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PubMed2004

An automated high performance capillary liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometer for high-throughput proteomics.

We describe a fully automated high performance liquid chromatography 9.4 tesla Fourier transform ion resonance cyclotron (FTICR) mass spectrometer system designed for proteomics research. A synergistic suite of ion introduction and manipulation technologies were developed and integrated as a high-performance front-end to a commercial Bruker Daltonics FTICR instrument. The developments incorporated included a dual-ESI-emitter ion source; a dual-channel electrodynamic ion funnel; tandem quadrupoles for collisional cooling and focusing, ion selection, and ion accumulation, and served to significantly improve the sensitivity, dynamic range, and mass measurement accuracy of the mass spectrometer. In addition, a novel technique for accumulating ions in the ICR cell was developed that improved both resolution and mass measurement accuracy. A new calibration methodology is also described where calibrant ions are introduced and controlled via a separate channel of the dual-channel ion funnel, allowing calibrant species to be introduced to sample spectra on a real-time basis, if needed. We also report on overall instrument automation developments that facilitate high-throughput and unattended operation. These included an automated version of the previously reported very high resolution, high pressure reversed phase gradient capillary liquid chromatography (LC) system as the separations component. A commercial autosampler was integrated to facilitate 24 h/day operation. Unattended operation of the instrument revealed exceptional overall performance: Reproducibility (1-5% deviation in uncorrected elution times), repeatability (<20% deviation in detected abundances for more abundant peptides from the same aliquot analyzed a few weeks apart), and robustness (high-throughput operation for 5 months without significant downtime). When combined with modulated-ion-energy gated trapping, the dynamic calibration of FTICR mass spectra provided decreased mass measurement errors for peptide identifications in conjunction with high resolution capillary LC separations over a dynamic range of peptide peak intensities for each spectrum of 10(3), and >10(5) for peptide abundances in the overall separation.

Amino Acid Sequence↗

PubMed2026

A distinct effector B cell population drives autoantibody production in SARS-CoV-2 infection.

Autoantibodies (autoAbs) are linked to mortality and Long COVID, yet their cellular origins remain unclear. We analyzed the INCOV cohort and identified 12 age- and sex-matched participants with varying autoAb abundance and integrated single-cell RNA-seq and ATAC-seq data from B cells, plasma proteomics, proteome-wide autoAb profiling, clinical data, and in vitro assays. AutoAb abundance inversely correlated with neutralizing IgG and declined as infection resolved, paralleling the contraction of atypical memory B cells (AtMs). In vitro, AtMs preferentially differentiated into autoAb-producing antibody-secreting cells upon TLR7/8 stimulation. CD11c+ AtMs (double-negative 2, DN2s) in autoAb-high individuals exhibited increased TLR7 signaling, oxidative stress, and isotype switching, regulated by transcription factors T-bet and XBP1. Integrated genetic and genomic analyses showed that DN2s had the strongest enrichment for autoimmune trait heritability and inferred regulatory effects of autoimmune risk variants among B cell subsets. These findings identify DN2s as key precursors of autoAb-producing cells during SARS-CoV-2 infection.

B cell↗

PubMed2006

Recent developments in proteomics: implications for the study of cardiac hypertrophy and failure.

The key components to the molecular understanding of the pathophysiology of various forms of heart failure involve global and/or large-scale identifications of proteins, their patterns of expression, posttranslational modifications, and functional characterization. Particularly, proteins involved in the induction of cardiac (mal)adaptive hypertrophic growth, interstitial fibrosis, and contractile dysfunction are of interest. In general, with the accumulation of vast amounts of DNA sequences in databases, researchers have become aware that merely having complete sequences of genomes and transcriptional changes for thousands of genes simultaneously will not be sufficient to elucidate, in molecular terms, the etiology and pathophysiology of cardiovascular disease. In the last decade, a new technology called proteomics has become available that allows biological and (patho)physiological questions to be approached exclusively from the protein perspective. Proteomics may enable us to map the entire complement of proteins expressed by the heart at any time and condition. This approach creates the unique possibility to identify, by differential analysis, protein alterations associated with the etiology of heart disease and its progression, outcome, and response to therapy. To illustrate the true power of proteomics, most of the currently available methodologies are first reviewed, including their limitations. This review also deals with the current status and the perspectives of proteomics applications in research on heart failure in general. Furthermore, examples of our recent data on global protein profiling of the pressure-overloaded rat right ventricle and of endothelin-1-stimulated cultures of neonatal rat cardiac myocytes are provided. The last section is devoted to the continuous advances in proteomic technologies, including protein separation methods, mass spectrometric instrumentation, computational analysis, and bioinformatic tools, together with integrative databases.

Cardiomegaly↗

PubMed2026

Multi-omic analyses of the same sample using metabolomics, lipidomics, proteomics, phosphoproteomics, and glycoproteomics.

Mass spectrometry (MS)-based multi-omics offers powerful tools to comprehensively characterize proteins, post-translational modifications, metabolites, and lipids. However, these measurements are typically performed using separate sample preparation workflows and modality-specific liquid chromatography mass spectrometry (LC-MS) platforms, limiting integration and constraining applications to small amounts of sample materials, especially scarce clinical specimens. Here, we describe a unified nano-LC-MS framework that enables metabolomic, lipidomic, proteomic, phosphoproteomic, and glycoproteomic analyses from the same starting material using a single nano-LC-MS platform, with only the chromatographic conditions, acquisition methods, and enrichment procedures tailored to each omics. This integrated strategy reduces workflow complexity and sample consumption while improves analytical continuity across molecular layers. By enabling deep multi-omics characterization from the same sample, this platform provides a practical foundation for comprehensive analysis of precious clinical samples.

Proteomics↗

PubMed2006

Universal metrics for quality assessment of protein identifications by mass spectrometry.

Increasing numbers of large proteomic datasets are becoming available. As attempts are made to interpret these datasets and integrate them with other forms of genomic data, researchers are becoming more aware of the importance of data quality with respect to protein identification. We present three simple and universal metrics that describe different aspects of the quality of protein identifications by peptide mass fingerprinting. Hit ratio gives an indication of the signal-to-noise ratio in a mass spectrum, mass coverage measures the amount of protein sequence matched, and excess of limit-digested peptides reflects the completeness of the digestion that precedes the peptide mass fingerprinting. Receiver-operating characteristic plots show that the novel metric, excess of limit-digested peptides, can discriminate between correct and random matches more accurately than search score when validating the results from a state-of-the-art protein identification software system (Mascot) especially when combined with the two other metrics, hit ratio and mass coverage. Recommendations are made regarding the use of the metrics when reporting protein identification experiments.

Mass Spectrometry↗

PubMed2006

Integrating metabolomics and transcriptomics for probing SE anticancer mechanisms.

Transcriptomics provides the tool for deciphering gene expression networks, and proteomics links these networks to protein products. The third crucial partner is metabolomics, which defines the metabolic network(s) linked to gene expression. NMR and mass spectrometry enable the broad screen analysis of the metabolome and its transformation pathways, transcending classical targeted metabolic studies. These tools were combined to investigate the anticancer mechanisms of different selenium forms in human lung cancer cells. Using 2-D NMR and tandem-MS, we mapped perturbations of 13C labeling patterns in numerous metabolites induced by selenite and selenomethionine. This information was used to interpret selenite-induced changes in gene expression networks. Linking metabolic dysfunctions to altered gene expression profiles provided new insights into the regulatory network underlying the metabolic dysfunctions, enabled the assembly of discrete gene expression events into functional pathways, and revealed protein targets for proteomic analysis.

Animals↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2004

YETI: Yeast Exploration Tool Integrator.

UNLABELLED: Yeast Exploration Tool Integrator (YETI) is a novel bioinformatics tool for the integrated visualization and analysis of functional genomic data sets from the budding yeast Saccharomyces cerevisiae. AVAILABILITY: YETI is freely available for use over the WWW, or download under license, at http://www.bru.ed.ac.uk/~orton/yeti.html

Computer Graphics↗

PubMed2006

Redox proteomics in some age-related neurodegenerative disorders or models thereof.

Neurodegenerative diseases cause memory loss and cognitive impairment. Results from basic and clinical scientific research suggest a complex network of mechanisms involved in the process of neurodegeneration. Progress in treatment of such disorders requires researchers to better understand the functions of proteins involved in neurodegenerative diseases, to characterize their role in pathogenic disease mechanisms, and to explore their roles in the diagnosis, treatment, and prevention of neurodegenerative diseases. A variety of conditions of neurodegenerative diseases often lead to post-translational modifications of proteins, including oxidation and nitration, which might be involved in the pathogenesis of neurodegenerative diseases. Redox proteomics, a subset of proteomics, has made possible the identification of specifically oxidized proteins in neurodegenerative disorders, providing insight into a multitude of pathways that govern behavior and cognition and the response of the nervous system to injury and disease. Proteomic analyses are particularly suitable to elucidate post-translational modifications, expression levels, and protein-protein interactions of thousands of proteins at a time. Complementing the valuable information generated through the integrative knowledge of protein expression and function should enable the development of more efficient diagnostic tools and therapeutic modalities. Here we review redox proteomic studies of some neurodegenerative diseases.

Aged↗

PubMed2006

The PeptideAtlas project.

The completion of the sequencing of the human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to archive proteomic data in a repository that enables the exchange of data among researchers and also accurate integration with genomic data. PeptideAtlas (http://www.peptideatlas.org/) addresses these needs by identifying peptides by tandem mass spectrometry (MS/MS), statistically validating those identifications and then mapping identified sequences to the genomes of eukaryotic organisms. A meaningful comparison of data across different experiments generated by different groups using different types of instruments is enabled by the implementation of a uniform analytic process. This uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications. The raw data from many diverse proteomic experiments are made available in the associated PeptideAtlas repository in several formats. Here we present a summary of our process and details about the Human, Drosophila and Yeast PeptideAtlas builds.

Animals↗

PubMed2004

Comparative proteomic analysis of human whole saliva.

Human saliva performs a wide variety of biological functions that are critical for the maintenance of the oral health. Various functions include lubrication, buffering, antimicrobial protection, and the maintenance of mucosal integrity. In addition, whole saliva may be analysed for the diagnosis of human systemic diseases, since it can be readily collected and contains identifiable serum constituents. By using proteomic approach, we have established a reference proteome map of human whole saliva allowing for the resolution of greater than 200 protein spots in a single two-dimensional polyacrylamide gel. Fifty-four protein spots, comprised of 26 different proteins, were identifies using N-terminal sequencing, mass spectrometry, and/or computer matching with protein database. Ten proteins, whose levels were significantly different when bleeding had occurred in the oral cavity, were discussed in this study. These 10 proteins include alpha-1-antrypsin, apolipoprotein A-I, cystatin A, SA, SA-III, and SN, enolase I, hemoglobin beta-chain, thioredoxin peroxiredoxin B, as well as a prolactin-inducible protein. The proteomic approach identifies candidates from human whole saliva that may prove to be of diagnostic and therapeutic significance.

Adult↗

PubMed2001

The Medicago Genome Initiative: a model legume database.

The Medicago Genome Initiative (MGI) is a database of EST sequences of the model legume MEDICAGO: truncatula. The database is available to the public and has resulted from a collaborative research effort between the Samuel Roberts Noble Foundation and the National Center for Genome Resources to investigate the genome of M.truncatula. MGI is part of the greater integrated MEDICAGO: functional genomics program at the Noble Foundation (http://www.noble.org ), which is taking a global approach in studying the genetic and biochemical events associated with the growth, development and environmental interactions of this model legume. Our approach will include: large-scale EST sequencing, gene expression profiling, the generation of M.truncatula activation-tagged and promoter trap insertion mutants, high-throughput metabolic profiling, and proteome studies. These multidisciplinary information pools will be interfaced with one another to provide scientists with an integrated, holistic set of tools to address fundamental questions pertaining to legume biology. The public interface to the MGI database can be accessed at http://www.ncgr.org/research/mgi.

Computational Biology↗

PubMed2003

Anticancer drug discovery using chemical genomics.

Cancer is the leading cause of death in United States and World wide. Drug discovery and development for cancer therapeutics takes several years before a patient is benefited from a new drug. The average time length from start to finish is approximately 15 years. This time length includes 4-5 years of basic research, discovery, preclinical development and validation studies. Next, it takes approximately 7-10 years for a drug to go through human clinical trials. This time length is too long and need to be shortened to benefit patients quickly from new technologies and product development ideas. Furthermore, with the recent explosion of genomics and proteomics information, it is now becoming difficult to make rapid and logical decisions on hundreds of potential drug targets available. Thus, there is immediate need to develop and integrate tools and technologies that will not only reduce the time length but also the risk of late clinical drug failure. Chemical Genomics is an emerging field in which tools and technologies from biology and chemistry are utilized in a parallel and cyclic fashion very early in the development process. In addition chemical genomics proposes to integrate latest developments in tools and technologies from a variety of modern fields such as combinatorial chemistry, informatics, synthesis chemistries, cell based assays, microarrays, genomics and proteomics tools to accelerate drug discovery and development. Thus, in cancer therapeutics the aim of chemical genomics is not only to reduce the time length of pre-clinical development but also the risk of late clinical failure by making smart decisions early in the process.

Animals↗

PubMed2006

Resources for integrative systems biology: from data through databases to networks and dynamic system models.

In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.

Animals↗

PubMed2006

Proteomic analysis of injured spinal cord tissue proteins using 2-DE and MALDI-TOF MS.

Spinal cord injury (SCI) induces a progressive pathophysiology affecting cell survival and neurological integrity via complex and evolving molecular cascades whose interrelationships are not fully understood. Acute injury to the spinal cord undergoes sequential pathological change including hemorrhage, edema, axonal and neuronal necrosis, and demyelination. In the present study, we aimed to establish the proteomic profiles and characterization of the total protein expressed in traumatic injured spinal cord tissue by using 2-DE and matrix assisted laser desorption/ionization-TOF MS (MALDI-TOF MS). We performed proteomic analysis using 2-DE and MS to describe total proteins and differential proteins expression between normal and traumatic injured spinal cord tissues. The study discovered 947 total proteins and analyzed 219 and 270 proteins from normal and injured tissue, respectively. After 24 h of traumatic damage induction, the injured spinal cord tissue up-regulated over 39 proteins including neurofilament light chain, annexin 5, heat shock protein, tubulin beta, peripherin, glial fibrillary acidic protein delta, peroxiredoxin 2, and apolipoprotein A. Twenty-one proteins showed reduction. The majority of the modulated proteins belonged to the 13 functional categories. Proteins that were identified with neural functional category in injured tissue were considered most likely to be involved in wound healing response coupled with neurogenesis and gliogenesis.

Animals↗

PubMed2007

Quantitative proteomics analysis of human endothelial cell membrane rafts: evidence of MARCKS and MRP regulation in the sphingosine 1-phosphate-induced barrier enhancement.

Endothelial cell barrier dysfunction results in the increased vascular permeability observed in inflammation, tumor metastasis, angiogenesis, and atherosclerosis. Sphingosine 1-phosphate (S1P), a biologically active phosphorylated lipid growth factor released from activated platelets, enhances the endothelial cell barrier integrity in vitro and in vivo. To begin to identify the molecular mechanisms mediating S1P induced endothelial barrier enhancement, quantitative proteomics analysis (iTRAQ) was performed on membrane rafts isolated from human pulmonary artery endothelial cells in the absence or presence of S1P stimulation. Our results demonstrated that S1P mediates rapid and specific recruitment (1 microM, 5 min) of myristoylated alanine-rich protein kinase C substrate (MARCKS) and MARCKS-related protein (MRP) to membrane rafts. Western blot experiments confirmed these findings with both MARCKS and MRP. Finally, small interfering RNA-mediated silencing of MARCKS or MRP or both attenuates S1P-mediated endothelial cell barrier enhancement. These data suggest the regulation of S1P-mediated endothelial cell barrier enhancement via the cell specific localization of MARCKS and MRP and validate the utility of proteomics approaches in the identification of novel molecular targets.

Amino Acid Sequence↗

PubMed2025

Single-cell proteomics using mass spectrometry.

Over the past 2 to 3 years, mass-spectrometry-based single-cell proteomics (SCP) has experienced transformative improvements in microfluidic and robotic sample preparation, innovative MS1- and MS2-based multiplexing strategies, and specialized hardware (e.g., timsTOF Ultra 2, Astral), which have dramatically boosted sensitivity, throughput, and proteome coverage from picogram-level protein inputs. Concurrently, tailored computational workflows that encompass normalization, imputation, and no-code platforms have addressed pervasive missing data challenges and standardized analyses, collectively enabling high-throughput, reproducible profiling of cellular heterogeneity. This minireview summarizes the latest progress in SCP technology and software solutions, highlighting how the closer integration of analytical, computational, and experimental strategies will facilitate a deeper and broader coverage of single-cell proteomes.

Single-Cell Analysis↗

PubMed2026

A lipid-immune network signature defines susceptibility to asparaginase-associated pancreatitis.

BACKGROUNDAsparaginase is essential for curing acute lymphoblastic leukemia (ALL), but its use is limited by asparaginase-associated pancreatitis (AAP), a severe and unpredictable toxicity lacking validated prospective biomarkers. We sought to define early systemic molecular features of susceptibility to AAP.METHODSWe performed longitudinal lipidomic and proteomic profiling in two independent pediatric ALL cohorts (n = 161; 79 AAP cases, 82 controls) using paired blood samples collected before asparaginase exposure and at the end of induction therapy (including a single dose of asparaginase), thereby capturing pre-injury biology rather than consequences of pancreatitis. We applied differential abundance and network-based analyses and integrated lipid-cytokine associations using proteomics.RESULTSAcross cohorts, we identified a reproducible lysophosphatidylcholine-centered (LPC-centered) signature characterized by attenuated induction therapy-associated LPC responses and disruption of LPC coregulation at the network level. Proteomic profiling revealed enrichment of cytokine signaling pathways, and integrative analyses demonstrated altered lipid-cytokine coupling, including a flip in association direction for LPC species and IL-18 between cases and controls. Although IL-18/LPC ratios did not differ globally, elevated postinduction IL-18/LPC ratios identified AAP risk within a protocol-defined very high-risk ALL subgroup (AUC = 0.81).CONCLUSIONThese findings support a systems-level model in which failure of coordinated lipid-immune responses under therapeutic stress confers vulnerability to AAP, providing a framework for validation and mitigation strategies.TRIAL REGISTRATIONNCT00400946; NCT01574274; NCT03020030 (parent trials).FUNDINGServier Pharmaceuticals (IIT-95014-027-USA); SDRC (P30DK116074); Stanford SPARK; Fonds de Recherche du Québec - Santé; Fondation Charles-Bruneau; Leukemia & Lymphoma Society of Canada.

Adolescent↗