Proteomics: the first decade and beyond

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification

The identification of protein complexes from protein interaction networks is crucial in the understanding of protein function, cellular processes and disease mechanisms. Existing methods commonly rely on the assumption that protein interaction networks are highly reliable, yet in reality, there is considerable noise in the data. In addition, these methods fail to account for the regulatory roles of biomolecules during the formation of protein complexes, which is crucial for understanding the generation of protein interactions. To this end, we propose a SpatioTemporal constrained RNA-protein heterogeneous network for Protein Complex Identification (STRPCI). STRPCI first constructs a multiplex heterogeneous protein information network to capture deep semantic information by extracting spatiotemporal interaction patterns. Then, it utilizes a dual-view aggregator to aggregate heterogeneous neighbor information from different layers. Finally, through contrastive learning, STRPCI collaboratively optimizes the protein embedding representations under different spatiotemporal interaction patterns. Based on the protein embedding similarity, STRPCI reweights the protein interaction network and identifies protein complexes with core-attachment strategy. By considering the spatiotemporal constraints and biomolecular regulatory factors of protein interactions, STRPCI measures the tightness of interactions, thus mitigating the impact of noisy data on complex identification. Evaluation results on four real PPI networks demonstrate the effectiveness and strong biological significance of STRPCI. The source code implementation of STRPCI is available from https://github.com/LI-jasm/STRPCI.

The application of omics technologies for understanding tropical plants-based bioactive compounds in ruminants: a review

Finding out how diet impacts health and metabolism while concentrating on the functional qualities and bioactive components of food is the crucial scientific objective of nutritional research. The complex relationship between metabolism and nutrition could be investigated with cutting-edge "omics" and bioinformatics techniques. This review paper provides an overview of the use of omics technologies in nutritional research, with a particular emphasis on the new applications of transcriptomics, proteomics, metabolomics, and genomes in functional and biological activity research on ruminant livestock and products in the tropical regions. A wealth of knowledge has been uncovered regarding the regulation and use of numerous physiological and pathological processes by gene, mRNA, protein, and metabolite expressions under various physiological situations and guidelines. In particular, the components of meat and milk were assessed using omics research utilizing the various methods of transcriptomics, proteomics, metabolomics, and genomes. The goal of this review is to use omics technologies-which have been steadily gaining popularity as technological tools-to develop new nutritional, genetic, and leadership strategies to improve animal products and their quality control. We also present an overview of the new applications of omics technologies in cattle production and employ nutriomics and foodomics technologies to investigate the microbes in the rumen ecology. Thus, the application of state-of-the-art omics technology may aid in our understanding of how species and/or breeds adapt, and the sustainability of tropical animal production, in the long run, is becoming increasingly important as a means of mitigating the consequences of climate change.

Mass Spectrometric Proteomics 2.0

This Special Issue, "Mass Spectrometric Proteomics 2 [...].

Identification of differentially expressed exosome proteins in serum as potential biomarkers for cognitive impairments in cerebral small vessel disease

BACKGROUND

Cognitive impairment arising from cerebral small vessel disease (CSVD) represents a critical subtype of vascular cognitive impairments (VCI) and is the primary cause of vascular dementia. However, identifying reliable clinical and laboratory indicators for this disease remain elusive. We hypothesize that plasma exosome proteins hold the potential to serve as biomarkers for the onset of cognitive dysfunction associated with cerebrovascular diseases.

METHODS

We employed TMT-based proteomics to discern variations in serum exosome proteomes between individuals with cognitive impairments due to CSVD and healthy volunteers.

RESULTS

Each group comprised 18 subjects, and through differential expression analysis, we identified 22 down-regulated and 8 up-regulated proteins between the two groups. Our research revealed 30 differentially expressed plasma exosome proteins, including histone, proteasome, clusterin and coagulation factor XIII, in individuals with cognitive impairments caused by CSVD.

CONCLUSION

The 30 differentially expressed plasma exosome proteins identified in our study are promising as biomarkers for diagnosing cognitive impairments resulting from CSVD. These findings may help us better understand the underlying pathological mechanisms involved in the diseases.

Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer's disease: findings based on urine proteomics and machine learning

BACKGROUND

Alzheimer's disease is a prevalent disease with a heavy global burden. Proteomics is the systematic study of proteins and peptides to provide comprehensive descriptions. Aiming to obtain a more accurate and convenient clinical diagnosis, researchers are working for better biomarkers. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.

METHODS

We firstly enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples, and conducted an LC-MS/MS analysis. In parallel, clinical data were collected, and clinical examinations were performed. After statistical and bioinformatics analyses, significant risk factors and differential urinary proteins were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.

RESULTS

Fifty-seven AD patients, 43 MCI patients, and 62 CN subjects were enrolled. A total of 3366 proteins were identified, and 608 urine proteins were finally included in the analysis. There were 33 significantly differential proteins between the AD and CN groups and 15 significantly differential proteins between the MCI and CN groups. AD diagnostic panel included DDC, CTSC, EHD4, GSTA3, SLC44A4, GNS, GSTA1, ANXA4, PLD3, CTSH, HP, RPS3, CPVL, age, and APOE ε4 with an AUC of 0.9989 in the training test and 0.8824 in the test set while MCI diagnostic panel included TUBB, SUCLG2, PROCR, TCP1, ACE, FLOT2, EHD4, PROZ, C9, SERPINA3, age, and APOE ε4 with an AUC of 0.9985 in the training test and 0.8143 in the test set. Besides, diagnostic proteins were weakly correlated with cognitive functions.

CONCLUSIONS

In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN.

Unveiling the potential of proteomics in addressing food and feed safety challenges

The food and feed sector in Europe is rapidly evolving to address contemporary challenges, striving for fairer, safer, greener and more sustainable food systems. This includes the exploration of new protein sources for human consumption and animal feed such as protein derived from insects, algae or novel plant-derived proteins, and the re-evaluation of existing sources like processed animal protein (PAP). To generate reliable data on the diverse array of emerging protein sources for future food and feed safety assessments, a growing demand for the development and implementation of advanced analytical techniques exists. New approach methodologies (NAMs) including, mass spectrometry (MS)-based proteomics methods have been emerging as valuable techniques which potentially can be implemented in regulatory laboratory settings to complement conventional approaches in this realm. These MS-driven strategies have already proven their utility in diverse applications, including the detection of prohibited substances in feed, identification of allergens, differentiation of fish species in complex mixtures for fraud detection and the verification of novel foods and alternative protein sources. This EU-FORA programme was focused on three core objectives namely: (i) the training of the fellow in utilising MS-based proteomics for food and feed safety analyses, (ii) the involvement of the fellow in the development of standardised operating procedures (SOP) for targeted and non-targeted proteomic MS-based workflows for species and tissues specific PAP identification in a national reference laboratory (NRL) and (iii) the transfer and implementation of MS-based approaches and standardised protocols for PAP analysis at the fellow's home institution. Altogether, this programme facilitates the broadening and diversification of use of MS-based proteomic methodologies for reinforcing their significance within the domains of food and feed safety research and regulatory science applications.

Deep proteome coverage advances knowledge of Treponema pallidum protein expression profiles during infection

Comprehensive proteome-wide analysis of the syphilis spirochete, Treponema pallidum ssp. pallidum, is technically challenging due to high sample complexity, difficulties with obtaining sufficient quantities of bacteria for analysis, and the inherent fragility of the T. pallidum cell envelope which further complicates proteomic identification of rare T. pallidum outer membrane proteins (OMPs). The main aim of the present study was to gain a deeper understanding of the T. pallidum global proteome expression profile under infection conditions. This will corroborate and extend genome annotations, identify protein modifications that are unable to be predicted at the genomic or transcriptomic levels, and provide a foundational knowledge of the T. pallidum protein expression repertoire. Here we describe the optimization of a T. pallidum-specific sample preparation workflow and mass spectrometry-based proteomics pipeline which allowed for the detection of 77% of the T. pallidum protein repertoire under infection conditions. When combined with prior studies, this brings the overall coverage of the T. pallidum proteome to almost 90%. These investigations identified 27 known/predicted OMPs, including potential vaccine candidates, and detected expression of 11 potential OMPs under infection conditions for the first time. The optimized pipeline provides a robust and reproducible workflow for investigating T. pallidum protein expression during infection. Importantly, the combined results provide the deepest coverage of the T. pallidum proteome to date.

Proteomic analyses of smear-positive/negative tuberculosis patients uncover differential antigen-presenting cell activation and lipid metabolism

Background

Tuberculosis (TB) remains a major global health concern, ranking as the second most lethal infectious disease following COVID-19. Smear-Negative Pulmonary Tuberculosis (SNPT) and Smear-Positive Pulmonary Tuberculosis (SPPT) are two common types of pulmonary tuberculosis characterized by distinct bacterial loads. To date, the precise molecular mechanisms underlying the differences between SNPT and SPPT patients remain unclear. In this study, we aimed to utilize proteomics analysis for identifying specific protein signatures in the plasma of SPPT and SNPT patients and further elucidate the molecular mechanisms contributing to different disease pathogenesis.

Methods

Plasma samples from 27 SPPT, 37 SNPT patients and 36 controls were collected and subjected to TMT-labeled quantitative proteomic analyses and targeted GC-MS-based lipidomic analysis. Ingenuity Pathway Analysis (IPA) was then performed to uncover enriched pathways and functionals of differentially expressed proteins.

Results

Proteomic analysis uncovered differential protein expression profiles among the SPPT, SNPT, and Ctrl groups, demonstrating dysfunctional immune response and metabolism in both SPPT and SNPT patients. Both groups exhibited activated innate immune responses and inhibited fatty acid metabolism, but SPPT patients displayed stronger innate immune activation and lipid metabolic inhibition compared to SNPT patients. Notably, our analysis uncovered activated antigen-presenting cells (APCs) in SNPT patients but inhibited APCs in SPPT patients, suggesting their critical role in determining different bacterial loads/phenotypes in SNPT and SPPT. Furthermore, some specific proteins were detected to be involved in the APC activation/acquired immune response, providing some promising therapeutic targets for TB.

Conclusion

Our study provides valuable insights into the differential molecular mechanisms underlying SNPT and SPPT, reveals the critical role of antigen-presenting cell activation in SNPT for effectively clearing the majority of Mtb in bodies, and shows the possibility of APC activation as a novel TB treatment strategy.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Influence of chemical dose and exposure duration on protein synthesis in green sea turtle primary cells

Understanding the impacts of chemical exposure in marine wildlife is challenging, due to practical and ethical constraints that preclude traditional toxicology research on these animals. This study addressed some of these limitations by presenting an ethical and high throughput cell-based approach to elucidate molecular-level effects of contaminants on sea turtles. The experimental design addressed basic questions of cell-based toxicology, including chemical dose and exposure time. Primary green turtle skin cells were exposed to polychlorinated biphenyl (PCB) 153 and perfluorononanoic acid (PFNA) for 24 and 48 h, at three sub-lethal, environmentally relevant concentrations (1, 10 and 100 μg/L). Sequential window acquisition of all theoretical mass spectra (SWATH-MS) identified over 1000 differentially abundant proteins within the 1% false discovery rate (FDR) threshold. The 24 h exposure resulted in a greater number of differentially abundant proteins, compared to 48 h exposure, for both contaminants. However, there were no statistically significant dose-response relationships for the number of differentially synthesised proteins, nor differences in the proportion of increased vs decreased proteins between or within exposure times. Known in vivo markers of contaminant exposure, superoxide dismutase and glutathione S-transferase, were differentially abundant following exposure to PCB153 and PFNA. SIGNIFICANCE: Cell-based (in vitro) proteomics provides an ethical and high throughput approach to understanding the impacts of chemical contamination on sea turtles. Through investigating effects of chemical dose and exposure duration on unique protein abundance in vitro, this study provides an optimised framework for conducting cell-based studies in wildlife proteomics, and highlights that proteins detected in vitro could act as biomarkers of chemical exposure and effect in vivo.

Investigation of targets and anticancer mechanisms of covalently acting natural products by functional proteomics

Eriocalyxin B (EB), 17-hydroxy-jolkinolide B (HJB), parthenolide (PN), xanthatin (XT) and andrographolide (AG) are terpenoid natural products with a variety of promising antitumor activities, which commonly bear electrophilic groups (α,β-unsaturated carbonyl groups and/or epoxides) capable of covalently modifying protein cysteine residues. However, their direct targets and underlying molecular mechanisms are still largely unclear, which limits the development of these compounds. In this study, we integrated activity-based protein profiling (ABPP) and quantitative proteomics approach to systematically characterize the covalent targets of these natural products and their involved cellular pathways. We first demonstrated the anti-proliferation activities of these five compounds in triple-negative breast cancer cell MDA-MB-231. Tandem mass tag (TMT)-based quantitative proteomics showed all five compounds commonly affected the ubiquitin mediated proteolysis pathways. ABPP platform identified the preferentially modified targets of EB and PN, two natural products with high anti-proliferation activity. Biochemical experiments showed that PN inhibited the cell proliferation through targeting ubiquitin carboxyl-terminal hydrolase 10 (USP10). Together, this study uncovered the covalently modified targets of these natural products and potential molecular mechanisms of their antitumor activities.

Increasing the production of the bioactive compounds in medicinal mushrooms: an omics perspective

Macroscopic fungi, mainly higher basidiomycetes and some ascomycetes, are considered medicinal mushrooms and have long been used in different areas due to their pharmaceutically/nutritionally valuable bioactive compounds. However, the low production of these bioactive metabolites considerably limits the utilization of medicinal mushrooms both in commerce and clinical trials. As a result, many attempts, ranging from conventional methods to novel approaches, have been made to improve their production. The novel strategies include conducting omics investigations, constructing genome-scale metabolic models, and metabolic engineering. So far, genomics and the combined use of different omics studies are the most utilized omics analyses in medicinal mushroom research (both with 31% contribution), while metabolomics (with 4% contribution) is the least. This article is the first attempt for reviewing omics investigations in medicinal mushrooms with the ultimate aim of bioactive compound overproduction. In this regard, the role of these studies and systems biology in elucidating biosynthetic pathways of bioactive compounds and their contribution to metabolic engineering will be highlighted. Also, limitations of omics investigations and strategies for overcoming them will be provided in order to facilitate the overproduction of valuable bioactive metabolites in these valuable organisms.

Proteome profiling of ductal carcinoma in situ

BACKGROUND AND AIM

DCIS is the most common type of non-invasive breast cancer, accounting for about 15 to 30%. Proteome profile is used to detect biomarkers in the tissues of breast cancer patients by mass spectrometry. This study aimed to obtain the expression profile of DCIS proteome, and the expression profile of invasive biomarkers, and finally to introduce a dedicated biomarker panel to facilitate the prognosis and early detection for in situ breast cancer patients.

METHODS AND MATERIALS

In this study, 10 patients with breast cancer (DCIS) were studied. Benign (marginal) and cancerous tissue samples were obtained from patients for proteomics experiments. Initially, all tissue proteins were extracted using standard methods, and the proteins were separated using two-dimensional electrophoresis. Then, the expression amount of the extracted proteins was determined by ITRAQ. The data were analysed by R software, and gene ontology was utilised for describing the protein in detail.

RESULTS

30 spots on gel electrophoresis were found in the tumor tissue group (sample), and 15 spots in the margin group (control) with P < 0.05. Healthy and cancerous tissue gels showed that 5 spots had different expression. VWF, MMP9, ITGAM, MPO and PLG protein spots were identified using the site www.ebi.ac.uk/IPI. Finally, protein biomarkers for breast tumor tissue with margin were introduced with the names of P04406, P49915, P05323, P06733, and P02768.

DISCUSSION

There are 5 critical proteins in inducing cancer pathways especially complement and coagulation cascades. The hall markers of a healthy cell to be cancerous are proliferation, invasion, angiogenesis, and changes in the immune system. Hence, regulation of protein plays a key role in developing recurrence to breast cancer in margins.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

Proteomics in Biomarker Discovery for Tuberculosis: Current Status and Future Perspectives

Tuberculosis (TB) continues to threaten many peoples' health worldwide, regardless of their country of residence or age. The current diagnosis of TB still uses mainly traditional, time-consuming, and/or culture-based techniques. Efforts have focused on discovering new biomarkers with higher efficiency and accuracy for TB diagnosis. Proteomics-the systematic study of protein diversity-is being applied to the discovery of novel protein biomarkers for different types of diseases. Mass spectrometry (MS) technology plays a revolutionary role in proteomics, and its applicability benefits from the development of other technologies, such as matrix-based and immune-based methods. MS and derivative strategies continuously contribute to disease-related discoveries, and some promising proteomic biomarkers for efficient TB diagnosis have been identified, but challenges still exist. For example, there are discrepancies in the biomarkers identified among different reports and the diagnostic accuracy of clinically applied proteomic biomarkers. The present review summarizes the current status and future perspectives of proteomics in the field of TB biomarker discovery and aims to elicit more promising findings for rapid and accurate TB diagnosis.

Characterization of the Skeletal Muscle Proteome in Undernourished Old Rats

Aging is associated with a progressive loss of skeletal muscle mass and function termed sarcopenia. Various metabolic alterations that occur with aging also increase the risk of undernutrition, which can worsen age-related sarcopenia. However, the impact of undernutrition on aged skeletal muscle remains largely under-researched. To build a deeper understanding of the cellular and molecular mechanisms underlying age-related sarcopenia, we characterized the undernutrition-induced changes in the skeletal muscle proteome in old rats. For this study, 20-month-old male rats were fed 50% or 100% of their spontaneous intake for 12 weeks, and proteomic analysis was performed on both slow- and fast-twitch muscles. Proteomic profiling of undernourished aged skeletal muscle revealed that undernutrition has profound effects on muscle proteome independently of its effect on muscle mass. Undernutrition-induced changes in muscle proteome appear to be muscle-type-specific: slow-twitch muscle showed a broad pattern of differential expression in proteins important for energy metabolism, whereas fast-twitch muscle mainly showed changes in protein turnover between undernourished and control rats. This first proteomic analysis of undernourished aged skeletal muscle provides new molecular-level insight to explain phenotypic changes in undernourished aged muscle. We anticipate this work as a starting point to define new biomarkers associated with undernutrition-induced muscle loss in the elderly.

Multiplexed Quantitative Proteomic Profiling of Cancer Cells and Tissues Using Isobaric Labeling-Based Tags

Comparing cancer proteomes across many samples offers a window into cancer cell biology and may reveal new treatment options for specific subsets of cancer. Here we describe a method using tandem mass tag (TMT) technology to multiplex up to 18 samples in a single analysis, paving the way for the analysis of large cohorts of tumors, cell lines, and perturbations thereof. The procedure we describe will result in samples ready for in-depth LC-MS/MS analysis in 3-4 days.

Novel Autoantibodies in Idiopathic Small Fiber Neuropathy

Objective

Small fiber neuropathy (SFN) is clinically and etiologically heterogeneous. Although autoimmunity has been postulated to be pathophysiologically important in SFN, few autoantibodies have been described. We aimed to identify autoantibodies associated with idiopathic SFN (iSFN) by a novel high‐throughput protein microarray platform that captures autoantibodies expressed in the native conformational state.

Methods

Sera from 58 SFN patients and 20 age‐ and gender‐matched healthy controls (HCs) were screened against >1,600 immune‐related antigens. Fluorescent unit readout and postassay imaging were performed, followed by composite data normalization and protein fold change (pFC) analysis. Analysis of an independent validation cohort of 33 SFN patients against the same 20 HCs was conducted to identify reproducible proteins in both cohorts.

Results

Nine autoantibodies were screened with statistical significance and pFC criteria in both cohorts, with at least 50% change in serum levels. Three proteins showed consistently high fold changes in main and validation cohorts: MX1 (FC = 2.99 and 3.07, respectively, p = 0.003, q = 0.076), DBNL (FC = 2.11 and 2.16, respectively, p = 0.009, q < 0.003), and KRT8 (FC = 1.65 and 1.70, respectively, p = 0.043, q < 0.003). Further subgroup analysis into iSFN and SFN by secondary causes (secondary SFN) in the main cohort showed that MX1 is higher in iSFN compared to secondary SFN (FC = 1.61 vs 0.106, p = 0.009).

Interpretation

Novel autoantibodies MX1, DBNL, and KRT8 are found in iSFN. MX1 may allow diagnostic subtyping of iSFN patients. ANN NEUROL 2022;91:66–77

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

MS_Piano: A Software Tool for Annotating Peaks in CID Tandem Mass Spectra of Peptides and N-Glycopeptides

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

Orthosiphon stamineus Proteins Alleviate Hydrogen Peroxide Stress in SH-SY5Y Cells

The neuroprotective potential of Orthosiphon stamineus leaf proteins (OSLPs) has never been evaluated in SH-SY5Y cells challenged by hydrogen peroxide (H₂O₂). This work thus aims to elucidate OSLP neuroprotective potential in alleviating H₂O₂ stress. OSLPs at varying concentrations were evaluated for cytotoxicity (24 and 48 h) and neuroprotective potential in H₂O₂-induced SH-SY5Y cells (24 h). The protective mechanism of H₂O₂-induced SH-SY5Y cells was also explored via mass-spectrometry-based label-free quantitative proteomics (LFQ) and bioinformatics. OSLPs (25, 50, 125, 250, 500, and 1000 µg/mL; 24 and 48 h) were found to be safe. Pre-treatments with OSLP doses (250, 500, and 1000 µg/mL, 24 h) significantly increased the survival of SH-SY5Y cells in a concentration-dependent manner and improved cell architecture—pyramidal-shaped cells, reduced clumping and shrinkage, with apparent neurite formations. OSLP pre-treatment (1000 µg/mL, 24 h) lowered the expressions of two major heat shock proteins, HSPA8 (heat shock protein family A (Hsp70) member 8) and HSP90AA1 (heat shock protein 90), which promote cellular stress signaling under stress conditions. OSLP is, therefore, suggested to be anti-inflammatory by modulating the “signaling of interleukin-4 and interleukin-13” pathway as the predominant mechanism in addition to regulating the “attenuation phase” and “HSP90 chaperone cycle for steroid hormone receptors” pathways to counteract heat shock protein (HSP)-induced damage under stress conditions.

Detection of Metabolite-Protein Interactions in Complex Biological Samples by High-Resolution Relaxometry: Toward Interactomics by NMR

Metabolomics, the systematic investigation of metabolites in biological fluids, cells, or tissues, reveals essential information about metabolism and diseases. Metabolites have functional roles in a myriad of biological processes, as substrates and products of enzymatic reactions but also as cofactors and regulators of large numbers of biochemical mechanisms. These functions involve interactions of metabolites with macromolecules. Yet, methods to systematically investigate these interactions are still scarce to date. In particular, there is a need for techniques suited to identify and characterize weak metabolite-macromolecule interactions directly in complex media such as biological fluids. Here, we introduce a method to investigate weak interactions between metabolites and macromolecules in biological fluids. Our approach is based on high-resolution NMR relaxometry and does not require any invasive procedure or separation step. We show that we can detect interactions between small and large molecules in human blood serum and quantify the size of the complex. Our work opens the way for investigations of metabolite (or other small molecules)-protein interactions in biological fluids for interactomics or pharmaceutical applications.

PIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction

Background

Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning models. However, existing machine learning methods have not considered relationship among the proteins sufficiently.

Results

We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein–protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements.

Conclusion

The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.

Co-culturing multicellular tumor models: Modeling the tumor microenvironment and analysis techniques

Advances in two-dimensional (2D) and three-dimensional (3D) cell culture over the last 10 years have led to the development of a plethora of methods for cultivating tumor models. More recently, cellular co-cultures have become a suitable testbed. The first portion of this review focuses on co-culturing methods that have been developed in recent years utilizing the multicellular tumor spheroid model. The latter portion describes techniques that are used to analyze the proteomes of mono- or co-cultured tumor models, with a focus on mass spectrometry (MS)-based analyses. Protein profiles are important indicators of the tumor heterogeneity. Therefore, there is a specific focus within this review on analysis by MS and MS imaging methods evaluating the proteomic profiles of 2D and 3D co-cultures. While these models are incredibly important for biological research, so far, they have not been widely explored on the proteomic level. With this review, we aim to introduce these systems to an analytical audience, with the goal of highlighting MS as an underutilized tool for proteomic analysis of tumor models.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Shotgun Proteomics and Mass Spectrometry as a Tool for Protein Identification and Profiling of Bio-Carrier-Based Therapeutics on Human Cancer Cells

Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.

A quantitative proteomics analysis for small molecule Stemazole's effect on human neural stem cells

BACKGROUND

Stemazole is a novel small molecule that has been suggested to have the ability to protect multiple stem cells. The proliferation-promoting activity and promising neuroprotective effects of stemazole make it a prospective drug for neurodegenerative disease treatment.

METHODS

Since previous studies have shown that it protective effect in extreme conditions, to understand more aspects of stemazole, in this study, a systematic tandem mass tags (TMT)-labelled proteomics approach was used to address the whole proteome expression profile with or without stemazole in normal conditions instead of extreme conditions. Bioinformatics analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network analyses, were employed.

RESULTS

The effect of stemazole on the expression profiles of neural stem cells was obtained. A total of 408 proteins with changes at the abundance level of two groups were identified: 178 proteins increase in abundance and 240 proteins decrease in abundance, respectively. Low abundance of some mitochondrial respiratory chain enzyme, overproduction of reactive oxygen species (ROS) and reduction of mitochondrial membrane potential may indicate stemazole has cytotoxicity.

CONCLUSIONS

It is the first proteomics research about stemazole, and the possible cytotoxicity of stemazole has been reported for the first time. The information about proteins that were affected by stemazole and more characteristics of stemazole will help obtain a complete picture of this small molecule drug. These findings provide a scientific basis for further stemazole treatment research.

Antibody Arrays: Barcode Technology

Antibody microarray is a fundamental, high-content technology for analyzing biomarkers with a multiplexity even at the proteomic level. Recent advancement in this field has driven the antibody array into a new territory related with single-cell analysis. Here we describe a flow pattern-based method for producing a high-density barcode antibody microarray for the detection of proteins in fluidic samples and in single cells. The antibody microarray is fabricated by a perpendicularly oriented flow patterning of single-stranded barcode DNAs, which are then converted into DNA-antibody conjugates. Compared to conventional microarrays, this barcode antibody microarray features a simple and high-throughput assay while achieving both high sensitivity and specificity. This barcode technology provides new clues for developing next-generation antibody microarrays and can be widely used in protein biomarker discovery, cell signaling network analysis, and disease diagnosis and prognosis.

Quantitative proteomic strategies to study reproduction in farm animals: Female reproductive fluids

Reproductive fluids from the female reproductive tract are gaining attention for their potential to support and optimize reproductive processes, including gamete maturation and embryo culture in vitro. Quantitative proteomics is a powerful way to decipher the proteome of reproductive tract fluids and to identify biologically relevant proteins. The present review describes proteomic strategies for analysing female reproductive fluid proteins. In addition, it considers the strategies for the preparation of oviductal, uterine and follicular fluid samples. Finally, it highlights the main results of quantitative proteomic studies, providing insights into the biological processes related to reproductive biology in farm animals. SIGNIFICANCE: Assisted reproductive technologies (ARTs) have become vitally important for farm animal breeding and much effort is going into the optimization and refinement of the techniques. There are also attempts to imitate physiological conditions by adding reproductive fluids or individual fluid proteins to improve in vitro procedures. A detailed knowledge of the reproductive fluid proteomes is indispensable. The present review summarizes the most widely used quantitative proteomic approaches for the analysis of fluids from the female reproductive tract and highlights the potential of quantitative proteomics to delineate reproductive processes and identify candidate proteins for ARTs in farm animals.

Technological Advances in Preclinical Drug Evaluation: The Role of -Omics Methods

Preclinical drug development is an essential step in the drug development process where the evaluation of new chemical entities occurs. In particular, preclinical drug development phases include deep analysis of drug candidates' interactions with biomolecules/targets, their safety, toxicity, pharmacokinetics, metabolism by use of assays in vitro and in vivo animal assays. Legal aspects of the required procedures are well-established. Herein, we present a comprehensive summary of current state-of-the art approaches and techniques used in preclinical studies. In particular, we will review the potential of new, -omics methods and platforms for mechanistic evaluation of drug candidates and speed-up of the preclinical evaluation steps.

Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.

Proteomics in the World of Induced Pluripotent Stem Cells

Omics approaches have significantly impacted knowledge about molecular signaling pathways driving cell function. Induced pluripotent stem cells (iPSC) have revolutionized the field of biological sciences and proteomics and, in particular, has been instrumental in identifying key elements operating during the maintenance of the pluripotent state and the differentiation process to the diverse cell types that form organisms. This review covers the evolution of conceptual and methodological strategies in proteomics; briefly describes the generation of iPSC from a historical perspective, the state-of-the-art of iPSC-based proteomics; and compares data on the proteome and transcriptome of iPSC to that of embryonic stem cells (ESC). Finally, proteomics of healthy and diseased cells and organoids differentiated from iPSC are analyzed.

From Molecules to Mechanisms: Functional Proteomics and Its Application to Renal Tubule Physiology

Classical physiological studies using electrophysiological, biophysical, biochemical, and molecular techniques have created a detailed picture of molecular transport, bioenergetics, contractility and movement, and growth, as well as the regulation of these processes by external stimuli in cells and organisms. Newer systems biology approaches are beginning to provide deeper and broader understanding of these complex biological processes and their dynamic responses to a variety of environmental cues. In the past decade, advances in mass spectrometry-based proteomic technologies have provided invaluable tools to further elucidate these complex cellular processes, thereby confirming, complementing, and advancing common views of physiology. As one notable example, the application of proteomics to study the regulation of kidney function has yielded novel insights into the chemical and physical processes that tightly control body fluids, electrolytes, and metabolites to provide optimal microenvironments for various cellular and organ functions. Here, we systematically review, summarize, and discuss the most significant key findings from functional proteomic studies in renal epithelial physiology. We also identify further improvements in technological and bioinformatics methods that will be essential to advance precision medicine in nephrology.

A gold nanocluster chemical tongue sensor array for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a common neurodegenerative disorder in elderly people, and is associated with a heavy financial burden on our society. The use of serologic biomarkers is an attractive method to diagnose AD. Although the determination of blood-based biomarkers for AD has been explored in many studies, few practical diagnosis methods have been used in the clinic. In this work, we constructed a "chemical tongue" sensor array that is easy to use and based on four kinds of fluorescent gold nanoclusters (Au NCs) for discriminating between multiple proteins at nanomolar concentrations. The device utilizes a linear discrimination analysis based on fluorescence intensity response patterns. Using this chemical tongue sensor array, multiple proteins can be confidently identified even in complex biological systems, such as human urine. Most importantly, sera of AD patients could be effectively discriminated from those of osteoarthritis patients, or of healthy people. Also, the results obtained for the AD patients by the chemical tongue sensor array were validated by CSF determination. We conclude that the chemical tongue sensor array manufactured in this work paves the way for designing an auxiliary diagnosis method for AD that is less invasive and more convenient for the large-scale screening of patients.

Gel-based proteomics in disease research: Is it still valuable?

Gel electrophoresis had been the primary method in proteomics. In the early era of proteomics, gel electrophoresis was a dominant technique of sample preparation for mass spectrometry analysis. Particularly, two-dimensional electrophoresis provided high-resolution proteome separation, and was regarded as the standard methodology for the separation of wide-range proteomes. However, gel electrophoresis turned downwards due to the progress of other separations including liquid chromatography and ionization techniques, resulting gel-free proteomics finally becoming dominant players at present. There are numerous advantages in gel-free approach in aspects of current trends of disease research. Interestingly, gel-free approaches are still advanced, it seems that gel electrophoresis will not be disappeared. The unique features of gel electrophoresis can be complementary for gel-free and it is suitable for the new wave of top-down functional proteomics.

Biophysical, Biochemical, and Cell Based Approaches Used to Decipher the Role of Carbonic Anhydrases in Cancer and to Evaluate the Potency of Targeted Inhibitors

Carbonic anhydrases (CAs) are thought to be important for regulating pH in the tumor microenvironment. A few of the CA isoforms are upregulated in cancer cells, with only limited expression in normal cells. For these reasons, there is interest in developing inhibitors that target these tumor-associated CA isoforms, with increased efficacy but limited nonspecific cytotoxicity. Here we present some of the biophysical, biochemical, and cell based techniques and approaches that can be used to evaluate the potency of CA targeted inhibitors and decipher the role of CAs in tumorigenesis, cancer progression, and metastatic processes. These techniques include esterase activity assays, stop flow kinetics, and mass inlet mass spectroscopy (MIMS), all of which measure enzymatic activity of purified protein, in the presence or absence of inhibitors. Also discussed is the application of X-ray crystallography and Cryo-EM as well as other structure-based techniques and thermal shift assays to the studies of CA structure and function. Further, large-scale genomic and proteomic analytical methods, as well as cell based techniques like those that measure cell growth, apoptosis, clonogenicity, and cell migration and invasion, are discussed. We conclude by reviewing approaches that test the metastatic potential of CAs and how the aforementioned techniques have contributed to the field of CA cancer research.

A Network Hierarchy-Based method for functional module detection in protein-protein interaction networks

In the post-genomic era, one of the important tasks is to identify protein complexes and functional modules from high-throughput protein-protein interaction data, so that we can systematically analyze and understand the molecular functions and biological processes of cells. Although a lot of functional module detection studies have been proposed, how to design correctly and efficiently functional modules detection algorithms is still a challenging and important scientific problem in computational biology. In this paper, we present a novel Network Hierarchy-Based method to detect functional modules in PPI networks (named NHB-FMD). NHB-FMD first constructs the hierarchy tree corresponding to the PPI network and then encodes the tree such that genetic algorithm is employed to obtain the hierarchy tree with Maximum Likelihood. After that functional module partitioning is performed based on it and the best partitioning is selected as the result. Experimental results in the real PPI networks have shown that the proposed algorithm not only significantly outperforms the state-of-the-art methods but also can detect protein modules more effectively and accurately.

Target Identification Using Cell Permeable and Cleavable Chloroalkane Derivatized Small Molecules

An important aspect for gaining functional insight into the activity of small molecules revealed through phenotypic screening is the identification of their interacting proteins. Yet, isolating and validating these interacting proteins remains difficult. Here, we present a new approach utilizing a chloroalkane (CA) moiety capture handle, which can be chemically attached to small molecules to isolate their respective protein targets. Derivatization of small molecules with the CA moiety has been shown to not significantly impact their cell permeability or potency, allowing for phenotypic validation of the derivatized small molecule prior to capture. The retention of cell permeability also allows for treatment of live cells with the derivatized small molecule and the CA moiety enables rapid covalent capture onto HaloTag coated magnetic beads. Additionally, several options are available for the elution of interacting proteins, including chemical cleavage of the CA moiety, competitive elution using excess unmodified small molecule, or sodium dodecyl sulfate (SDS) elution. These features taken together yield a highly robust and efficient process for target identification, including capture of weak or low abundance interactors.