Mass-spectrometric exploration of proteome structure and function

Model Organisms in Aging Research: Evolution of Database Annotation and Ortholog Discovery

BACKGROUND

This study aims to analyze the exploration degree of popular model organisms by utilizing annotations from the UniProtKB (Swiss-Prot) knowledge base. The research focuses on understanding the genomic and post-genomic data of various organisms, particularly in relation to aging as an integral model for studying the molecular mechanisms underlying pathological processes and physiological states.

METHODS

Having characterized the organisms by selected parameters (numbers of gene splice variants, post-translational modifications, etc.) using previously developed information models, we calculated proteome sizes: the number of possible proteoforms for each species. Our analysis also involved searching for orthologs of human aging genes within these model species.

RESULTS

Our findings indicate that genomic and post-genomic data for more primitive species, such as bacteria and fungi, are more comprehensively characterized compared to other organisms. This is attributed to their experimental accessibility and simplicity. Additionally, we discovered that the genomes of the most studied model organisms allow for a detailed analysis of the aging process, revealing a greater number of orthologous genes related to aging.

CONCLUSIONS

The results highlight the importance of annotating the genomes of less-studied species to identify orthologs of marker genes associated with complex physiological processes, including aging. Species that potentially possess unique traits associated with longevity and resilience to age-related changes require comprehensive genomic studies.

AI-driven eyelid tumor classification in ocular oncology using proteomic data

Eyelid tumors pose diagnostic challenges due to their diverse pathological types and limited biopsy materials. This study aimed to develop an artificial intelligence (AI) diagnostic system for accurate classification of eyelid tumors. Utilizing mass spectrometry-based proteomics, we analyzed proteomic data from eight tissue types and identified eighteen novel biomarkers based on 233 formalin-fixed, paraffin-embedded (FFPE) samples from 150 patients. The 18-protein model, validated by an independent cohort (99 samples from 60 patients), exhibited high accuracy (84.8%), precision (86.2%), and recall (84.8%) in multi-class classification. The model demonstrated distinct clustering of different lesion types, as visualized through UMAP plots. Receiver operator characteristic (ROC) curve analysis revealed strong predictive ability with area under the curve (AUC) values ranging from 0.80 to 1.00. This AI-based diagnostic system holds promise for improving the efficiency and precision of eyelid tumor diagnosis, addressing the limitations of traditional pathological methods.

A Proteomic Examination of Plasma Extracellular Vesicles Across Colorectal Cancer Stages Uncovers Biological Insights That Potentially Improve Prognosis

BACKGROUND

Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC).

METHODS

This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic profiles of 80 patients (20 from each CRC stage I-IV) against 20 healthy age- and sex-matched controls using a high-resolution SWATH-MS proteomics with a reproducible centrifugation method to isolate plasma EVs.

RESULTS

We applied the High-Stringency Human Proteome Project (HPP) guidelines for SWATH-MS analysis, which refined our initial EV protein identification from 1362 proteins (10,993 peptides) to a more reliable and confident subset of 853 proteins (6231 peptides). In early-stage CRC, we identified 11 plasma EV proteins with differential expression between patients and healthy controls (three up-regulated and eight down-regulated), many of which are involved in key cancer hallmarks. Additionally, within the same cohort, we analysed EV proteins associated with tumour recurrence to identify potential prognostic indicators for CRC. A subset of up-regulated proteins associated with extracellular vesicle formation (GDI1, NSF, and TMED9) and the down-regulation of TSG101 suggest that micro-metastasis may have occurred earlier than previously anticipated.

DISCUSSION

By employing stringent proteomic analysis and a robust SWATH-MS approach, we identified dysregulated EV proteins that potentially indicate early-stage CRC and predict recurrence risk, including proteins involved in metabolism, cytoskeletal remodelling, and immune response. While our findings underline discrepancies with other studies due to differing isolation and stringency parameters, they provide valuable insights into the complexity of the EV proteome, emphasising the need for standardised protocols and larger, well-controlled studies to validate potential biomarkers.

ProteoNet: A CNN-based framework for analyzing proteomics MS-RGB images

Proteomics is crucial in clinical research, yet the clinical application of proteomic data remains challenging. Transforming proteomic mass spectrometry (MS) data into red, green, and blue color (MS-RGB) image formats and applying deep learning (DL) techniques has shown great potential to enhance analysis efficiency. However, current DL models often fail to extract subtle, crucial features from MS-RGB data. To address this, we developed ProteoNet, a deep learning framework that refines MS-RGB data analysis. ProteoNet incorporates semantic partitioning, adaptive average pooling, and weighted factors into the Convolutional Neural Network (CNN) model, thus enhancing data analysis accuracy. Our experiments with proteomics data from urine, blood, and tissue samples related to liver, kidney, and thyroid diseases demonstrate that ProteoNet outperforms existing models in accuracy. ProteoNet also provides a direct conversion method for MS-RGB data, enabling a seamless workflow. Moreover, its compatibility with various CNN architectures, including lightweight models like MobileNetV2, underscores its scalability and clinical potential.

A Photo-Caged Cross-Linker for Identifying Protein-Protein Interactions

Cross-linking mass spectrometry (XL-MS) has seen significant improvements which have enhanced its utility for studying protein-protein interactions (PPIs), primarily due to the emergence of novel crosslinkers and the development of streamlined analysis workflows. Nevertheless, poor membrane permeability and side reactions with water limit the extent of productive intracellular crosslinking events that can be achieved with current crosslinkers. To address these problems, we have synthesized a novel crosslinker with o-nitrobenzyl-based photoresponsive groups. These o-nitrobenzyl ester (o-NBE) groups enhance the stability and hydrophobic properties of the crosslinker and add the potential for temporal resolution, i. e. the ability to control the initiation of the crosslinking reaction. Upon exposure to UV light the resulting aldehyde product reacts with adjacent amino groups and subsequent reductive amination of the formed Schiff-bases yields stable secondary amine linkages. This controlled activation mechanism enables precise UV-triggered protein crosslinking. We demonstrate proof-of principle of our o-NBE cross-linker to reliably detect PPIs by XL-MS using a recombinant model protein. We also demonstrate its ability to enter intact Hela cells, thereby indicating its future potential as a useful tool to study PPIs within the cellular environment.

Biological Function Analysis of MicroRNAs and Proteins in the Cerebrospinal Fluid of Patients with Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by alpha-synuclein aggregation into Lewy bodies in the neurons. Cerebrospinal fluid (CSF) is considered the most suited source for investigating PD pathogenesis and identifying biomarkers. While microRNA (miRNA) profiling can aid in the investigation of post-transcriptional regulation in neurodegenerative diseases, information on miRNAs in the CSF of patients with PD remains limited. This review combines miRNA analysis with proteomic profiling to explore the collective impact of CSF miRNAs on the neurodegenerative mechanisms in PD. We constructed separate networks for altered miRNAs and proteomes using a bioinformatics method. Altered miRNAs were poorly linked to biological functions owing to limited information; however, changes in protein expression were strongly associated with biological functions. Subsequently, the networks were integrated for further analysis. In silico prediction from the integrated network revealed relationships between miRNAs and proteins, highlighting increased reactive oxygen species generation, neuronal loss, and neurodegeneration and suppressed ATP synthesis, mitochondrial function, and neurotransmitter release in PD. The approach suggests the potential of miRNAs as biomarkers for critical mechanisms underlying PD. The combined strategy could enhance our understanding of the complex biochemical networks of miRNAs in PD and support the development of diagnostic and therapeutic strategies for precision medicine.

Perturbation of mRNA splicing in liver cancer: insights, opportunities and challenges

Perturbation of mRNA splicing is commonly observed in human cancers and plays a role in various aspects of cancer hallmarks. Understanding the mechanisms and functions of alternative splicing (AS) not only enables us to explore the complex regulatory network involved in tumour initiation and progression but also reveals potential for RNA-based cancer treatment strategies. This review provides a comprehensive summary of the significance of AS in liver cancer, covering the regulatory mechanisms, cancer-related AS events, abnormal splicing regulators, as well as the interplay between AS and post-transcriptional and post-translational regulations. We present the current bioinformatic approaches and databases to detect and analyse AS in cancer, and discuss the implications and perspectives of AS in the treatment of liver cancer.

Towards Next Generation Protein Sequencing

Understanding the structure and function of proteins is a critical objective in the life sciences. Protein sequencing, a central aspect of this endeavor, was first accomplished through Edman degradation in the 1950s. Since the late 20th century, mass spectrometry has emerged as a prominent method for protein sequencing. In recent years, single-molecule technologies have increasingly been applied to this field, yielding numerous innovative results. Among these, nanopore sensing has proven to be a reliable single-molecule technology, enabling advancements in amino acid recognition, short peptide differentiation, and peptide sequence reading. These developments are set to elevate protein sequencing technology to new heights. The next generation of protein sequencing technologies is anticipated to revolutionize our understanding of molecular mechanisms in biological processes and significantly enhance clinical diagnostics and treatments.

Preserve or destroy: Orphan protein proteostasis and the heat shock response

Most eukaryotic genes encode polypeptides that are either obligate members of hetero-stoichiometric complexes or clients of organelle-targeting pathways. Proteins in these classes can be released from the ribosome as "orphans"-newly synthesized proteins not associated with their stoichiometric binding partner(s) and/or not targeted to their destination organelle. Here we integrate recent findings suggesting that although cells selectively degrade orphan proteins under homeostatic conditions, they can preserve them in chaperone-regulated biomolecular condensates during stress. These orphan protein condensates activate the heat shock response (HSR) and represent subcellular sites where the chaperones induced by the HSR execute their functions. Reversible condensation of orphan proteins may broadly safeguard labile precursors during stress.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Development of a rapid and robust hydrop interaction liquid chromatography tandem mass spectrometry method for the detection of 13 endogenous amino acids as well as trimethylamine oxide in serum and tissues of the mice

This work aimed to establish an HILIC-MS/MS method to simultaneously determine the levels of 13 endogenous amino acids and trimethylamine oxide in the biological samples from the mice. Electrospray ion source was used for the analysis of mass spectrometry. The 20 min separation was applied in a Dikma Inspire Hilic column (2.1 × 100.0 mm, 3 μM). Positive ion mode under an MRM model gave a satisfying response value. The limits of quantitation were evaluated by accuracy from -12.59% to 7.89% and precision from 1.77% to 14.00% as well as acceptable interday and intraday precision, matrix effect, recovery, and stability. Later, the assay was successfully used to measure the concentrations of the determinands in the biological samples. Individual and tissue distribution differences for these metabolites were observable. The amino acids had a consistent highest content in the spleens, while the lowest levels were found in the livers. Alanine was the most abundant amino acid in the serum, and taurine kept the highest content in all of the tissues. Trimethylamine oxide remained low level, especially in the liver samples.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Protein expression changes in Tibetan middle-to-long distance runners after the transition from high altitude to low altitude: Implications for enhancing endurance training

The study aims to investigate the differences in protein expressions in Xizang's (Tibetan) middle-to-long distance runners after the transition from high altitude to low altitude and reveal the molecular mechanisms underlying their enhanced middle-to-long distance running performance. In the study, eleven subjects were selected from native Tibetan middle-to-long distance runners to participate in an 8-week pre-competition exercise training program consisting of a 6-week training stage in Kangding City at an altitude of 2 560 meters (m) and a subsequent 2-week training stage in Leshan City at an altitude of 360 ​m. Blood samples were collected twice from the runners before beginning altitude exercise training in Kangding and after going to sea level - Leshan City. Using a label-free quantitative method, peptides in the samples were analyzed by mass spectrometry. Proteomic analysis was performed to identify differentially expressed proteins and predict their biological functions. A total of 846 proteins were identified in the 21 samples, including 719 quantified proteins. In total, 49 significantly differentially expressed proteins (p ​< ​0.05) were identified, including twenty-eight 0.2-fold up-regulated proteins or twenty-one 0.17-fold down-regulated proteins. The up-regulated proteins, including cystic fibrosis transmembrane conductance regulator (CFTR) and carbonic anhydrase I (CAI), were of particular interest due to their role in regulating the oxygen saturation in deep tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these proteins were mainly involved in regulating actin cytoskeleton, local adhesion, biotin absorption and metabolism, immune system, cancer, and membrane transport processes. In conclusion, Tibetan middle-to-long distance runners who resided in high-altitude areas benefited from repeated plateau-plain alternate training mode during the pre-competition period. The training mode induced positive changes in peripheral blood plasma proteins (CFTR and CAI), the biomarkers associated with aerobic capacity. Among the 11 runners, one female athlete won the gold medal in the 3 000-m running event in this competition, demonstrating that the plateau-plain alternate training mode could enhance the aerobic capacity of athletes.

Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives

The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.

Mitochondrial Proteome Defined Molecular Pathological Characteristics of Oncocytic Thyroid Tumors

Oncocytic thyroid tumors are characterized by an elevated mitochondrial density within the cells, distinguishing them from other thyroid tumors, exhibit distinct clinical behaviors, including increased invasiveness and iodine therapy resistance. However, the proteomic alterations in oncocytic thyroid tumors remain inadequately characterized. In this study, we analyzed 156 Asian patients with oncocytic thyroid adenomas (OA) and carcinomas (OCA) to explore their clinical, genetic, and proteomic features. Genetic testing of 73 samples revealed frequent mutations in TERT, NRAS, EIF1AX, EZH1, and HRAS, with TERT promoter mutations being exclusive to OCAs. Proteomic analysis identified 66 mitochondrial-specific proteins significantly highly expressed in oncocytic tumors than in non-oncocytic tumors. This led to the development of a thyroid oncocytic score (TOS) to quantify oncocytic characteristics. Among these proteins, isocitrate dehydrogenase 2 (IDH2) was substantially overexpressed in oncocytic tumors and further confirmed by immunohistochemistry in oncocytic tumor slides (n = 41) and non-oncocytic tumor slides (n = 40). Moreover, IDH2 is significantly overexpressed in OCA compared to OA highlighting its potential as a biomarker for differential diagnosis of oncocytic tumors and malignancy. These findings improve the understanding of oncocytic thyroid tumors molecular pathology and suggest IDH2 as a valuable marker for clinical management.

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

π-HuB: the proteomic navigator of the human body

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Proteomics: An Essential Tool to Study Plant-Specialized Metabolism

Plants are a valuable source of specialized metabolites that provide a plethora of therapeutic applications. They are natural defenses that plants use to adapt and respond to their changing environment. Decoding their biosynthetic pathways and understanding how specialized plant metabolites (SPMs) respond to biotic or abiotic stress will provide vital knowledge for plant biology research and its application for the future sustainable production of many SPMs of interest. Here, we focus on the proteomic approaches and strategies that help with the study of plant-specialized metabolism, including the: (i) discovery of key enzymes and the clarification of their biosynthetic pathways; (ii) study of the interconnection of both primary (providers of carbon and energy for SPM production) and specialized (secondary) metabolism; (iii) study of plant responses to biotic and abiotic stress; (iv) study of the regulatory mechanisms that direct their biosynthetic pathways. Proteomics, as exemplified in this review by the many studies performed to date, is a powerful tool that forms part of omics-driven research. The proteomes analysis provides an additional unique level of information, which is absent from any other omics studies. Thus, an integrative analysis, considered versus a single omics analysis, moves us more closely toward a closer interpretation of real cellular processes. Finally, this work highlights advanced proteomic technologies with immediate applications in the field.

Role of Proteins in Oncology: Advances in Cancer Diagnosis, Prognosis, and Targeted Therapy-A Narrative Review

Modern oncology increasingly relies on the role of proteins as key components in cancer diagnosis, prognosis, and targeted therapy. This review examines advancements in protein biomarkers across several cancer types, including breast cancer, lung cancer, ovarian cancer, and hepatocellular carcinoma. These biomarkers have proven critical for early detection, treatment response monitoring, and tailoring personalized therapeutic strategies. The article highlights the utility of targeted therapies, such as tyrosine kinase inhibitors and monoclonal antibodies, in improving treatment efficacy while minimizing systemic toxicity. Despite these advancements, challenges like tumor resistance, variability in protein expression, and diagnostic heterogeneity persist, complicating universal application. The review underscores future directions, including the integration of artificial intelligence, advanced protein analysis technologies, and the development of combination therapies to overcome these barriers and refine personalized cancer treatment.

Pesticide residues: Bridging the gap between environmental exposure and chronic disease through omics

Pesticide residues, resulting from agricultural practices, pose significant health and environmental risks. This review synthesizes the current understanding of pesticide impacts on the immune system, highlighting their role in chronic diseases such as asthma, diabetes, Parkinson's disease (PD) and cancer. We emphasize the significant role of omics technologies in the study of pesticide toxicity mechanisms. The integration of genomics, proteomics, metabolomics, and epigenomics offers a multidimensional strategy for a comprehensive assessment of pesticide effects, facilitating personalized risk management and policy formulation. We advocate for stringent regulatory policies, public education, and global cooperation to enhance food safety and environmental sustainability. By adopting a unified approach, we aim to mitigate the risks of pesticide residues, ensuring human health and ecological balance are preserved.

AI-empowered perturbation proteomics for complex biological systems

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed "MS1Former", that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model's interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.

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.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Meta-Analysis and DIA-MS-Based Proteomic Investigation of COPD Patients and Asymptomatic Smokers in the Indian Population

Chronic obstructive pulmonary disease (COPD) is India's second largest cause of death and is largely caused by smoking. Asymptomatic smokers develop COPD due to genetic, environmental, and molecular variables, making early screening crucial. Data-independent acquisition mass spectrometry (DIA-MS) based-proteomics offers an unbiased method to analyze proteomic profiles. This study is the first to use DIA-based proteomics to analyze individual serum samples from three distinct male cohorts: healthy individuals (n = 10), asymptomatic smokers (n = 10), and COPD patients (n = 10). This comprehensive approach identified 667 proteins with a 1% false discovery rate. Differentially expressed proteins included 40 in the normal versus asymptomatic comparison, 88 in the COPD versus normal comparison, and 40 in the COPD versus asymptomatic comparison. Among them, protein-associated genes such as PRDX6, ELANE, PRKCSH, PRTN3, and MNDA could help differentiate COPD from asymptomatic smokers, while ELANE, H3-3A, IGHE, SLC4A1, and SERPINA11 could differentiate COPD from healthy subjects. Pathway enrichment and protein-protein interaction analyses revealed significant alterations in hemostasis, immune system functions, fibrin clot formation, and post-translational protein modifications. Key proteins were validated using a parallel reaction monitoring assay. DIA data are available via ProteomeXchange with identifier PXD055242. Our findings reveal key protein classifiers in COPD patients, asymptomatic smokers, and healthy individuals, helping clinicians understand disease pathobiology and improve disease management and quality of life.

Automated multiplexed affinity-based enrichment of peptides for LC-MS/MS plasma proteomics

Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.

Pressure Cycling Technology Combined With MicroLC-SWATH Mass Spectrometry for the Analysis of Sex-Related Differences Between Male and Female Cerebella: A Promising Approach to Investigating Proteomics Differences in Psychiatric and Neurodegenerative Diseases

PURPOSE

Pressure cycling technology (PCT) coupled with data-independent sequential window acquisition of all theoretical mass spectra (SWATH-MS) can be a powerful tool for identifying and quantifying biomarkers (e.g., proteins) in complex biological samples. Mouse models are frequently used in brain studies, including those focusing on different neurodevelopmental and psychiatric disorders. More and more pieces of evidence have suggested that sex-related differences in the brain impact the rates, clinical manifestations, and therapy outcomes of these disorders. However, sex-based differences in the proteomic profiles of mouse cerebella have not been widely investigated.

EXPERIMENTAL DESIGN

In this pilot study, we evaluate the applicability of coupling PCT sample preparation with microLC-SWATH-MS analysis to map and identify differences in the proteomes of two female and two male mice cerebellum samples.

RESULTS

We identified and quantified 174 proteins in mice cerebella. A comparison of the proteomic profiles revealed that the levels of 11 proteins in the female and male mice cerebella varied significantly.

CONCLUSIONS AND CLINICAL RELEVANCE

Although this study utilizes a small sample, our results indicate that the studied male and female mice cerebella possessed differing proteome compositions, mainly with respect to energy metabolism processes.

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

INTRODUCTION

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

AREAS COVERED

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

EXPERT OPINION

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Human-mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome

Murine models of Alzheimer's disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. We comprehensively profiled age-dependent brain proteome and phosphoproteome (n > 10,000 for both) across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata, and prioritized novel components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Determining the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including Aβ-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identify potential new targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.

The evolution of immune profiling: will there be a role for nanoparticles?

Immune profiling provides insights into the functioning of the immune system, including the distribution, abundance, and activity of immune cells. This understanding is essential for deciphering how the immune system responds to pathogens, vaccines, tumors, and other stimuli. Analyzing diverse immune cell types facilitates the development of personalized medicine approaches by characterizing individual variations in immune responses. With detailed immune profiles, clinicians can tailor treatment strategies to the specific immune status and needs of each patient, maximizing therapeutic efficacy while minimizing adverse effects. In this review, we discuss the evolution of immune profiling, from interrogating bulk cell samples in solution to evaluating the spatially-rich molecular profiles across intact preserved tissue sections. We also review various multiplexed imaging platforms recently developed, based on immunofluorescence and imaging mass spectrometry, and their impact on the field of immune profiling. Identifying and localizing various immune cell types across a patient's sample has already provided important insights into understanding disease progression, the development of novel targeted therapies, and predicting treatment response. We also offer a new perspective by highlighting the unprecedented potential of nanoparticles (NPs) that can open new horizons in immune profiling. NPs are known to provide enhanced detection sensitivity, targeting specificity, biocompatibility, stability, multimodal imaging features, and multiplexing capabilities. Therefore, we summarize the recent developments and advantages of NPs, which can contribute to advancing our understanding of immune function to facilitate precision medicine. Overall, NPs have the potential to offer a versatile and robust approach to profile the immune system with improved efficiency and multiplexed imaging power.

Deciphering the molecular heterogeneity of intermediate- and (very-)high-risk non-muscle-invasive bladder cancer using multi-layered -omics studies

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered -omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different -omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner.

Storing liquid chromatographic separations on surface energy traps: decoupling the LC and the mass spectrometer

We report a micro-fractionation device for high performance liquid chromatography-mass spectrometry to archive chromatographic separations on an array of optimized surface energy traps (SETs). The method has the potential to significantly alter nanoflow LC-MS workflow, decoupling separation and analysis. The wetting characteristics of the SETs cause the HPLC eluent stream to spontaneously split into droplet microfractions. The droplet mirofractions are then dried down to enable facile storage and transport of the archived separation. Discontinuously dewetting array parameters were explored to maximize array volume and resolution using a combination of SET design, shape, size, and spacing. Mass spectrometry analysis is performed utilizing a liquid micro-junction surface sampling probe to extract dried analytes from the surface of the SETs followed by electrospray ionisation. A reverse phase separation of pharmaceutical compounds is "recorded" using the micro-fractionation device followed by "reading" the chromatographic trace with a mass spectrometer 24 hours after the separation was performed/archived, demonstrating a true decoupling of LC, and MS. Additionally, we demonstrate the ability to collect microfractions with sub-one-second integration time, approaching the scan time of a mass spectrometer or UV-Vis detector. With further improvements to the device, sub-1-second micro-fractionation may enable seamless reconstruction of archived chromatograms indistinguishable from online LC-MS data, while also providing the benefits of easy storage and transport of archived separations.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

A Chemoselective Enrichment Strategy for In-Depth Coverage of the Methyllysine Proteome

Proteomics is a powerful method to comprehensively understand cellular posttranslational modifications (PTMs). Owing to low abundance, tryptic peptides with PTMs are usually enriched for enhanced coverage by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Affinity chromatography for phosphoproteomes by metal-oxide and pan-specific antibodies for lysine acetylome allow identification of tens of thousands of modification sites. Lysine methylation is a significant PTM; however, only hundreds of methylation sites were identified by available approaches. Herein we report an aryl diazonium based chemoselective strategy that enables enrichment of monomethyllysine (Kme1) peptides through covalent bonds with extraordinary sensitivity. We identified more than 10000 Kme1 peptides from diverse cell lines and mouse tissues, which implied a wide lysine methylation impact on cellular processes. Furthermore, we found a significant amount of methyl marks that were not S-adenosyl methionine (SAM)-dependent by isotope labeling experiments.

Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

nQuant Enables Precise Quantitative N-Glycomics

N-glycosylation is a highly heterogeneous post-translational modification that modulates protein function. Defects in N-glycosylation are directly linked to various human diseases. Despite the importance of quantifying N-glycans with high precision, existing glycoinformatics tools are limited. Here, we developed nQuant, a glycoinformatics tool that enables label-free and isotopic labeling quantification of N-glycomics data obtained via LC-MS/MS, ensuring a low false quantitation rate. Using the label-free quantification module, we profiled the N-glycans released from purified glycoproteins and HEK293 cells as well as the dynamic changes of N-glycosylation during mouse corpus callosum development. Through the isotopic labeling quantification module, we revealed the dynamic changes of N-glycans in acute promyelocytic leukemia cells after all-trans retinoic acid treatment. Taken together, we demonstrate that nQuant enables fast and precise quantitative N-glycomics.

TermineR: Extracting information on endogenous proteolytic processing from shotgun proteomics data

State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforce a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify of native protein termini or those arising from endogenous proteolytic activity (also referred to as "neo-N-termini" analysis or "N-terminomics"). Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce TermineR, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity and known protein processing features, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, and (3) visualization of neo-N-termini location. We illustrate the use of TermineR by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease, and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The TermineR approach and example data are available as an R package at https://github.com/MiguelCos/TermineR.

PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals

To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

Exploring Protein Post-Translational Modifications of Breast Cancer Cells Using a Novel Combinatorial Search Algorithm

Post-translational modification of proteins plays an important role in cancer cell biology. Proteins encoded by oncogenes may be activated by phosphorylation, products of tumour suppressors might be inactivated by phosphorylation or ubiquitinylation, which marks them for degradation; chromatin-binding proteins are often methylated and/or acetylated. These are just a few of the many hundreds of post-translational modifications discovered by years of painstaking experimentation and the chemical analysis of purified proteins. In recent years, mass spectrometry-based proteomics emerged as the principal technique for identifying such modifications in samples from cultured cells and tumour tissue. Here, we used a recently developed combinatorial search algorithm implemented in the MGVB toolset to identify novel modifications in samples from breast cancer cell lines. Our results provide a rich resource of coupled protein abundance and post-translational modification data seen in the context of an important biological function-the response of cells to interferon gamma treatment-which can serve as a starting point for future investigations to validate promising modifications and explore the utility of the underlying molecular mechanisms as potential diagnostic or therapeutic targets.

Molecular biomarkers identification and applications in CHO bioprocessing

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.

Mitigating In-Column Artificial Modifications in High-Temperature LC-MS for Bottom-Up Proteomics and Quality Control of Protein Biopharmaceuticals

Elevating the column temperature is an effective strategy for improving the chromatographic separation of peptides. However, high temperatures induce artificial modifications that compromise the quality of the peptide analysis. Here, we present a novel high-temperature LC-MS method that retains the benefits of a high column temperature while significantly reducing peptide modification and degradation during reversed-phase liquid chromatography. Our approach leverages a short inline trap column maintained at a near-ambient temperature installed upstream of a separation column. The retentivity and dimensions of the trap column were optimized to shorten the residence time of peptides in the heated separation column without compromising the separation performance. This easy-to-implement approach increased peak capacity by 1.4-fold within a 110 min peptide mapping of trastuzumab and provided 10% more peptide identifications in exploratory LC-MS proteomic analyses compared with analyses conducted at 30 °C while maintaining the extent of modifications close to the background level. In the peptide mapping of biopharmaceuticals, where in-column modifications can falsely elevate the levels of some critical quality attributes, the method reduced temperature-related artifacts by 66% for N-terminal pyroGlu and 63% for oxidized Met compared to direct injection at 60 °C, thus improving reliability in quality control of protein drugs. Our findings represent a promising advancement in LC-MS methodology, providing researchers and industry professionals with a valuable tool for improving the chromatographic separation of peptides while significantly reducing the unwanted modifications.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

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.

Nanopore ion sources deliver individual ions of amino acids and peptides directly into high vacuum

Electrospray ionization is widely used to generate vapor phase ions for analysis by mass spectrometry in proteomics research. However, only a small fraction of the analyte enters the mass spectrometer due to losses that are fundamentally linked to the use of a background gas to stimulate the generation of ions from electrosprayed droplets. Here we report a nanopore ion source that delivers ions directly into high vacuum from aqueous solutions. The ion source comprises a pulled quartz pipette with a sub-100 nm opening. Ions escape an electrified meniscus by ion evaporation and travel along collisionless trajectories to the ion detector. We measure mass spectra of 16 different amino acid ions, post-translationally modified variants of glutathione, and the peptide angiotensin II, showing that these analytes can be emitted as desolvated ions. The emitted current is composed of ions rather than charged droplets, and more than 90% of the current can be recovered in a distant collector.

Feature selection by replicate reproducibility and non-redundancy

MOTIVATION

A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new synthetic coordinates and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability, and modularity. A common criterion for unsupervized feature selection is variance or dynamic range. However, in practice, it can occur that high-variance features are noisy, that important features have low variance, or that variances are simply not comparable across features because they are measured in unrelated numeric scales or physical units. Moreover, users may want to include measures of signal-to-noise ratio and non-redundancy into feature selection.

RESULTS

Here, we introduce the RNR algorithm, which selects features based on (i) the reproducibility of their signal across replicates and (ii) their non-redundancy, measured by linear dependence. It takes as input a typically large set of features measured on a collection of objects with two or more replicates per object. It returns an ordered list of features, i1,i2,…,ik, where feature i1 is the one with the highest reproducibility across replicates, i2 that with the highest reproducibility across replicates after projecting out the dimension spanned by i1, and so on. Applications to microscopy-based imaging of cells and proteomics highlight benefits of the approach.

AVAILABILITY AND IMPLEMENTATION

The RNR method is available via Bioconductor (Huber W, Carey VJ, Gentleman R et al. (Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 2015;12:115-21.) in the R package FeatSeekR. Its source code is also available at https://github.com/tcapraz/FeatSeekR under the GPL-3 open source license.