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

Proteo-SAFARI: An R Application for Identification of Fragment Ions in Top-Down MS/MS Spectra of Proteins

Proteo-SAFARI is a shiny application for fragment assignment by relative isotopes, an R-based software application designed for identification of protein fragment ions directly in the m/z domain. This program provides an open-source, user-friendly application for identification of fragment ions from a candidate protein sequence with support for custom covalent modifications and various visualizations of identified fragments. Additionally, Proteo-SAFARI includes a nonnegative least-squares fitting approach to determine the contributions of various hydrogen shifted fragment ions (a + 1, x + 1, y - 1, y - 2) observed in UVPD mass spectra which exhibit overlapping isotopic distributions. To show its utility, Proteo-SAFARI is applied to various MS/MS spectra of intact proteins, including proteins exhibiting dynamic hydrogen shifts in y ions, ubiquitin charge-reduced to the 1+ charge state, and a large protein recorded in full profile mode. Proteo-SAFARI is available at: github.com/mblanzillotti/Proteo-SAFARI.

Proteomic Profiling Towards a Better Understanding of Genetic Based Muscular Diseases: The Current Picture and a Look to the Future

Proteomics accelerates diagnosis and research of muscular diseases by enabling the robust analysis of proteins relevant for the manifestation of neuromuscular diseases in the following aspects: (i) evaluation of the effect of genetic variants on the corresponding protein, (ii) prediction of the underlying genetic defect based on the proteomic signature of muscle biopsies, (iii) analysis of pathophysiologies underlying different entities of muscular diseases, key for the definition of new intervention concepts, and (iv) patient stratification according to biochemical fingerprints as well as (v) monitoring the success of therapeutic interventions. This review presents-also through exemplary case studies-the various advantages of mass proteomics in the investigation of genetic muscle diseases, discusses technical limitations, and provides an outlook on possible future application concepts. Hence, proteomics is an excellent large-scale analytical tool for the diagnostic workup of (hereditary) muscle diseases and warrants systematic profiling of underlying pathophysiological processes. The steady development may allow to overcome existing limitations including a quenched dynamic range and quantification of different protein isoforms. Future directions may include targeted proteomics in diagnostic settings using not only muscle biopsies but also liquid biopsies to address the need for minimally invasive procedures.

Umbrella Sampling MD Simulations for Retention Prediction in Peptide Reversed-phase Liquid Chromatography

Reversed-phase liquid chromatography (RPLC) is an essential tool for separating complex mixtures such as proteolytic digests in bottom-up proteomics. There is growing interest in methods that can predict the RPLC retention behavior of peptides and other analytes. Already, existing algorithms provide excellent performance based on empirical rules or large sets of RPLC training data. Here we explored a new type of retention prediction strategy that relies on first-principles modeling of peptide interactions with a C18 stationary phase. We recently demonstrated that molecular dynamics (MD) simulations can provide atomistic insights into the behavior of peptides under RPLC conditions (Anal. Chem. 2023, 95, 3892). However, the current work found that it is problematic to use conventional MD data for retention prediction, evident from a poor correlation between experimental retention times and MD-generated "fraction bound" values. We thus turned to umbrella sampling MD, a complementary technique that has previously been applied to probe noncovalent contacts in other types of systems. By restraining the peptide dynamic motions at various positions inside a C18-lined pore, we determined the free energy of the system as a function of peptide-stationary phase distance. ΔGbinding values determined in this way under various mobile phase conditions were linearly correlated with experimental retention times of tryptic test peptides. This work opens retention prediction avenues for novel types of stationary and mobile phases, and for peptides (or other analytes) having arbitrary chemical properties, without the need for RPLC reference data. Umbrella sampling can be used as a stand-alone tool, or it may serve to enhance existing retention prediction algorithms.

Progress in Tandem Mass Spectrometry Data Analysis for Nucleic Acids

Mass spectrometry (MS) has become a critical tool in the characterization of covalently modified nucleic acids. Well-developed bottom-up approaches, where nucleic acids are digested with an endonuclease and the resulting oligonucleotides are separated before MS and MS/MS analysis, provide substantial insight into modified nucleotides in biological and synthetic nucleic. Top-down MS presents an alternative approach where the entire nucleic acid molecule is introduced to the mass spectrometer intact and then fragmented by MS/MS. Current top-down MS workflows have incorporated automated, on-line HPLC workflows to enable rapid desalting of nucleic acid samples for facile mass analysis without complication from adduction. Furthermore, optimization of MS/MS parameters utilizing collision, electron, or photon-based activation methods have enabled effective bond cleavage throughout the phosphodiester backbone while limiting secondary fragmentation, allowing characterization of progressively larger (~100 nt) nucleic acids and localization of covalent modifications. Development of software applications to perform automated identification of fragment ions has accelerated the broader adoption of mass spectrometry for analysis of nucleic acids. This review focuses on progress in tandem mass spectrometry for characterization of nucleic acids with particular emphasis on the software tools that have proven critical for advancing the field.

Deep Proteome Profiling of Primary Skin Fibroblasts

Skin-derived primary cells and skin tissue are prime model systems to study molecular disease mechanisms on cell and tissue level. Cells and tissue can be cultivated ex vivo and protocols for both, simple 2D as well as 3D in vitro cultures reflecting different levels of complexity exist. Mass spectrometry (MS)-based proteomics is a prime approach to link molecular mechanisms to observable disease phenotypes. In this chapter, we describe in detail the analysis of 2D and 3D primary skin fibroblast cultures by MS. We focus on automated sample processing to increase throughput and usage of limited cell numbers to reduce costs. The described workflow supports the study of proteome regulation in large scale screening approaches, be it for drug discovery or in clinical studies.

Protein Sample Preparation for Bottom-Up, Label-Free Quantitative Proteomics of Adipose Tissue

Adipose tissue (AT) is a complex, multifunctional endocrine organ that plays a significant role in animal evolution and human disease. Profiling of the proteome, or the set of proteins produced by a cell or tissue at a given time, can be used to explore the myriad functions of adipose tissue and understand its role in health and disease. The main challenges of adipose tissue proteomics include the high lipid and low protein content of the tissue and association of many proteins with lipid droplets. Here, we present a protocol for gel-free, label-free, bottom-up, relative quantitative proteomics of adipose tissue based on findings from the literature and our laboratory that yields reproducible protein and peptide identification rates while minimizing cost and processing time. This approach involves tissue homogenization, protein precipitation from homogenates, solubilization and denaturation of proteins in a buffer containing 5% sodium deoxycholate (SDC, an acid-insoluble detergent) and 5 mM tris(2-carboxyethyl)phosphine (TCEP, a reducing agent), alkylation with chloroacetamide, and in-solution tandem digestion with trypsin and Lys-C enzymes in the presence of 1% SDC. Acidification of peptides efficiently removes SDC prior to desalting and mass spectrometry. This method has been used successfully in our laboratory by both experienced researchers and those with limited technical backgrounds, including high school, undergraduate, and graduate students. We have identified >1500 proteins in adipose tissue of non-model mammals (e.g., blubber of marine mammals) spanning a dynamic range of 105 using this approach, including proteins of interest for comparative physiology such as adipokines, metabolic and antioxidant enzymes, lipid droplet proteins, metabolite transporters, and mitochondrial proteins, among others.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning

BACKGROUND

Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.

METHODS

We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.

RESULTS

We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5.

CONCLUSIONS

We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Use of genomics & proteomics in studying lipase producing microorganisms & its application

In biotechnological applications, lipases are recognized as the most widely utilized and versatile enzymes, pivotal in biocatalytic processes, predominantly produced by various microbial species. Utilizing omics technology, natural sources can be meticulously screened to find microbial flora which are responsible for oil production. Lipases are versatile biocatalysts. They are used in a variety of bioconversion reactions and are receiving a lot of attention because of the quick development of enzyme technology and its usefulness in industrial operations. This article offers recent insights into microbial lipase sources, including fungi, bacteria, and yeast, alongside traditional and modern methods of purification such as precipitation, immunopurification and chromatographic separation. Additionally, it explores innovative methods like the reversed micellar system, aqueous two-phase system (ATPS), and aqueous two-phase flotation (ATPF). The article deals with the use of microbial lipases in a variety of sectors, including the food, textile, leather, cosmetics, paper, detergent, while also critically analyzing lipase-producing microbes. Moreover, it highlights the role of lipases in biosensors, biodiesel production, tea processing, bioremediation, and racemization. This review provides the concept of the use of omics technique in the mechanism of screening of microbial species those are capable of producing lipase and also find the potential applications.

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Advancements in mass spectrometry-based proteomics: a new era in pathology research and diagnostics

BACKGROUND

Mass spectrometry (MS)-based proteomics is rapidly transforming pathology research and diagnostics by enabling comprehensive studies of protein expression and post-translational modifications (PTMs).

OBJECTIVE

This article discusses recent advancements in MS-based proteomics, focusing on emerging technologies in sample preparation, MS instrumentation, and data analysis. These developments are scrutinized for their applications in clinical cohort studies and molecular pathology diagnostics.

MATERIALS AND METHODS

The article reviews innovations in automated sample preparation, chromatography systems, advanced MS technologies, and proteomic data analysis in the context of pathology. Specific applications such as liquid biopsy, spike-in heavy peptide panels, immunopeptidomics, and PTM screening are highlighted alongside opportunities for data integration.

RESULTS

Recent technological improvements have significantly increased the throughput, precision, and scope of proteomic studies, enabling the analysis of large clinical cohorts and small specimens with unprecedented sensitivity. Advanced MS techniques have broadened applications, opening new avenues for discovery and diagnosis of marker proteins and therapeutic targets.

CONCLUSION

Advancements in MS-based proteomics have created new opportunities in clinical research and diagnostics. By facilitating more comprehensive and integrated analyses of proteomes, these technologies are set to play a pivotal role in the future of personalized medicine and pathology research.

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis - Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers

Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SIGNIFICANCE: Our exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.

Mass Spectrometry-Based Proteomics for Classification and Treatment Optimisation of Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) presents a significant medical challenge due to its highly invasive nature, high rate of metastasis, and lack of drug-targetable receptors, which together lead to poor prognosis and limited treatment options. The traditional treatment guidelines for early TNBC are based on a multimodal approach integrating chemotherapy, surgery, and radiation and are associated with low overall survival and high relapse rates. Therefore, the approach to treating early TNBC has shifted towards neoadjuvant treatment (NAC), given to the patient before surgery and which aims to reduce tumour size, reduce the risk of recurrence, and improve the pathological complete response (pCR) rate. However, recent studies have shown that NAC is associated with only 30% of patients achieving pCR. Thus, novel predictive biomarkers are essential if treatment decisions are to be optimised and chemotherapy toxicities minimised. Given the heterogeneity of TNBC, mass spectrometry-based proteomics technologies offer valuable tools for the discovery of targetable biomarkers for prognosis and prediction of toxicity. These biomarkers can serve as critical targets for therapeutic intervention. This review aims to provide a comprehensive overview of TNBC diagnosis and treatment, highlighting the need for a new approach. Specifically, it highlights how mass spectrometry-based can address key unmet clinical needs by identifying novel protein biomarkers to distinguish and early prognostication between TNBC patient groups who are being treated with NAC. By integrating proteomic insights, we anticipate enhanced treatment personalisation, improved clinical outcomes, and ultimately, increased survival rates for TNBC patients.

Mass spectrometry analysis of phosphotyrosine-containing proteins

Tyrosine phosphorylation is a crucial posttranslational modification that is involved in various aspects of cell biology and often has functions in cancers. It is necessary not only to identify the specific phosphorylation sites but also to quantify their phosphorylation levels under specific pathophysiological conditions. Because of its high sensitivity and accuracy, mass spectrometry (MS) has been widely used to identify endogenous and synthetic phosphotyrosine proteins/peptides across a range of biological systems. However, phosphotyrosine-containing proteins occur in extremely low abundance and they degrade easily, severely challenging the application of MS. This review highlights the advances in both quantitative analysis procedures and enrichment approaches to tyrosine phosphorylation before MS analysis and reviews the differences among phosphorylation, sulfation, and nitration of tyrosine residues in proteins. In-depth insights into tyrosine phosphorylation in a wide variety of biological systems will offer a deep understanding of how signal transduction regulates cellular physiology and the development of tyrosine phosphorylation-related drugs as cancer therapeutics.

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Unravelling G protein-coupled receptor signalling networks using global phosphoproteomics

G protein-coupled receptor (GPCR) activation initiates signalling via a complex network of intracellular effectors that combine to produce diverse cellular and tissue responses. Although we have an advanced understanding of the proximal events following receptor stimulation, the molecular detail of GPCR signalling further downstream often remains obscure. Unravelling these GPCR-mediated signalling networks has important implications for receptor biology and drug discovery. In this context, phosphoproteomics has emerged as a powerful approach for investigating global GPCR signal transduction. Here, we provide a brief overview of the phosphoproteomic workflow and discuss current limitations and future directions for this technology. By highlighting some of the novel insights into GPCR signalling networks gained using phosphoproteomics, we demonstrate the utility of global phosphoproteomics to dissect GPCR signalling networks and to accelerate discovery of new targets for therapeutic development. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Phosphoproteomic analysis of APP/PS1 mice of Alzheimer's disease by DIA based mass spectrometry analysis with PRM verification

Traditional Chinese medicine has been utilized in China for approximately thousands of years in clinical settings to prevent Alzheimer's disease (AD) and enhance memory, despite the lack of a systematic exploration of its biological underpinnings. Exciting research has corroborated the beneficial effects of tetrahydroxy stilbene glycoside (TSG), an extract derived from Polygonum multiflorum, in delaying learning and memory impairment in a model that mimics AD. Therefore, the primary objective of this study is to investigate the major function of TSG upon protein regulation in AD. Herein, a novel approach, encompassing data independent acquisition (DIA), DIA phosphorylated proteomics, and parallel reaction monitoring (PRM), was utilized to integrate quantitative proteomic data collected from APP/PS1 mouse model exhibiting toxic intracellular aggregation of Aβ. Initially, we deliberated upon both single and multi-dimensional data pertaining to AD model mice. Furthermore, we authenticated disparities in protein phosphorylation quantity and expression, phosphorylation function, and ultimately phosphorylation kinase analysis. In order to validate the results, we utilized PRM ion monitoring technology to identify potential protein or peptide biomarkers. In the mixed samples, targeted detection of 50 target proteins revealed that 26 to 33 target proteins were stably detected by PRM. In summary, our findings provide new candidates for AD biomarker, which have been identified and validated through protein researches conducted on mouse brains. This offers a wealth of potential resources for extensive biomarker validation in neurodegenerative diseases. SIGNIFICANCE: DIA phosphorylated proteomics technique was used to detect and analyze phosphorylated proteins in brain tissues of mice with AD. Data were analyzed by various bioinformatics tools to explore the phosphorylation events and characterize them related to TSG. The results of DIA were further verified by PRM. Besides, we mapped the major metabolite classes emerging from the analyses to key biological pathways implicated in AD to understand the potential roles of the molecules and the interactions in triggering symptom onset and progression of AD. Meanwhile, we clarified that in the context of AD onset and TSG intervention, the changes in proteins, protein phosphorylation, phosphorylation kinases, and the internal connections.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

DJ-1 protects proteins from acylation by catalyzing the hydrolysis of highly reactive cyclic 3-phosphoglyceric anhydride

Mutations in the human PARK7 gene that encodes protein DJ-1 lead to familial Parkinsonism due to loss of dopaminergic neurons. However, the molecular function of DJ-1 underpinning its cytoprotective effects are unclear. Recently, DJ-1 has been shown to prevent acylation of amino groups of proteins and metabolites by 1,3-bisphosphoglycerate. This acylation is indirect and thought to proceed via the formation of an unstable intermediate, presumably a cyclic 3-phosphoglyceric anhydride (cPGA). Several lines of evidence indicate that DJ-1 destroys cPGA, however this enzymatic activity has not been directly demonstrated. Here, we report simple and effective procedures for synthesis and quantitation of cPGA and present a comprehensive characterization of this highly reactive acylating electrophile. We demonstrate that DJ-1 is an efficient cPGA hydrolase with kcat/Km = 5.9 × 106 M-1s-1. Experiments with DJ-1-null cells reveal that DJ-1 protects against accumulation of 3-phosphoglyceroyl-lysine residues in proteins. Our results establish a definitive cytoprotective function for DJ-1 that uses catalytic hydrolysis of cPGA to mitigate the damage from this glycolytic byproduct.

Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Proteomic Profiling of Samples Derived from a Murine Model Following Cryptococcus neoformans Infection

Proteomic profiling provides in-depth information about the regulation of diverse biological processes, activation of and communication across signaling networks, and alterations to protein production, modifications, and interactions. For infectious disease research, mass spectrometry-based proteomics enables detection of host defenses against infection and mechanisms used by the pathogen to evade such responses. In this chapter, we outline protein extraction from organs, tissues, and fluids collected following intranasal inoculation of a murine model with the human fungal pathogen Cryptococcus neoformans. We describe sample preparation, followed by purification, processing on the mass spectrometer, and a robust bioinformatics analysis. The information gleaned from proteomic profiling of fungal infections supports the detection of novel biomarkers for diagnostic and prognostic purposes.

Infrared Laser Ablation and Capture of Biological Tissue

Sampling thin tissue sections with cellular precision can be accomplished using laser ablation microsampling for mass spectrometry analysis. In this work, the use of a pulsed mid-infrared (IR) laser for selecting small regions of interest (ROI) in tissue sections for offline liquid chromatography-tandem mass spectrometry (LC-MS/MS) is described. The laser is focused onto the tissue section, which is rastered as the laser is fired. The ablated tissue is captured in a microwell array and processed in situ through reduction, alkylation, and digestion with a low liquid volume workflow. The resulting peptides from areas as small as 0.01 mm2 containing 5 ng of protein are analyzed for protein identification and quantification using offline LC-MS/MS.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Advances in high-resolution mass spectrometry techniques for analysis of high mass-to-charge ions

A major challenge in modern mass spectrometry (MS) is achieving high mass resolving power and accuracy for precision analyses in high mass-to-charge (m/z) regions. To advance the capability of MS for increasingly demanding applications, understanding limitations of state-of-the-art techniques and their status in applied sciences is essential. This review summarizes important instruments in high-resolution mass spectrometry (HRMS) and related advances to extend their working range to high m/z regions. It starts with an overview of HRMS techniques that provide adequate performance for macromolecular analysis, including Fourier-transform, time-of-flight (TOF), quadrupole-TOF, and related data-processing techniques. Methodologies and applications of HRMS for characterizing macromolecules in biochemistry and material sciences are summarized, such as top-down proteomics, native MS, drug discovery, structural virology, and polymer analyses.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Molecular Evidence of Structural Changes in Silk Using Unlimited Degradation Mass Spectrometry

Proteomics has important uses in archeological science because it can distinguish species, reveal the evolution of paleontology, and provide biological evidence of historical events. However, this technique still has full potential in the study of silk aging mechanisms. In this work, we propose a strategy combining unlimited degradation with mass-spectrometry-based proteomics techniques, which interpret protein fragmentation propensity and secondary structure changes by detecting content changes of specific peptide groups in complex proteomes. This approach was employed to study the conformational changes in silk microscopic crystals after heat treatment. Combining conventional mechanics and crystallographic characterization, a thermal aging degradation mechanism model was proposed. At the same time, it explained the interesting problem that the crystallinity remained unchanged, but the mechanical properties decreased significantly. Focusing on the unlimited degradation process, this method will be widely applicable to the study of silk and wool aging processes and regenerated silk fibroin.

Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach

Current proteomics approaches rely almost exclusively on using the positive ionization mode, resulting in inefficient ionization of many acidic peptides. This study investigates protein identification efficiency in the negative ionization mode using the DirectMS1 method. DirectMS1 is an ultrafast data acquisition method based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identification in the negative ion mode to date, identifying over 1000 proteins in a human cell line at a 1% false discovery rate. This is accomplished using a single-shot 10 min separation gradient, comparable to lengthy MS/MS-based analyses. Optimizing separation and experimental conditions was achieved by utilizing mobile buffers containing 2.5 mM imidazole and 3% isopropanol. The study emphasized the complementary nature of data obtained in positive and negative ion modes. Combining the results from all replicates in both polarities increased the number of identified proteins to 1774. Additionally, we analyzed the method's efficiency using different proteases for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), trypsin and LysC demonstrated the highest protein identification yield. This suggests that digestion procedures utilized in positive-mode proteomics can be effectively applied in the negative ion mode. Data are deposited to ProteomeXchange: PXD040583.

Ultrasensitive proteomics depicted an in-depth landscape for the very early stage of mouse maternal-to-zygotic transition

Single-cell or low-input multi-omics techniques have revolutionized the study of pre-implantation embryo development. However, the single-cell or low-input proteomic research in this field is relatively underdeveloped because of the higher threshold of the starting material for mammalian embryo samples and the lack of hypersensitive proteome technology. In this study, a comprehensive solution of ultrasensitive proteome technology (CS-UPT) was developed for single-cell or low-input mouse oocyte/embryo samples. The deep coverage and high-throughput routes significantly reduced the starting material and were selected by investigators based on their demands. Using the deep coverage route, we provided the first large-scale snapshot of the very early stage of mouse maternal-to-zygotic transition, including almost 5,500 protein groups from 20 mouse oocytes or zygotes for each sample. Moreover, significant protein regulatory networks centered on transcription factors and kinases between the MII oocyte and 1-cell embryo provided rich insights into minor zygotic genome activation.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Integrative Proteomics and Metabolomics Analysis Reveals the Role of Small Signaling Peptide Rapid Alkalinization Factor 34 (RALF34) in Cucumber Roots

The main role of RALF small signaling peptides was reported to be the alkalization control of the apoplast for improvement of nutrient absorption; however, the exact function of individual RALF peptides such as RALF34 remains unknown. The Arabidopsis RALF34 (AtRALF34) peptide was proposed to be part of the gene regulatory network of lateral root initiation. Cucumber is an excellent model for studying a special form of lateral root initiation taking place in the meristem of the parental root. We attempted to elucidate the role of the regulatory pathway in which RALF34 is a participant using cucumber transgenic hairy roots overexpressing CsRALF34 for comprehensive, integrated metabolomics and proteomics studies, focusing on the analysis of stress response markers. CsRALF34 overexpression resulted in the inhibition of root growth and regulation of cell proliferation, specifically in blocking the G2/M transition in cucumber roots. Based on these results, we propose that CsRALF34 is not part of the gene regulatory networks involved in the early steps of lateral root initiation. Instead, we suggest that CsRALF34 modulates ROS homeostasis and triggers the controlled production of hydroxyl radicals in root cells, possibly associated with intracellular signal transduction. Altogether, our results support the role of RALF peptides as ROS regulators.

Characterization and implications of host-cell protein aggregates in biopharmaceutical processing

In the production of biopharmaceuticals such as monoclonal antibodies (mAbs) and vaccines, the residual amounts of host-cell proteins (HCPs) are among the critical quality attributes. In addition to overall HCP levels, individual HCPs may elude purification, potentially causing issues in product stability or patient safety. Such HCP persistence has been attributed mainly to biophysical interactions between individual HCPs and the product, resin media, or residual chromatin particles. Based on measurements on process streams from seven mAb processes, we have found that HCPs in aggregates, not necessarily chromatin-derived, may play a significant role in the persistence of many HCPs. Such aggregates may also hinder accurate detection of HCPs using existing proteomics methods. The findings also highlight that certain HCPs may be difficult to remove because of their functional complementarity to the product; specifically, chaperones and other proteins involved in the unfolded protein response (UPR) are disproportionately present in the aggregates. The methods and findings described here expand our understanding of the origins and potential behavior of HCPs in cell-based biopharmaceutical processes and may be instrumental in improving existing techniques for HCP detection and clearance.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis

Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.

Atomistic Details of Peptide Reversed-Phase Liquid Chromatography from Molecular Dynamics Simulations

Peptide separations by reversed-phase liquid chromatography (RPLC) are an integral part of bottom-up proteomics. These separations typically employ C18 columns with water/acetonitrile gradient elution in the presence of formic acid. Despite the widespread use of such workflows, the exact nature of peptide interactions with the stationary and mobile phases is poorly understood. Here, we employ microsecond molecular dynamics (MD) simulations to uncover details of peptide RPLC. We examined two tryptic peptides, a hydrophobic and a hydrophilic species, in a slit pore lined with C18 chains that were grafted onto SiO₂ support. Our simulations explored peptide trapping, followed by desorption and elution. Trapping in an aqueous mobile phase was initiated by C18 contacts with Lys butyl moieties. This was followed by extensive anchoring of nonpolar side chains (Leu/Ile/Val) in the C18 layer. Exposure to water/acetonitrile triggered peptide desorption in a stepwise fashion; charged sites close to the termini were the first to lift off, followed by the other residues. During water/acetonitrile elution, both peptides preferentially resided close to the pore center. The hydrophilic peptide exhibited no contacts with the stationary phase under these conditions. In contrast, the hydrophobic species underwent multiple transient Leu/Ile/Val binding interactions with C18 chains. These nonpolar interactions represent the foundation of differential peptide retention, in agreement with the experimental elution behavior of the two peptides. Extensive peptide/formate ion pairing was observed in water/acetonitrile, particularly at N-terminal sites. Overall, this work uncovers an unprecedented level of RPLC molecular details, paving the way for MD simulations as a future tool for improving retention prediction algorithms and for the design of novel column materials.

Miniprep assisted proteomics (MAP) for rapid proteomics sample preparation

Complete enzymatic digestion of proteins for bottom-up proteomics is substantially improved by use of detergents for denaturation and solubilization. Detergents however, are incompatible with many proteases and highly detrimental to LC-MS/MS. Recently; filter-based methods have seen wide use due to their capacity to remove detergents and harmful reagents prior to digestion and mass spectrometric analysis. We hypothesized that non-specific protein binding to negatively charged silica-based filters would be enhanced by addition of lyotropic salts, similar to DNA purification. We sought to exploit these interactions and investigate if low-cost DNA purification spin-filters, 'Minipreps,' efficiently and reproducibly bind proteins for digestion and LC-MS/MS analysis. We propose a new method, Miniprep Assisted Proteomics (MAP), for sample preparation. We demonstrate binding capacity, performance, recovery and identification rates for proteins and whole-cell lysates using MAP. MAP recovered equivalent or greater protein yields from 0.5-50 μg analyses benchmarked against commercial trapping preparations. Nano UHPLC-MS/MS proteome profiling of lysates of Escherichia coli had 99.3% overlap vs. existing approaches and reproducibility of replicate minipreps was 98.8% at the 1% FDR protein level. Label Free Quantitative proteomics was performed and 91.2% of quantified proteins had a %CV <20% (2044/2241). Miniprep Assisted Proteomics can be performed in minutes, shows low variability, high recovery and proteome depth. This suggests a significant role for adventitious binding in developing new proteomics sample preparation techniques. MAP represents an efficient, ultra-low-cost alternative for sample preparation in a commercially obtainable device that costs ∼$0.50 (USD) per miniprep.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Top-Down Mass Spectrometry of Synthetic Single Guide Ribonucleic Acids Enabled by Facile Sample Clean-Up

In recent years, CRISPR-Cas9 genome editing has become an important technology in biomedical research and has demonstrated tremendous therapeutic potential. With Cas9 endonuclease, the use of single guide ribonucleic acids (sgRNAs) allows for sequence-specific cutting on target double-stranded deoxyribonucleic acids. Therefore, the design and quality of sgRNAs can greatly affect the efficiency and specificity of genome editing. Mass spectrometry (MS) has been a powerful tool to detect molecular features and sequence a variety of biomolecules; however, as the sizes of oligonucleotides get larger, it becomes more challenging to desalt samples and achieve high-quality intact spectra with effective fragmentation. Here, we develop a simple but effective online column-based clean-up method (reversed-phase column in a size exclusion mode) that removes formulation salts and metal adducts from larger oligonucleotides upon entering the mass spectrometer in a consistent manner. Using the top-down approach without any nuclease digestion, we characterized and sequenced 100-nucleotide-long sgRNAs by higher-energy collision dissociation (HCD), collision-induced dissociation (CID), ultraviolet photodissociation (UVPD), and activated electron photodetachment (a-EPD). In a single 10 min liquid chromatography-tandem MS (LC-MS/MS) run, CID yielded the best sequence coverage, of 67%. When adding complementary UVPD and a-EPD runs, we achieved 80% overall sequence coverage and 100% cleavages for the variable sequence, the first 20 nucleotides from the 5' end. This LC-MS/MS platform provides a facile top-down workflow to analyze and sequence larger chemically modified oligonucleotides with no sample treatment.

A peptide-centric approach to analyse quantitative proteomics data- an application to prostate cancer biomarker discovery

Bottom-up proteomics is a popular approach in molecular biomarker research. However, protein analysts have realized the limitations of protein-based approaches for identifying and quantifying proteins in complex samples, such as the identification of peptides sequences shared by multiple proteins and the difficulty in identifying modified peptides. Thus, there are many exciting opportunities to improve analysis methods. Here, an alternative method focused on peptide analysis is proposed as a complement to the conventional proteomics data analysis. To investigate this hypothesis, a peptide-centric approach was applied to reanalyse a urine proteome dataset of samples from prostate cancer patients and controls. The results were compared with the conventional protein-centric approach. The relevant proteins/peptides to discriminate the groups were detected based on two approaches, p-value and VIP values obtained by a PLS-DA model. A comparison of the two strategies revealed high inconsistency between protein and peptide information and greater involvement of peptides in key PCa processes. This peptide analysis unveiled discriminative features that are lost when proteins are analyzed as homogeneous entities. This type of analysis is innovative in PCa and integrated with the widely used protein-centric approach might provide a more comprehensive view of this disease and revolutionize biomarker discovery. SIGNIFICANCE: In this study, the application of a protein and peptide-centric approaches to reanalyse a urine proteome dataset from prostate cancer (PCa) patients and controls showed that many relevant proteins/peptides are missed by the conservative nature of p-value in statistical tests, therefore, the inclusion of variable selection methods in the analysis of the dataset reported in this work is fruitful. Comparison of protein- and peptide-based approaches revealed a high inconsistency between protein and peptide information and a greater involvement of peptides in key PCa processes. These results provide a new perspective to analyse proteomics data and detect relevant targets based on the integration of peptide and protein information. This data integration allows to unravel discriminative features that normally go unnoticed, to have a more comprehensive view of the disease pathophysiology and to open new avenues for the discovery of biomarkers.

Analysis of milk with liquid chromatography–mass spectrometry: a review

As a widely consumed foodstuff, milk and dairy products are increasingly studied over the years. At the present time, milk profiling is used as a benchmark to assess the properties of milk. Modern biomolecular mass spectrometers have become invaluable to fully characterize the milk composition. This review reports the analysis of milk and its components using liquid chromatography coupled with mass spectrometry (LC–MS). LC–MS analysis as a whole will be discussed subdivided into the major constituents of milk, namely, lipids, proteins, sugars and the mineral fraction.

Quantitative phosphoproteomic analysis of mice with liver fibrosis by DIA mass spectrometry analysis with PRM verification

Liver fibrosis (LF), commonly associated with chronic liver diseases, is a major public health problem worldwide. Protein phosphorylation is not only an important form of protein modification in organisms but also the most important mechanism to regulate and control the activity and function of proteins, affecting the occurrence and development of many diseases. However, comprehensive phosphoproteomic profiling in LF has not been fully elucidated. In this study, data-independent acquisition (DIA) was used to analyse the phosphoproteomics of mice with LF. A total of 553 phosphopeptides (representing 440 phosphoproteins) had significant phosphorylation levels. Among these phosphoproteins, 49 were upregulated and 401 were downregulated, and 5 phosphoserine (P-Ser) motifs and 2 phosphothreonine (P-Thr) motifs were conserved in LF. GO and KEGG pathway enrichment analyses identified 769 significant GO terms and 49 significant KEGG pathways. Four phosphorylated proteins were selected for parallel reaction monitoring (PRM) verification, and the results were consistent with DIA data. Together, there were significantly different phosphoproteomic profiles in LF, suggesting that protein phosphorylation was related to the occurrence and progression of LF, which could pave the way for further investigation into the related regulatory mechanisms. SIGNIFICANCE: LF is a necessary stage in the development of chronic liver disease to liver cirrhosis and has attracted wide attention. To the best of our knowledge, there are few reports on the phosphorylated proteomics of LF. In this study, DIA and PRM techniques were used to study the liver tissue of mice induced by CCl₄. The results showed that phosphorylation had a significant effect on the activity and function of proteins, and the PRM results were consistent with the trend observed in DIA analysis. This study will help to better reveal the relationship of phosphorylated proteins in LF and lay a foundation for further study of related regulatory mechanisms.

Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery

In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.

Contribution of Capillary Zone Electrophoresis Hyphenated with Drift Tube Ion Mobility Mass Spectrometry as a Complementary Tool to Microfluidic Reversed Phase Liquid Chromatography for Antigen Discovery

The discovery of new antigens specific to multiple myeloma that could be targeted by novel immunotherapeutic approaches is currently of great interest. To this end, it is important to increase the number of proteins identified in the sample by combining different separation strategies. A capillary zone electrophoresis (CZE) method, coupled with drift tube ion mobility (DTIMS) and quadrupole time-of-flight mass spectrometry (QTOF), was developed for antigen discovery using the human myeloma cell line LP-1. This method was first optimized to obtain a maximum number of identifications. Then, its performance in terms of uniqueness of identifications was compared to data acquired by a microfluidic reverse phase liquid chromatography (RPLC) method. The orthogonality of these two approaches and the physicochemical properties of the entities identified by CZE and RPLC were evaluated. In addition, the contribution of DTIMS to CZE was investigated in terms of orthogonality as well as the ability to provide unique information. In conclusion, we believe that the combination of CZE-DTIMS-QTOF and microfluidic RPLC provides unique information in the context of antigen discovery.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.