red fluorescent protein; SILAC: stable isotope labeling by amino acids in cell culture; TAP: tandem affinity purification; TMT: tandem mass tag.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Mimicking LysC Proteolysis by 'Arginine-Modification-cum-Trypsin digestion': Comparison of Bottom-Up & Middle-Down Proteomic Approaches by ESI-QTOF-MS

BACKGROUND

Middle-down (MD) proteomics is an emerging approach for reliable identification of post- translational modifications and isoforms, as this approach focuses on proteolytic peptides containing > 25 - 30 amino acid residues (a.a.r.), which are longer than typical tryptic peptides. Such longer peptides can be obtained by AspN, GluC, LysC proteases. Additionally, some special proteases were developed specifically to effect MD approach, e.g., OmpT, Sap9, etc. However, these proteases are expensive. Herein we report a cost-effective strategy, 'arginine modification-cum trypsin digestion', which can produce longer tryptic peptides resembling LysC peptides derived from proteins.

OBJECTIVE

To obtain proteolytic peptides that resemble LysC peptides, by using 'trypsin', which is an less expensive protease.

METHODS

This strategy is based on the simple principle that trypsin cannot act at the C-termini of those arginines in proteins, whose sidechain guanidine groups are modified by 1,2-cyclohexanedione or phenylglyoxal.

RESULTS

As a proof of concept, we demonstrate this strategy on four models: β-casein (bovine), β- lactoglobulin (bovine), ovalbumin (chick) and transferrin (human), by electrospray ionization-mass spectrometry (ESI-MS) involving hybrid quadrupole time-of-flight. From the ESI-MS of these models, we obtained several arginine modified tryptic peptides, whose lengths are in the range, 30 - 60 a.a.r. The collision-induced dissociation MS/MS characteristics of some of the arginine modified longer tryptic peptides are compared with the unmodified standard tryptic peptides.

CONCLUSION

The strategy followed in this proof-of-concept study, not only helps in obtaining longer tryptic peptides that mimic LysC proteolytic peptides, but also facilitates in enhancing the probability of missed cleavages by the trypsin. Hence, this method aids in evading the possibility of obtaining very short peptides that are < 5 - 10 a.a.r. Therefore, this is indeed an cost-effective alternative/substitute for LysC proteolysis and in turn, for those MD proteomic studies that utilize LysC. Additionally, this methodology can be fruitful for mass spectrometry based de novo protein and peptide sequencing.

Omics in the detection and identification of biosynthetic pathways related to mycotoxin synthesis

Mycotoxins are secondary metabolites that are known to be toxic to humans and animals. On the other hand, some mycotoxins and their analogues possess antioxidant as well as antitumor properties, which could be relevant in the fields of pharmaceutical analysis and food research. Omics techniques are a group of analytical tools applied in the biological sciences in order to study genes (genomics), mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics). Omics have become a vital tool in the field of mycotoxins, especially contributing to the identification of biomarkers with potential use for the detection of mycotoxigenic species and the gathering of information about the biosynthetic pathways of mycotoxins in different environments. This approach has provided tools for the development of prevention strategies and control measures for different mycotoxins. Additionally, research has revealed important information about the impact of global warming and climate change on the prevalence of mycotoxin issues in society. In the context of foodomics, the aim is to apply omics techniques in order to ensure food safety. The objective of the present review is to determine the state of the art regarding the development of analytical techniques based on omics in the identification of biosynthetic pathways related to mycotoxin synthesis.

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

A Fungal Secretome Adapted for Stress Enabled a Radical Wood Decay Mechanism

Brown rot fungi release massive amounts of carbon from forest deadwood, particularly at high latitudes. These fungi degrade wood by generating small reactive oxygen species (ROS) to loosen lignocellulose, to then selectively remove carbohydrates. The ROS mechanism has long been considered the key adaptation defining brown rot wood decomposition, but recently, we found preliminary evidence that fungal glycoside hydrolases (GHs) implicated in early cell wall loosening might have been adapted to tolerate ROS stress and to synergize with ROS to loosen woody lignocellulose. In the current study, we found more specifically that side chain hemicellulases that help in the early deconstruction of the lignocellulosic complex are significantly more tolerant of ROS in the brown rot fungus Rhodonia placenta than in a white rot fungus (Trametes versicolor) and a soft rot fungus (Trichoderma reesei). Using proteomics to understand the extent of tolerance, we found that significant oxidation of secreted R. placenta proteins exposed to ROS was less than half of the oxidation observed for T. versicolor or T. reesei. The principal oxidative modifications observed in all cases were monooxidation and dioxidation/trioxidation (mainly in methionine and tryptophan residues), some of which were critical for enzyme activity. At the peptide level, we found that GHs in R. placenta were the least ROS affected among our tested fungi. These results confirm and describe underlying mechanisms of tolerance in early-secreted brown rot fungal hemicellulases. These enzymatic adaptations may have been as important as nonenzymatic ROS pathway adaptations in brown rot fungal evolution.

Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro

Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

Evaluation of protein extraction methods for in-depth proteome analysis of narrow-leafed lupin (Lupinus angustifolius) seeds

Lupin is slated as a potential contributor towards future food security. Lupin possesses several nutritional and nutraceutical attributes, many linked to seed proteins. For in-depth characterisation of the lupin proteome, liquid chromatography-tandem mass spectrometry was used to evaluate four protein extraction procedures. The proteomes of three narrow-leafed lupin were qualitatively evaluated using protein/peptide identifications and further quantitatively assessed by data-independent proteome measurement. Each extraction buffer led to unique protein identifications; altogether yielding 2,760 protein identifications from lupin varieties. The analysis of protein abundance data highlighted distinct differences between Tris-HCl and urea extracted proteomes, while also revealing variation amongst the cultivar proteomes with the wild accession (P27255) distinctly different from the domesticated cultivars (Tanjil, Unicrop). The extraction buffer used influenced the proteome coverage, downstream functional annotation results and consequently the biological interpretation demonstrating the need to optimise and understand the impact of protein extraction conditions.

Coupling proteomics and metabolomics for the unsupervised identification of protein-metabolite interactions in Chaetomium thermophilum

Protein-metabolite interactions play an important role in the cell's metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.

Synthetic Fibrin-Derived Bβ15-42 Peptide Delays Thrombus Resolution in a Mouse Model

[Figure: see text].

The Acute Stress Response in the Multiomic Era

Studying the stress response is a major pillar of neuroscience research not only because stress is a daily reality but also because the exquisitely fine-tuned bodily changes triggered by stress are a neuroendocrinological marvel. While the genome-wide changes induced by chronic stress have been extensively studied, we know surprisingly little about the complex molecular cascades triggered by acute stressors, the building blocks of chronic stress. The acute stress (or fight-or-flight) response mobilizes organismal energy resources to meet situational demands. However, successful stress coping also requires the efficient termination of the stress response. Maladaptive coping-particularly in response to severe or repeated stressors-can lead to allostatic (over)load, causing wear and tear on tissues, exhaustion, and disease. We propose that deep molecular profiling of the changes triggered by acute stressors could provide molecular correlates for allostatic load and predict healthy or maladaptive stress responses. We present a theoretical framework to interpret multiomic data in light of energy homeostasis and activity-dependent gene regulation, and we review the signaling cascades and molecular changes rapidly induced by acute stress in different cell types in the brain. In addition, we review and reanalyze recent data from multiomic screens conducted mainly in the rodent hippocampus and amygdala after acute psychophysical stressors. We identify challenges surrounding experimental design and data analysis, and we highlight promising new research directions to better understand the stress response on a multiomic level.

Structural Characterization of Carbonic Anhydrase-Arylsulfonamide Complexes Using Ultraviolet Photodissociation Mass Spectrometry

Numerous mass spectrometry-based strategies ranging from hydrogen-deuterium exchange to ion mobility to native mass spectrometry have been developed to advance biophysical and structural characterization of protein conformations and determination of protein-ligand interactions. In this study, we focus on the use of ultraviolet photodissociation (UVPD) to examine the structure of human carbonic anhydrase II (hCAII) and its interactions with arylsulfonamide inhibitors. Carbonic anhydrase, which catalyzes the conversion of carbon dioxide to bicarbonate, has been the target of countless thermodynamic and kinetic studies owing to its well-characterized active site, binding cavity, and mechanism of inhibition by hundreds of ligands. Here, we showcase the application of UVPD for evaluating structural changes of hCAII upon ligand binding on the basis of variations in fragmentation of hCAII versus hCAII-arylsulfonamide complexes, particularly focusing on the hydrophobic pocket. To extend the coverage in the midregion of the protein sequence, a supercharging agent was added to the solutions to increase the charge states of the complexes. The three arylsulfonamides examined in this study largely shift the fragmentation patterns in similar ways, despite their differences in binding affinities.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Mass spectrometric investigations of caloric restriction mimetics

Caloric restriction (CR) is an innovative therapy used in tumor tissue and tumor model studies to promote cell death and decrease cell viability. Caloric restriction mimetics (CRMs) are a class of drugs that induce CR and starvation conditions within a cell. When used simultaneously with other chemotherapy agents, the effects are synergistic and effective at promoting tumor cell death. In this review, we discuss CRMs and their potential as cancer therapeutics. Firstly, we establish an overview of CR and its impacts on healthy and tumor cells. CR and CRM drugs have shown to decrease age-related diseases and can act as an anti-cancer agent. As it can be challenging for an individual to diligently stick to a diet that would induce CR, CRMs are even more desirable. Then, we discuss the drug class by highlighting three CRMs: resveratrol, (-)-hydroxycitric acid, and rapamycin. These CRMs are commonly known for their dietary effects, but the underlying mechanisms that drive cellular metabolic and proteomic changes show promise as a cancer therapeutic. Lastly, we highlight the use of mass spectrometry and proteomic techniques on experiments utilizing CRM drugs to understand the cellular pathways impacted by this drug class, leading to a better understanding of the anti-cancer properties and potentials of CRM.

Generation of a mouse SWATH-MS spectral library to quantify 10148 proteins involved in cell reprogramming

Murine models are amongst the most widely used systems to study biology and pathology. Targeted quantitative proteomic analysis is a relatively new tool to interrogate such systems. Recently the need for relative quantification on hundreds to thousands of samples has driven the development of Data Independent Acquisition methods. One such technique is SWATH-MS, which in the main requires prior acquisition of mass spectra to generate an assay reference library. In stem cell research, it has been shown pluripotency can be induced starting with a fibroblast population. In so doing major changes in expressed proteins is inevitable. Here we have created a reference library to underpin such studies. This is inclusive of an extensively documented script to enable replication of library generation from the raw data. The documented script facilitates reuse of data and adaptation of the library to novel applications. The resulting library provides deep coverage of the mouse proteome. The library covers 29519 proteins (53% of the proteome) of which 7435 (13%) are supported by a proteotypic peptide.

Administration of isoliquiritigenin prevents nonalcoholic fatty liver disease through a novel IQGAP2-CREB-SIRT1 axis

Isoliquiritigenin (ISO) is a flavonoid extracted from the root of licorice, which serves various biological and pharmacological functions including antiinflammatory, antioxidation, liver protection, and heart protection. However, the mechanism of its action remains elusive and the direct target proteins of ISO have not been identified so far. Through cell-based screening, we identified ISO as a potent lipid-lowering compound. ISO treatment successfully ameliorated fatty acid-induced cellular lipid accumulation and improved nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) by increasing PPARα-dependent lipid oxidation and decreasing SREBPs-dependent lipid synthesis. Both these signaling required the activation of SIRT1. Knockdown of SIRT1 resulted in the reversal of ISO beneficiary effects suggesting that the lipid-lowering activity of ISO was regulated by SIRT1 expression. To identify the direct target of ISO, limited proteolysis combined with mass spectrometry (LiP-SMap) strategy was applied and IQGAP2 was identified as the direct target for ISO in regulating lipid homeostasis. In the presence of ISO, both mRNA and protein levels of SIRT1 were increased; however, this effect was abolished by blocking IQGAP2 expression using siRNA. To explore how IQGAP2 regulated the expression level of SIRT1, proteome profiler human phospho-kinase array kit was used to reveal possible phosphorylated kinases and signaling nodes that ISO affected. We found that through phosphorylation of CREB, ISO transduced signals from IQGAP2 to upregulate SIRT1 expression. Thus, we not only demonstrated the molecular basis of ISO in regulating lipid metabolism but also exhibited for the first time a novel IQGAP2-CREB-SIRT1 axis in treating NAFLD/NASH.

Bioactive Molecular Discovery Using Deer Antlers as a Model of Mammalian Regeneration

The ability to activate and regulate stem cells during wound healing and tissue regeneration is a promising field that is resulting in innovative approaches in the field of regenerative medicine. The regenerative capacity of invertebrates has been well documented; however, in mammals, stem cells that drive organ regeneration are rare. Deer antlers are the only known mammalian structure that can annually regenerate to produce a tissue containing dermis, blood vessels, nerves, cartilage, and bone. The neural crest derived stem cells that drive this process result in antlers growing at up to 2 cm/day. Deer antlers thus provide superior attributes compared to lower-order animal models, when investigating the regulation of stem cell-based regeneration. Antler stem cells can therefore be used as a model to investigate the bioactive molecules, biological processes, and pathways involved in the maintenance of a stem cell niche, and their activation and differentiation during organ formation. This review examines stem cell-based regeneration with a focus on deer antlers, a neural crest stem cell-based mammalian regenerative structure. It then discusses the omics technical platforms highlighting the proteomics approaches used for investigating the molecular mechanisms underlying stem cell regulation in antler tissues.

MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB

Protein sequence databases play a crucial role in the majority of the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB serves as one of the major sources, as it combines the information of several smaller databases and enriches the entries with additional biological information. For the identification of peptides in a sample by tandem mass spectra, as generated by data-dependent acquisition, protein sequence databases provide the basis for most spectrum identification search engines. In addition, for targeted proteomics approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), knowledge of the peptide sequences, their masses, and whether they are unique for a protein is essential. Because most bottom-up proteomics approaches use trypsin to cleave the proteins in a sample, the tryptic peptides contained in a protein database are of great interest. We present a database, called MaCPepDB (mass-centric peptide database), that consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts of UniProtKB. This database is especially designed to not only allow queries of peptide sequences and return the respective information about connected proteins and thus whether a peptide is unique but also allow queries of specific masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational modifications can be considered in a query as well as different mass deviations for posttranslational modifications. Hence the database can be used by a sequence query not only to, for example, check in which proteins of the UniProt database a tryptic peptide can be found but also to find possibly interfering peptides in PRM/SRM experiments using the mass query. The complete database contains currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version 2020_03), for which a single query usually takes less than 1 s. For easy exploration of the data, a web interface was developed. A REST application programming interface (API) for programmatic and workflow access is also available at https://macpepdb.mpc.rub.de.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.

ProteaseGuru: A Tool for Protease Selection in Bottom-Up Proteomics

Bottom-up proteomics is currently the dominant strategy for proteome analysis. It relies critically upon the use of a protease to digest proteins into peptides, which are then identified by liquid chromatography-mass spectrometry (LC-MS). The choice of protease(s) has a substantial impact upon the utility of the bottom-up results obtained. Protease selection determines the nature of the peptides produced, which in turn affects the ability to infer the presence and quantities of the parent proteins and post-translational modifications in the sample. We present here the software tool ProteaseGuru, which provides in silico digestions by candidate proteases, allowing evaluation of their utility for bottom-up proteomic experiments. This information is useful for both studies focused on a single or small number of proteins, and for analysis of entire complex proteomes. ProteaseGuru provides a convenient user interface, valuable peptide information, and data visualizations enabling the comparison of digestion results of different proteases. The information provided includes data tables of theoretical peptide sequences and their biophysical properties, results summaries outlining the numbers of shared and unique peptides per protease, histograms facilitating the comparison of proteome-wide proteolytic data, protein-specific summaries, and sequence coverage maps. Examples are provided of its use to inform analysis of variant-containing proteins in the human proteome, as well as for studies requiring the use of multiple proteomic databases such as a human:mouse xenograft model, and microbiome metaproteomics.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.