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PubMed2026

Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.

Humans↗
PubMed2004

Two-dimensional protein database of human pancreas.

We report here the two-dimensional protein database of human pancreas. The proteins were analyzed by two-dimensional electrophoresis followed by matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS). Totally, 302 proteins were identified, of which about 27% were enzymes with a broad range of catalytic activities. Several of these are specifically expressed in pancreas, such as pancreatic amylase, pancreatic stone protein, pancreatitis-associated protein, pancreatic lipase, pancreatic elastase, etc. Structural and cytoskeletal proteins are also strongly represented on the gels. Thus, the pancreatic proteome reflects the organ's function. This work paves the way for further studies on pancreatic protein expression in health and disease, such as diabetes and pancreatic cancer.

Databases, Protein↗
PubMed2002

Structural proteomics: lessons learnt from the early case studies.

The genomics efforts have identified a large number of novel genes and thus provided a pool of interesting but not functionally characterized target proteins. It has been suggested that structural proteomics will significantly impact the success rate of functional characterization of such identified genes and proteins by providing structure-function hypotheses by fold and feature recognition and analysis. Structural proteomics initiatives, both in academic and industrial settings, are today generating protein structures at an unprecedented rate although relatively few large-scale efforts have been displayed in the public domain. However, a number of individual studies have provided a 'road-map' for selected approaches that hold the promise to significantly impact the process of deriving function from structure.

Crystallography, X-Ray↗
PubMed2006

Proteome survey reveals modularity of the yeast cell machinery.

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.

Genome, Fungal↗
PubMed2002

Primer on medical genomics. Part IV: Expression proteomics.

Proteomics, simply defined, is the study of proteomes. More completely, proteomics is defined as the study of all proteins, including their relative abundance, distribution, posttranslational modifications, functions, and interactions with other macromolecules, in a given cell or organism within a given environment and at a specific stage in the cell cycle. Proteins carry out the biological functions encoded by genes; hence, once the initial stage of genome sequencing and gene discovery is completed, a study of the proteome must be undertaken to address fundamental biological questions. The 3 broad areas are expression proteomics, which catalogues the relative abundance of proteins; cell-mapping or cellular proteomics, which delineates functional protein-protein interactions and organelle-specific protein distribution; and structural proteomics, which characterizes the 3-dimensional structure of proteins. With these approaches, proteins are studied on a global scale using a synergistic combination of powerful, high-throughput technologies, including 2-dimensional polyacrylamide gel electrophoresis, mass spectrometry, multidimensional liquid chromatography, and bioinformatics. Mass spectrometry, which provides highly accurate molecular mass measurements, has emerged as the analytical technology of choice for protein identification, characterization, and sequencing. This task has been made considerably easier with the availability of complete, nonredundant, and annotated genome sequence databases for many organisms. This article reviews the area of expression proteomics.

Biotechnology↗
PubMed2002

Proteomics and its trends facing nature's complexity.

The complexity of nature is tremendous, particularly at the epigenetic level. Proteomic studies must therefore complement genomic discoveries to better understand biological processes. Because of the very large number of modified proteins and their great variability in physico-chemical properties, no single method can be used to analyze all of them. Mass spectrometry has demonstrated its superior ability to rapidly identify and partially characterize numerous proteins in low abundance and has become a central element in most proteomic projects. Studies of protein function are necessary to understand biological pathways and this is being tackled using several approaches such as two hybrid systems, phage technology or affinity methods. Finally, mathematical and bioinformatic developments will be essential to study nature's complex systems.

Computational Biology↗
PubMed2006

Genomic and post-genomic approaches to polycystic ovary syndrome--progress so far: Mini Review.

Genomic studies in polycystic ovary syndrome (PCOS) have focused on ovarian tissues and gene expression changes related to the gynaecological manifestations of PCOS. These studies have revealed a variety of altered genes that fall into many functional categories. Of these, the genes involved in steroidogenesis, including genes related to retinoic acid biosynthesis and LH-stimulated gene pathways, are generally up-regulated in PCOS samples. Genes involved in the Wnt signalling pathway appear down-regulated. Immune response genes and those involved in apoptosis are altered, but the net effect of these alterations is unclear at present. However, these altered gene expression patterns are yet to produce a defined aetiological basis or diagnostic biomarker for PCOS. The use of proteomic technologies for the study of the PCOS proteome is in its infancy; however, a few pilot studies have been published and the data are reviewed. Proteomics looks directly at the functional units within a cell, the proteins. This approach should thus serve to validate some of the gene expression changes identified and then build on the genomic results collected to date.

DNA↗
PubMed2003

Targeted cellular process profiling approach for uterine leiomyoma using cDNA microarray, proteomics and gene ontology analysis.

This study utilized both cDNA microarray and two-dimensional protein gel electrophoresis technology to investigate the multiple interactions of genes and proteins involved in uterine leiomyoma pathophysiology. Also, the gene ontology analysis was used to systematically characterize the global expression profiles at cellular process levels. We profiled differentially expressed transcriptome and proteome in six-paired leiomyoma and normal myometrium. Screening up to 17 000 genes identified 21 upregulated and 50 downregulated genes. The gene-expression profiles were classified into mutually dependent 420 functional sets, resulting in 611 cellular processes according to the gene ontology. Also, protein analysis using two-dimensional gel electrophoresis identified 33 proteins (17 upregulated and 16 downregulated) of more than 500 total spots, which was classified into 302 cellular processes. Of these functional profilings, downregulations of transcriptomes and proteoms were shown in cell adhesion, cell motility, organogenesis, enzyme regulator, structural molecule activity and response to external stimulus functional activities that are supposed to play important roles in pathophysiology. In contrast, the upregulation was only shown in nucleic acid-binding activity. Taken together, potentially significant pathogenetic cellular processes were identified and showed that the downregulated functional profiling has a significant impact on the discovery of pathogenic pathway in leiomyoma. Also, the gene ontology analysis can overcome the complexity of expression profiles of cDNA microarray and two-dimensional protein analysis via its cellular process-level approach. Therefore, a valuable prognostic candidate gene with relevance to disease-specific pathogenesis can be found at cellular process levels.

Cell Communication↗
PubMed2004

Adipose tissue as a regulator of energy balance.

Adipose tissue plays an active role in energy balance because it is not only a lipid storing and mobilizing tissue but consists of functionally specialized tissues able to produce heat (in brown adipose tissue) and to produce or release a vast number of so called adipokines or adipocytokines. These consist of polypeptides but also non-protein factors and are metabolically active molecules belonging to different functional categories like immunity (complement factors, haptoglobin), endocrine function (leptin, sex steroids, various growth factors), metabolic function (fatty acids, adiponectin, resistin), and cardiovascular function (angiotensinogen, PAI-1). Recent advances using genomic and proteomic approaches have identified numerous new adipocyte secreted factors whose function remain to be established. Too little as well as too much adipose tissue leads to metabolic disturbances like insulin resistance. Visceral obesity is especially strongly correlated with the development of diabetes, hypertension and cardio-vascular disease. Thermogenesis in brown adipose tissue is a means to dissipate excess energy, but in adult humans brown fat is very scarce and probably not functional. However, human white adipose tissue contains mesenchymal stem cells, and if these could be stimulated to differentiate into brown adipocytes, increased energy expenditure in white fat could help to shift energy balance towards a more negative state.

Adipose Tissue↗
PubMed2004

Mapping the dynamic organization of the nuclear pore complex inside single living cells.

Most cellular activities are executed by multi-protein complexes that form the basic functional modules of their molecular machinery. Proteomic approaches can provide an evermore detailed picture of their composition, but do not reveal how these machines are organized dynamically to accomplish their biological function. Here, we present a method to determine the dissociation rates of protein subunits from complexes that have a traceable localization inside single living cells. As a case study, we systematically analysed the dynamic organization of vertebrate nuclear pore complexes (NPCs), large supramolecular complexes of about 30 different polypeptides. NPC components exhibited a wide range of residence times covering five orders of magnitude from seconds to days. We found the central parts of the NPC to be very stable, consistent with a function as a structural scaffold, whereas more peripheral components exhibited more dynamic behaviour, suggesting adaptor as well as regulatory functions. The presented strategy can be applied to many multi-protein complexes and will help to characterize the dynamic behaviour of complex networks of proteins in live cells.

Cells, Cultured↗
PubMed2003

Proteomics for the identification of specifically oxidized proteins in brain: technology and application to the study of neurodegenerative disorders.

Proteomics offers the opportunity elucidate the complex protein interactions of cellular systems by studying the products of genes, i.e., proteins, and their structure, function and localization. The purpose of proteomics is to explain the information contained in the genome sequences in order to provide clues on cellular events, especially related to disease. Our proteomic approach has made possible the identification of specifically oxidized proteins in Alzheimer's disease (AD) brain, providing for the first time evidence on how oxidative stress plays a crucial role in AD-related neurodegeneration. This represents an example of the use of proteomics to solve biological problems related to disease. The field, which is still in its infancy, represents a very promising way to elucidate mechanism of disease at a protein level. However, the techniques that support its development present several limitations and require introduction of new tools and innovation in order to achieve a fast, reliable and sensitive method to understand normal biological processes and their regulation as well as these cellular properties in disease.

Alzheimer Disease↗
PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗
PubMed2003

A proteomic study of the arabidopsis nuclear matrix.

The eukaryotic nucleus has been proposed to be organized by two interdependent nucleoprotein structures, the DNA-based chromatin and the RNA-dependent nuclear matrix. The functional composition and molecular organization of the second component have not yet been resolved. Here, we describe the isolation of the nuclear matrix from the model plant Arabidopsis, its initial characterization by confocal and electron microscopy, and the identification of 36 proteins by mass spectrometry. Electron microscopy of resinless samples confirmed a structure very similar to that described for the animal nuclear matrix. Two-dimensional gel electrophoresis resolved approximately 300 protein spots. Proteins were identified in batches by ESI tandem mass spectrometry after resolution by 1D SDS-PAGE. Among the identified proteins were a number of demonstrated or predicted Arabidopsis homologs of nucleolar proteins such as IMP4, Nop56, Nop58, fibrillarins, nucleolin, as well as ribosomal components and a putative histone deacetylase. Others included homologs of eEF-1, HSP/HSC70, and DnaJ, which have also been identified in the nucleolus or nuclear matrix of human cells, as well as a number of novel proteins with unknown function. This study is the first proteomic approach towards the characterization of a higher plant nuclear matrix. It demonstrates the striking similarities both in structure and protein composition of the operationally defined nuclear matrix across kingdoms whose unicellular ancestors have separated more than one billion years ago.

Arabidopsis↗
PubMed2004

Construction of representative transcript and protein sets of human, mouse, and rat as a platform for their transcriptome and proteome analysis.

The number of mammalian transcripts identified by full-length cDNA projects and genome sequencing projects is increasing remarkably. Clustering them into a strictly nonredundant and comprehensive set provides a platform for functional analysis of the transcriptome and proteome, but the quality of the clustering and predictive usefulness have previously required manual curation to identify truncated transcripts and inappropriate clustering of closely related sequences. A Representative Transcript and Protein Sets (RTPS) pipeline was previously designed to identify the nonredundant and comprehensive set of mouse transcripts based on clustering of a large mouse full-length cDNA set (FANTOM2). Here we propose an alternative method that is more robust, requires less manual curation, and is applicable to other organisms in addition to mouse. RTPSs of human, mouse, and rat have been produced by this method and used for validation. Their comprehensiveness and quality are discussed by comparison with other clustering approaches. The RTPSs are available at .

Animals↗
PubMed2006

Searching for aging-related proteins in human dermal microvascular endothelial cells treated with anti-aging agents.

Endothelial cells constitute an interface between blood and tissue and act as a medium for active interaction between plasma and the intracellular environment for homeostasis. Aging of endothelial cells plays a significant role in the pathophysiology of age-related vascular diseases; however, precise mechanisms for senescence have not been elucidated. Proteomics allows identification of protein structures, functions, and characteristics, and can be applied to the study of aging processes. Using cultured human dermal microvascular endothelial cells and two-dimensional proteomic mapping, we studied the effects of kinetin, epigallocatechin-3-gallate, all-trans-retinoic acid, and selenium on their senescence and searched for the aging-related proteins. The treatments resulted in 68 qualitative changes and 172 quantitative changes, and we were able to identify 46 spots among them. All of the agents indicated above induced changes in the expression of moesin, rho guanosine-5'-diphosphate-dissociation inhibitor, and actin, confirmed by immunoblotting and confocal laser microscopy. As these proteins were associated with cell cycle and cytoskeleton, immunoblotting of the proteins related to cell cycle was performed. Although practical significance remains to be confirmed by in vivo research, this fundamental discovery may provide a basis for understanding the mechanism of aging and age-related diseases.

Aging↗
PubMed2006

Protein biomarkers associated with acute renal failure and chronic kidney disease.

Acute renal failure (ARF) as well as chronic kidney disease (CKD) are currently categorized according to serum creatinine concentrations. Serum creatinine, however, has shortcomings because of its low predictive values. The need for novel markers for the early diagnosis and prognosis of renal diseases is imminent, particularly for markers reflecting intrinsic organ injury in stages when glomerular filtration is not impaired. This review summarizes protein markers discussed in the context of ARF as well as CKD, and provides an overview on currently available discovery results following 'omics' techniques. The identified set of candidate marker proteins is discussed in their cellular and functional context. The systematic review of proteomics and genomics studies revealed 56 genes to be associated with acute or chronic kidney disease. Context analysis, i.e. correlation of biological processes and molecular functions of reported kidney markers, revealed that 15 genes on the candidate list were assigned to the most significant ontology groups: immunity and defence. Other significantly enriched groups were cell communication (14 genes), signal transduction (22 genes) and apoptosis (seven genes). Among 24 candidate protein markers, nine proteins were also identified by gene expression studies. Next generation candidate marker proteins with improved diagnostic and prognostic values for kidney diseases will be derived from whole genome scans and protemics approaches. Prospective validation still remains elusive for all proposed candidates.

Acute Kidney Injury↗
PubMed2006

Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors.

Components of multiprotein complexes are routinely determined by using proteomic approaches. However, this information lacks functional content except when new complex members are identified. To analyze quantitatively the abundance of proteins in human Mediator we used normalized spectral abundance factors generated from shotgun proteomics data sets. With this approach we define a common core of mammalian Mediator subunits shared by alternative forms that variably associate with the kinase module and RNA polymerase (pol) II. Although each version of affinity-purified Mediator contained some kinase module and RNA pol II, Mediator purified through F-Med26 contained the most RNA pol II and the least kinase module as demonstrated by the normalized spectral abundance factor approach. The distinct forms of Mediator were functionally characterized by using a transcriptional activity assay, where F-Med26 Mediator/RNA pol II was the most active. This method of protein complex visualization has important implications for the analysis of multiprotein complexes and assembly of protein interaction networks.

HeLa Cells↗
PubMed2006

Reverse genetics for proteomics: from proteomic discovery to scientific content.

Proteomic strategies generally result in rather descriptive outcomes. Although proteomic patterns may lead to understanding of cellular function or failure, they do not provide insights into cellular mechanisms of normal physiology or molecular mechanisms of disease. The integration of single events or patterns into complex biological processes and signaling networks must be functionally validated by studying these processes in a dynamic physiological context. Effective methodologies, applied at the level of intact cells and model organisms that monitor their function, topology and interaction of proteins are necessary and essential. Reverse genetic strategies can greatly advance the understanding of patterns identified through proteomic analysis. RNAi as an accessible method for routine functional analysis of cells and whole model organisms represents a quantum leap in revolutionizing reverse genetics. This paper aims at describing an integrated workflow linking proteome-based discovery to molecular analysis of single genes and proteins by RNAi, potentially contributing to the advanced understanding of molecular mechanisms in neurodegenerative disease, including Parkinson's disease.

Animals↗