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PubMed2004

Data standards for proteomics: mitochondrial two-dimensional polyacrylamide gel electrophoresis data as a model system.

Proteomics has emerged as a major discipline that led to a re-examination of the need for consensus and a nationally sanctioned set of proteomics technology standards. Such standards for databases and data reporting may be applied to two-dimensional polyacrylamide gel electrophoresis (2D PAGE) technology as a pilot project for assessing global and national needs in proteomics, and the role of the National Institute of Standards and Technology (NIST) and other similar standards and measurement organizations. The experience of harmonizing the heterogeneous data included in the Protein Data Bank (PDB) provides a paradigm for technology in an area where significant heterogeneity in technical detail and data storage has evolved. Here we propose an approach toward standardizing mitochondrial 2D PAGE data in support of a globally relevant proteomics consensus.

Journal Article↗
PubMed2001

The human proteomics initiative (HPI).

The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focussed on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry.

Algorithms↗
PubMed2001

Proteomics: a technology-driven and technology-limited discovery science.

An emerging field for the analysis of biological systems is the study of the complete protein complement of the genome, the 'proteome'. There are several complementary tools available for proteome analysis including 2D protein electrophoresis and mass spectrometry. Emerging technologies for proteome analysis include spotted-array-based methods and microfluidic devices. Taken together, these technologies provide a wealth of information that is useful in discovery-based science. However, there are some key limitations of these approaches and new technology is required to be able to fully integrate proteomic information with information obtained about DNA sequence, mRNA profiles and metabolite concentrations into effective models of biological systems.

Electrophoresis, Gel, Two-Dimensional↗
PubMed1997

Proteome analysis: genomics via the output rather than the input code.

A knowledge of the 'proteome,' total protein output encoded by a genome, provides information on (1) if and when predicted gene products are translated, (2) the relative concentrations of gene products, and (3) the extent of posttranslational modification, none of which can be accurately predicted from the nucleic acid sequence alone. The current status of proteome analysis is reviewed with respect to some of the techniques employed, automation, relevance to genomic studies, mass spectrometry and bioinformatics, limitations, and recent improvements in resolution and sensitivity for the detection of protein expression in whole cells, tissues, or organisms. The concept of 'proteomic contigs' is introduced for the first time. Traditional approaches to genomic analysis call upon a number of strategies to produce contiguous DNA sequence information, while 'proteomic contigs' are derived from multiple molecular mass and isoelectric point windows in order to construct a picture of the total protein expression within living cells. In higher eukaryotes, the latter may require several dozen image subsets of protein spots to be stitched together using advanced image analysis. The utility of both experimental and theoretical peptide-mass fingerprinting (PMF) and associated bioinformatics is outlined. A previously unknown motif within the peptide sequence of Elongation Factor Tu from Thermus aquaticus was discovered using PMF. This motif was shown to possess potential significance in maintaining structural integrity of the entire molecule.

DNA↗
PubMed2003

The proteome of the chloroplast lumen of higher plants.

Recent research in proteomics of the higher plant chloroplast has achieved considerable progress and added to our knowledge of lumenal chloroplast proteins. This work shows that chloroplast lumen has its own specific proteome and may comprise as many as 80 proteins. Although the new map of the lumenal proteome provides a great deal of information, it also raises numerous questions because the physiological functions of most of the novel lumenal proteins are unknown. In this Minireview, we summarize the latest discoveries regarding lumenal proteins and present the currently known facts about the lumenal chloroplast proteome of higher plants.

Journal Article↗
PubMed2026

Proteomic analysis of pancreatic endocrine cells by mechanistic single-cell isolation identifies membrane pathways.

To better understand diabetes and normoglycemia, pancreatic islet biology requires a precise molecular understanding of islet cell types at both the transcriptomic and proteomic levels. While transcriptomic analyses are well established, comprehensive proteomic characterization has been lacking, limiting our knowledge of islet molecular complexity. Here we introduce a nonenzymatic, mechanistic single-cell isolation technology using laser microdissection (LMD7), facilitating proteomic and transcriptomic analysis of physically isolated α-, β- and δ-cells from fresh-frozen, unfixed pancreatic tissue. This mechanistic approach avoids enzymatic digestion and chemical fixation, preserving the cells' native molecular state before processing. Given the limited existing proteomic data, we supplemented our findings with transcriptomic analysis generated using the same method and compared our results with data from enzymatically isolated cells, obtained by fluorescence-activated cell sorting and compiled by others. Our analysis revealed that enzymatic digestion alters gene expression patterns, particularly those of membrane-associated proteins, underscoring the impact of isolation techniques on biological outcomes. We identified cell-type-specific proteins typically underrepresented in pancreatic single-cell transcriptomic datasets. β-cells exhibited enrichment in vesicle trafficking proteins, α-cells displayed distinct calcium-dependent action potential machinery and δ-cells showed elevated expression of focal adhesion-related proteins. In addition, we report an inverse molecular relationship between β- and δ-cells, potentially driven by transcriptional regulators such as Mlxipl. By establishing robust molecular profiles directly from intact pancreatic tissue, this work provides a reference point for future pathological comparisons, offering a framework to investigate how diabetes and other endocrine disorders reshape islet cell biology.

Journal Article↗
PubMed2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Journal Article↗
PubMed2024

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Journal Article↗
PubMed2002

Subcellular localization of the yeast proteome.

Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.

Algorithms↗
PubMed2003

Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone.

The postgenomic era, as manifest, inter alia, by proteomics, offers unparalleled opportunities for the efficient discovery of safe, efficacious, and novel subunit vaccines targeting a tranche of modern major diseases. A negative corollary of this opportunity is the risk of becoming overwhelmed by this embarrassment of riches. Informatics techniques, working to address issues of both data management and through prediction to shortcut the experimental process, can be of enormous benefit in leveraging the proteomic revolution. In this disquisition, we evaluate proteomic approaches to the discovery of subunit vaccines, focussing on viral, bacterial, fungal, and parasite systems. We also adumbrate the impact that proteomic analysis of host-pathogen interactions can have. Finally, we review relevant methods to the prediction of immunome, with special emphasis on quantitative methods, and the subcellular localization of proteins within bacteria.

Journal Article↗
PubMed2003

The proteome of neural stem cells from adult rat hippocampus.

BACKGROUND: Hippocampal neural stem cells (HNSC) play an important role in cerebral plasticity in the adult brain and may contribute to tissue repair in neurological disease. To describe their biological potential with regard to plasticity, proliferation, or differentiation, it is important to know the cellular composition of their proteins, subsumed by the term proteome. RESULTS: Here, we present for the first time a proteomic database for HNSC isolated from the brains of adult rats and cultured for 10 weeks. Cytosolic proteins were extracted and subjected to two-dimensional gel electrophoresis followed by protein identification through mass spectrometry, database search, and gel matching. We could map about 1141 PlusMinus; 209 (N = 5) protein spots for each gel, of which 266 could be identified. We could group the identified proteins into several functional categories including metabolism, protein folding, energy metabolism and cellular respiration, as well as cytoskeleton, Ca2+ signaling pathways, cell cycle regulation, proteasome and protein degradation. We also found proteins belonging to detoxification, neurotransmitter metabolism, intracellular signaling pathways, and regulation of DNA transcription and RNA processing. CONCLUSIONS: The HNSC proteome database is a useful inventory which will allow to specify changes in the cellular protein expression pattern due to specific activated or suppressed pathways during differentiation or proliferation of neural stem cells. Several proteins could be identified in the HNSC proteome which are related to differentiation and plasticity, indicating activated functional pathways. Moreover, we found a protein for which no expression has been described in brain cells before.

Journal Article↗
PubMed2006

Comparative proteomic analysis of high cell density cultivations with two recombinant Bacillus megaterium strains for the production of a heterologous dextransucrase.

High cell density cultivations were performed under identical conditions for two Bacillus megaterium strains (MS941 and WH320), both carrying a heterologous dextransucrase (dsrS) gene under the control of the xylA promoter. At characteristic points of the cultivations (end of batch, initial feeding, before and after induction) the proteome was analyzed based on two dimensional gel electrophoresis and mass spectrometric protein identification using the protein database "bmegMEC.v2" recently made available. High expression but no secretion of DsrS was found for the chemical mutant WH320 whereas for MS 941, a defined protease deficient mutant of the same parent strain (DSM319), not even expression of DsrS could be detected. The proteomic analysis resulted in the identification of proteins involved in different cellular pathways such as in central carbon and overflow metabolism, in protein synthesis, protein secretion and degradation, in cell wall metabolism, in cell division and sporulation, in membrane transport and in stress responses. The two strains exhibited considerable variations in expression levels of specific proteins during the different phases of the cultivation process, whereas induction of DsrS production had, in general, little effect. The largely differing behaviour of the two strains with regard to DsrS expression can be attributed, at least in part, to changes observed in the proteome which predominantly concern biosynthetic enzymes and proteins belonging to the membrane translocation system, which were strongly down-regulated at high cell densities in MS941 compared with WH320. At the same time a cell envelope-associated quality control protease and two peptidoglycan-binding proteins related to cell wall turnover were strongly expressed in MS941 but not found in WH320. However, to further explain the very different physiological responses of the two strains to the same cultivation conditions, it is necessary to identify the mutated genes in WH320 in addition to the known lacZ. In view of the results of this proteomic study it seems that at high cell density conditions and hence low growth rates MS941, in contrast to WH320, does not maintain a vegetative growth which is essential for the expression of the foreign dsrS gene by using the xylA promoter. It is conceivable that applications of a promoter which is highly active under nutrient-limited cultivation conditions is necessary, at least for MS941, for the overexpression of recombinant genes in such B. megaterium fed-batch cultivation process. However to obtain a heterologous protein in secreted and properly folded form stills remains a big challenge.

Journal Article↗
PubMed2004

Epitope definition by proteomic similarity analysis: identification of the linear determinant of the anti-Dsg3 MAb 5H10.

BACKGROUND: Walking along disease-associated protein sequences in the search for specific segments able to induce cellular immune response may direct clinical research towards effective peptide-based vaccines. To this aim, we are studying the targets of the immune response in autoimmune diseases by applying the principle of non-self-discrimination as a driving concept in the identification of the autoimmunogenic peptide sequences. METHODS: Computer-assisted proteomic analysis of the autoantigen protein sequence and dot-blot/NMR immunoassays are applied to the prediction and subsequent validation of the epitopic sequences. RESULTS: Using the experimental model Pemphigus vulgaris/desmoglein 3, we have identified the antigenic linear determinant recognized by MAb 5H10, a monoclonal antibody raised against the extracellular domain of human desmoglein-3. The computer-assisted search for the Dsg3 epitope was conducted by analyzing the similarity level to the mouse proteome of the human desmoglein protein sequence. Dot-blot immunoassay analyses mapped the epitope within the sequence Dsg349-60 REWVKFAKPCRE, which shows low similarity to the mouse proteome. NMR spectroscopy analyses confirmed the specificity of MAb 5H10 for the predicted epitope. CONCLUSIONS: This report promotes the concept that low level of sequence similarity to the host's proteome may modulate peptide epitopicity.

Journal Article↗
PubMed2006

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.

Journal Article↗
PubMed2006

Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls.

BACKGROUND: The ultimate goal of proteomic analysis of a cell compartment should be the exhaustive identification of resident proteins; excluding proteins from other cell compartments. Reaching such a goal closely depends on the reliability of the isolation procedure for the cell compartment of interest. Plant cell walls possess specific difficulties: (i) the lack of a surrounding membrane may result in the loss of cell wall proteins (CWP) during the isolation procedure, (ii) polysaccharide networks of cellulose, hemicelluloses and pectins form potential traps for contaminants such as intracellular proteins. Several reported procedures to isolate cell walls for proteomic analyses led to the isolation of a high proportion (more than 50%) of predicted intracellular proteins. Since isolated cell walls should hold secreted proteins, one can imagine alternative procedures to prepare cell walls containing a lower proportion of contaminant proteins. RESULTS: The rationales of several published procedures to isolate cell walls for proteomics were analyzed, with regard to the bioinformatic-predicted subcellular localization of the identified proteins. Critical steps were revealed: (i) homogenization in low ionic strength acid buffer to retain CWP, (ii) purification through increasing density cushions, (iii) extensive washes with a low ionic strength acid buffer to retain CWP while removing as many cytosolic proteins as possible, and (iv) absence of detergents. A new procedure was developed to prepare cell walls from etiolated hypocotyls of Arabidopsis thaliana. After salt extraction, a high proportion of proteins predicted to be secreted was released (73%), belonging to the same functional classes as proteins identified using previously described protocols. Finally, removal of intracellular proteins was obtained using detergents, but their amount represented less than 3% in mass of the total protein extract, based on protein quantification. CONCLUSION: The new cell wall preparation described in this paper gives the lowest proportion of proteins predicted to be intracellular when compared to available protocols. The application of its principles should lead to a more realistic view of the cell wall proteome, at least for the weakly bound CWP extractable by salts. In addition, it offers a clean cell wall preparation for subsequent extraction of strongly bound CWP.

Journal Article↗
PubMed2005

The future of post-genomic biology at the proteomic level: an outlook.

Drug discovery and early-stage drugs and biomarkers development is a continuous adaptation and maturation process. The cycle of changes based on new findings is coupled with shifts in research priorities and make this part of pharmaceutical research a challenging endeavour. Over the last years, the emphasis on genomics has shifted to proteomics, the science of understanding how proteins translate gene information into function, and metabonomics, the science of small metabolites that are further apart from genomic projects. Proteomics describes the analysis of the protein complement of a biological sample with respect to temporal and spatial resolution. This technology is based on separation of complex protein mixtures by 2D gel-electrophoresis, in gel digest and mass spectrometric analysis of the protein fragments. Proteomics has been recently flanked by peptidomics, a new research direction aimed at the comprehensive analysis of small (1-20 kDa) polypeptides, thus covering the gap between proteomics and metabonomics. The refinement of peptidomics is based on an essential paradigm related to modularity and diversity. Peptides are a paramount example of how one single gene can release multiple functionalities. We can expect fast progress in understanding protein and peptide networks from a systems biology approach ending in the discovery of new peptide targets. However, the way from a complex sample to potential diagnostic and therapeutic targets will depend on technological developments and from the ability to discriminate true disease-related signals from false positive and negative signals, and the way from target discovery to target validation will not be short.

Aged↗
PubMed2001

[Proteomic analysis: why and how ?].

The proteome, first formalized in 1995, designs all the proteins expressed by the genome of a cell, tissu, or organ at a defined time. Proteomic analysis leads to a description of the regulation of gene expression by the study of proteins and of their post-translational modifications. Proteomic analysis is based on three technologies: 1) Two-dimensional electrophoresis allowing the separation of thousands of proteins from a single mixture; 2) mass spectrometry allowing the characterization of picoquantities of polypeptides and providing data on post-translational modifications; 3) Bioinformatic which is required for the quantification of protein level and for the constitution of databases of protein expression profiles. Complementing the methods of the genomics, the use of proteomic analysis is widely spreading in the fields of fundamental biology, biomedicine and pharmacology for the identification of new biological markers and therapeutic targets.

Animals↗
PubMed1999

Applied proteomics--analyzing protein structure and function for drug discovery. 27-28 January 1999, London, UK.

This two-day meeting, attended by approximately 60 delegates, was aimed at researchers in the proteomics field. The proteome is defined as the entire protein content of the cell. Proteomics is the analysis of the end-product of gene expression and all regulations governing gene expression are taken into account, ie, transcriptional and translational. The three stages involved in proteomic analysis are: a separation phase, ie, two dimensional (2D) gel electrophoresis; a characterization phase, ie, mass spectrometry; and, a data analysis phase, ie, bioinformatics.

Journal Article↗