The 37TrillionCells initiative for improving global healthcare via cell-based interception and precision medicine: focus on neurodegenerative diseases.

One of the main burdens in the treatment of diseases is imputable to the delay between the appearance of molecular dysfunctions in the first affected disease cells and their presence in sufficient number for detection in specific tissues or organs. This delay obviously plays in favor of disease progression to an extent that makes efficient treatments difficult, as they arrive too late. The development of a novel medical strategy, termed cell-based interception and precision medicine, seeks to identify dysfunctional cells early, when tissue damages are not apparent and symptoms not yet present, and develop therapies to treat diseases early. Central to this strategy is the use of single-cell technologies that allow detection of molecular changes in cells at the time of phenotypical bifurcation from health to disease. In this article we describe a general procedure to support such an approach applied to neurodegenerative disorders. This procedure combines four components directed towards highly complementary objectives: 1) a high-performance single-cell proteomics (SCP) method (Detect), 2) the development of disease experimental cell models and predictive computational models of cell trajectories (Understand), 3) the discovery of specific targets and personalized therapies (Cure), and 4) the creation of a community of collaborating laboratories to accelerate the development of this novel medical paradigm (Collaborate). A global initiative named 37TrillionCells (37TC) was launched to advance the development of cell-based interception and precision medicine.

Enrichment, Characterization, and Proteomic Profiling of Small Extracellular Vesicles Derived from Human Limbal Mesenchymal Stromal Cells and Melanocytes.

Limbal epithelial progenitor cells (LEPC) rely on their niche environment for proper functionality and self-renewal. While extracellular vesicles (EV), specifically small EVs (sEV), have been proposed to support LEPC homeostasis, data on sEV derived from limbal niche cells like limbal mesenchymal stromal cells (LMSC) remain limited, and there are no studies on sEVs from limbal melanocytes (LM). In this study, we isolated sEV from conditioned media of LMSC and LM using a combination of tangential flow filtration and size exclusion chromatography and characterized them by nanoparticle tracking analysis, transmission electron microscopy, Western blot, multiplex bead arrays, and quantitative mass spectrometry. The internalization of sEV by LEPC was studied using flow cytometry and confocal microscopy. The isolated sEVs exhibited typical EV characteristics, including cell-specific markers such as CD90 for LMSC-sEV and Melan-A for LM-sEV. Bioinformatics analysis of the proteomic data suggested a significant role of sEVs in extracellular matrix deposition, with LMSC-derived sEV containing proteins involved in collagen remodeling and cell matrix adhesion, whereas LM-sEV proteins were implicated in other cellular bioprocesses such as cellular pigmentation and development. Moreover, fluorescently labeled LMSC-sEV and LM-sEV were taken up by LEPC and localized to their perinuclear compartment. These findings provide valuable insights into the complex role of sEV from niche cells in regulating the human limbal stem cell niche.

A compendium of multi-omics data illuminating host responses to lethal human virus infections.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

FLAG-KRAS4B as a Model System for KRAS4B Proteoform and PTM Evaluation by Mass Spectrometry.

Prior analysis of intact and modified protein forms (proteoforms) of KRAS4B isolated from cell lines and tumor samples by top-down mass spectrometry revealed the presence of novel posttranslational modifications (PTMs) and potential evidence of context-specific KRAS4B modifications. However, low endogenous proteoform signal resulted in ineffective characterization, making it difficult to visualize less abundant PTMs or perform follow-up PTM validation using standard proteomic workflows. The NCI RAS Initiative has developed a model system, whereby KRAS4B bearing an N-terminal FLAG tag can be stably expressed within a panel of cancer cell lines. Herein, we present a method for combining immunoprecipitation with complementary proteomic methods to directly analyze N-terminally FLAG-tagged KRAS4B proteoforms and PTMs. We provide detailed protocols for FLAG-KRAS4B purification, proteoform analysis by targeted top-down LC-MS/MS, and validation of abundant PTMs by bottom-up LC-MS/MS with example results.

Age-related changes after intracerebral hemorrhage: a comparative proteomics analysis of perihematomal tissue.

The risk factors and causes of intracerebral hemorrhage (ICH) and the degree of functional recovery after ICH are distinct between young and elderly patients. The increasing incidence of ICH in young adults has become a concern; however, research on the molecules and pathways involved ICH in subjects of different ages is lacking. In this study, tandem mass tag (TMT)-based proteomics was utilized to examine the protein expression profiles of perihematomal tissue from young and aged mice 24 h after collagenase-induced ICH. Among the 5,129 quantified proteins, ICH induced 108 and 143 differentially expressed proteins (DEPs) in young and aged mice, respectively; specifically, there were 54 common DEPs, 54 unique DEPs in young mice and 89 unique DEPs in aged mice. In contrast, aging altered the expression of 58 proteins in the brain, resulting in 39 upregulated DEPs and 19 downregulated DEPs. Bioinformatics analysis indicated that ICH activated different proteins in complement pathways, coagulation cascades, the acute phase response, and the iron homeostasis signaling pathway in mice of both age groups. Protein-protein interaction (PPI) analysis and ingenuity pathway analysis (IPA) demonstrated that the unique DEPs in the young and aged mice were related to lipid metabolism and carbohydrate metabolism, respectively. Deeper paired-comparison analysis demonstrated that apolipoprotein M exhibited the most significant change in expression as a result of both aging and ICH. These results help illustrate age-related protein expression changes in the acute phase of ICH.

Quantitative proteomics analysis reveals an important role of the transcriptional regulator UidR in the bacterial biofilm formation of Aeromonas hydrophila.

Introduction: Bacterial biofilm is a well-known characteristic that plays important roles in diverse physiological functions, whereas the current intrinsic regulatory mechanism of its formation is still largely unknown. Methods: In the present study, a label-free based quantitative proteomics technology was conducted to compare the differentially expressed proteins (DEPs) between ΔuidR and the wild-type strain in the biofilm state. Results: The results showed that the deletion of gene uidR encoding a TetR transcriptional regulator significantly increased the biofilm formation in Aeromonas hydrophila. And there was a total of 220 DEPs, including 120 up-regulated proteins and 100 down-regulated proteins between ΔuidR and the wild-type strain based on the quantitative proteomics. Bioinformatics analysis suggested that uidR may affect bacterial biofilm formation by regulating some related proteins in glyoxylic acid and dicarboxylic acid pathway. The expressions of selected proteins involved in this pathway were further confirmed by q-PCR assay, and the results was in accordance with the quantitative proteomics data. Moreover, the deletion of four genes (AHA_3063, AHA_3062, AHA_4140 and aceB) related to the glyoxylic acid and dicarboxylic acid pathway lead to a significant decrease in the biofilm formation. Discussion: Thus, the results indicated that uidR involved in the regulatory of bacterial biofilm formation, and it may provide a potential target for the drug development and a new clue for the prevention of pathogenic A. hydrophila in the future.

Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topologies.

The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome. Biochemical and imaging-based follow-up studies confirm that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. We also validate the CLASP concept in synaptic vesicles, demonstrating its applicability to different sub-cellular compartments. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, and establishes a method for concomitant profiling of sub-organelle and membrane proteomes.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Overcoming the detrimental O-acylation in TMTpro labeling improves the proteome depth and quantification precision.

BACKGROUND: With the advent of proline-based reporter isobaric Tandem Mass Tag (TMTpro) reagents, the sample multiplexing capacity of tandem mass tags (TMTs) has been expanded, and up to 18 samples can be quantified in a multiplexed manner. Like classic TMT reagents, TMTpro reagents contain a tertiary amine group, which markedly enhances their reactivity toward hydroxyl groups and results in O-acylation of serine, threonine and tyrosine residues. This overlabeling significantly compromises proteome analysis in terms of depth and precision. In particular, the reactivity of hydroxyl-containing residues can be dramatically enhanced when coexisting with a histidine in the same peptides, leading to a severe systematic bias against the analysis of these peptides. Although some protocols using a reduced molar excess of TMT under alkaline conditions can alleviate overlabeling of histidine-free peptides to some extent, they have a limited effect on histidyl- and hydroxyl-containing peptides. RESULTS: Here, we report a novel TMTpro labeling method that overcomes detrimental overlabeling while providing high labeling efficiency of amines. Additionally, our method is cost-effective, as it requires only half the amount of TMTpro reagents recommended by the reagent manufacturer. In a deep-scale analysis of a yeast/human two-proteome model sample, we compared our method with a typical alkaline labeling method using a reduced molar excess of TMTpro. Even at a depth of over 10,000 proteins, our method detected 23.7% more unique peptides and 8.7% more protein groups compared to the alkaline labeling method. Moreover, our method significantly improved the quantitative precision due to the reduced variability in labeling and increased protein sequence coverage. This substantially enhanced the statistical power of our method for detecting differentially abundant proteins, providing an average of 13% more yeast proteins that reached statistical significance. SIGNIFCANCE: We presented a novel TMTpro labeling method that overcomes the detrimental O-acylation and thus significantly improves the depth and quantitative precision for proteome analysis.

Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Salivary Biomarkers of Primary Sjögren's Syndrome.

Objective As primary Sj?gren's syndrome (pSS) primarily affects the salivary glands, saliva can serve as an indicator of the glands' pathophysiology and the disease's status. This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis.Methods The discovery set contained 49 samples (24 from pSS and 25 from age- and gender-matched healthy controls [HCs]) and the validation set included 25 samples (12 from pSS and 13 from HCs). Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio. Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition (DIA) strategy on a 2D LC?HRMS/MS platform to reveal differential proteins. The crucial proteins were verified using DIA analysis and annotated using gene ontology (GO) and International Pharmaceutical Abstracts (IPA) analysis. A prediction model for SS was established using random forests.Results A total of 1,963 proteins were discovered, and 136 proteins exhibited differential representation in pSS patients. The bioinformatic research indicated that these proteins were primarily linked to immunological functions, metabolism, and inflammation. A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm, and was validated as the predictive biomarkers exhibiting good performance with area under the curve (AUC) of 0.817 for discovery set and 0.882 for validation set.Conclusions The candidate protein panel discovered may aid in pSS diagnosis. Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients. DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.

Quantitative Pattern of hPTMs by Mass Spectrometry-Based Proteomics with Implications for Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is known for its aggressive nature, and TNBC management is currently challenging due to the lack of effective targets. Despite the importance of histone post-translational modifications (hPTMs) in breast cancer, their associations with molecular subtypes of breast cancer, especially TNBC, are poorly understood. In this study, a combination of untargeted and targeted proteomics approaches, supplemented by a derivatization method, was applied to breast cancer cells and tissue samples. Untargeted proteomics of eight breast cancer cell lines belonging to different molecular subtypes revealed 36 modified peptides with 12 lysine modification sites in histone H3, and the most frequently reported top 5 histone H3 methylation and acetylation sites were covered. Then, targeted proteomics was carried out to quantify the total 20 target hPTMs at the covered modification sites (i.e., mono-, di-, trimethylation, and acetylation for each site), indicating the difficulty in distinguishing TNBC cells from normal cells. Subsequently, the analysis in TNBC patients revealed significant expression differences in 4 specific hPTMs (H3K14ac, H3K27me1, H3K36me2, and H3K36me3) between TNBC and adjacent normal tissue samples. These unique hPTM patterns allowed for the differentiation of TNBC from normal cases. This finding provides promising implications for advancing targeted treatment strategies for TNBC in the future.

Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics.

The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.

Gehua Jiejiu Dizhi decoction ameliorates alcoholic fatty liver in mice by regulating lipid and bile acid metabolism and with exertion of antioxidant stress based on 4DLabel-free quantitative proteomic study.

OBJECTIVE: To analyze the effect and molecular mechanism of Gehua Jiejiu Dizhi decoction (, GJDD) on alcoholic fatty live disease (AFLD) by using proteomic methods. METHODS: The male C57BL/6J mouse were randomly divided into four groups: control group, model group, GJDD group and resveratrol group. After the AFLD model was successfully prepared by intragastric administration of alcohol once on the basis of the Lieber-DeCarli classical method, the GJDD group and resveratrol group were intragastrically administered with GJDD (4900 mg/kg) and resveratrol (400 mg/kg) respectively, once a day for 9 d. The fat deposition of liver tissue was observed and evaluated by oil red O (ORO) staining. 4DLabel-free quantitative proteome method was used to determine and quantify the protein expression in liver tissue of each experimental group. The differentially expressed proteins were screened according to protein expression differential multiples, and then analyzed by Gene ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Finally, expression validation of the differentially co-expressed proteins from control group, model group and GJDD group were verified by targeted proteomics quantification techniques. RESULTS: In semiquantitative analyses of ORO, all kinds of steatosis (ToS, MaS, and MiS) were evaluated higher in AFLD mice compared to those in GJDD or resveratrol-treated mice. 4DLabel-free proteomics analysis results showed that a total of 4513 proteins were identified, of which 3763 proteins were quantified and 946 differentially expressed proteins were screened. Compared with the control group, 145 proteins were up-regulated and 148 proteins were down-regulated in the liver tissue of model group. In addition, compared with the model group, 92 proteins were up-regulated and 135 proteins were down-regulated in the liver tissue of the GJDD group. 15 differentially co-expressed proteins were found between every two groups (model group vs control group, GJDD group vs model group and GJDD group vs control group), which were involved in many biological processes. Among them, 11 differentially co-expressed key proteins (Aox3, H1-5, Fabp5, Ces3a, Nudt7, Serpinb1a, Fkbp11, Rpl22l1, Keg1, Acss2 and Slco1a1) were further identified by targeted proteomic quantitative technology and their expression patterns were consistent with the results of 4D label-free proteomic analysis. CONCLUSIONS: Our study provided proteomics-based evidence that GJDD alleviated AFLD by modulating liver protein expression, likely through the modulation of lipid metabolism, bile acid metabolism and with exertion of antioxidant stress.

Simple method for isolation and culture of primary buffalo (Bubalus bubalis) endometrial epithelial cells (pBuEECs) and its characterization using high throughput proteomics approach.

Early embryonic mortality resulting from insufficient interaction between the embryo and the uterus leads to the failure of pregnancy in livestock animals. Thus, it is imperative to comprehend the multifaceted process of implantation at molecular levels, which requires synchronized feto-maternal interaction. The in-vitro models serve as valuable tools to investigate the specific stages of implantation. The present study was undertaken to develop a simple method to isolate and culture the primary buffalo endometrial epithelial cells (pBuEECs), followed by proteome profiling of the proliferating cells. Collagenase I was used to separate uterine epithelial cells (UECs) from the ipsilateral uterine horn, and then the cells were separated using a cell strainer. After being seeded on culture plates, UECs developed colonies with characteristic epithelial shape and expressed important markers such as cytokeratin 18 (KRT18), progesterone receptor (PGR), ß-estrogen receptor (ESR1), and leukemia inhibitory factor (LIF), which were confirmed by PCR. The purity of epithelial cells was assessed using cytokeratin 18 immunostaining, which indicated approximately 99% purity in cultured cells. The proteome profiling of pBuEECs via high-throughput tandem mass spectrometry (MS), identified a total of 3383 proteins. Bioinformatics analysis revealed enrichment in various biological processes, including cellular processes, metabolic processes, biological regulation, localization, signaling, and developmental processes. Moreover, the KEGG pathway analysis highlighted associations with the ribosome, proteosome, oxidative phosphorylation, spliceosome, and cytoskeleton regulation pathways. In conclusion, these well characterized cells offer valuable in-vitro model to enhance the understanding of implantation and uterine pathophysiology in livestock animals, particularly buffaloes.

Data-Independent Acquisition Peptidomics.

In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework "Nextflow" ( https://nextflow.io ). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user's interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.

Quantitative Peptidomics Using Reductive Methylation of Amines.

A number of different approaches have been used for quantitative peptidomics. In this protocol, we describe the method in which peptides are reacted with formaldehyde and sodium cyanoborohydride, which converts primary and secondary amines into tertiary amines. By using different combinations of regular reagents, deuterated reagents (2H), and reagents containing deuterium and 13C, it is possible to produce five isotopically distinct forms of the methylated peptides, which can be quantified by mass spectrometry. Peptides with free N-termini that are primary amines incorporate two methyl groups using this procedure, which differ by 2 Da for each of the five isotopic combinations. Peptides that contain unmodified lysine residues incorporate additional pairs of methyl groups, leading to larger mass differences between isotopic forms. The reagents are commercially available, relatively inexpensive, and chemically stable.

Quantitative Peptidomics: General Considerations.

Peptidomics is the detection and identification of the peptides present in a sample, and quantitative peptidomics provides additional information about the amounts of these peptides. It is possible to perform absolute quantitation of peptide levels in which the biological sample is compared to synthetic standards of each peptide. More commonly, relative quantitation is performed to compare peptide levels between two or more samples. Relative quantitation can measure differences between all peptides that are detectable, which can exceed 1000 peptides in a complex sample. In this chapter, various techniques used for quantitative peptidomics are described along with discussion of the advantages and disadvantages of each approach. A guide to selecting the optimal quantitative approach is provided, based on the goals of the experiment and the resources that are available.

Origins, Technological Advancement, and Applications of Peptidomics.

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Peptidomics of Zebrafish Brain in a 6-OHDA-Induced Neurodegeneration Model.

Bioactive peptides such as neuropeptides and peptide hormones are largely understood in their involvement in a variety of physiologic systems. In addition to the neuropeptides produced and processed by the classic secretory pathway, intracellular peptides (InPeps) have shown biological activity in studies involving different organisms. A model that has become attractive in many research fields is the zebrafish (Danio rerio), which has allowed correlating behavioral responses or physiological processes with underlying molecular pathways or signaling cascades, improving the understanding of homeostasis mechanisms of the central nervous system, as well as pathological processes such as neurodegenerative diseases. Here, we provide a detailed description of the protocol of treatment with 6-OHDA, which mimics some features of Parkinson's Disease, as well as the validation of the treatment by evaluation of the locomotor activity and the protocol of peptide extraction followed by isotopic labeling to peptide relative quantitation by mass spectrometry.