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At least 739 records · Page 41Linked to original sources
PubMed2005

Proteomic analysis of protein expression profiles during Caenorhabditis elegans development using two-dimensional difference gel electrophoresis.

Coordinated protein expression is critical for the normal execution of animal development. To obtain overall proteome profiles during animal development, a small free-living soil nematode, Caenorhabditis elegans, was used as a model and the developmental changes of protein expressions were analyzed using two-dimensional difference gel electrophoresis. Protein samples from six developmental stages were prelabeled with fluorescent cyanine dyes and separated on two-dimensional electrophoresis gels. Image-to-image analysis of protein abundances together with protein identification by peptide mass fingerprinting yielded the developmental expression profiles of 231 spots representing 165 proteins. About a quarter of the identified proteins were expressed in multiple spots with different isoelectric points, suggesting a certain proportion of proteins were variously modified. This notion was supported by the observation that about a third of the multispot proteins were stained positive for a phosphoprotein specific dye. While a fairly large number of the proteins showed little alteration in their expression profiles during development, about 40 proteins were found to be significantly either up- or down-regulated between the embryos and newly hatched L1 larvae. Down-regulated proteins included those related to the cell cycle such as MCM-7, PCN-1, and the mitotic checkpoint protein, while up-regulated proteins included structural proteins such as actins, LEV-11, DIM-1, VAB-21, metabolic enzymes such as ATP synthase, ALH-12, fluctose-1,6-bisphosphate aldolase and GPD-3, and galectins. A standard proteome map was obtained where the defects in the mutations of developmental genes and the effects of reagents on the development in C. elegans were analyzed.

Amino Acid Sequence↗
PubMed2004

Microarray platform for profiling enzyme activities in complex proteomes.

Activity-based protein profiling (ABPP) is a chemical method that utilizes active-site-directed probes to determine the functional state of enzymes in complex proteomes. Probe-labeled enzymes are typically detected by in-gel fluorescence scanning, a robust technique that nonetheless exhibits some key deficiencies, including limited sensitivity and resolution, as well as ambiguity regarding the molecular identity of the enzymes under investigation. Here, we report a microarray platform for ABPP that addresses these limitations. In this platform, proteomes are treated with ABPP probes in solution, after which labeled enzymes are captured and visualized on glass slides displaying an array of anti-enzyme antibodies. We show that ABPP microarrays exhibit superior sensitivity and resolution compared to gel-based methods, permitting the parallel analysis of several enzyme activities in proteomes, including cancer-associated proteases such as urokinase, matrix metalloproteinase-9, and prostate-specific antigen.

Antibodies↗
PubMed2025

Admixture-mapping analysis reveals genetic determinants of the human plasma proteome.

Protein profiling and genetic findings can be integrated to define the genetic architecture of the circulating proteome in chronic diseases. Most self-identified African American (AA) individuals have both African and European genetic ancestry. Admixture mapping can detect genomic association regions in which causal variants exist with substantial differences in allele frequency or effect sizes between genetic ancestries. We performed admixture mapping of the circulating proteome in 1,989 participants from the Jackson Heart Study (JHS), investigating the relation of local African ancestry within genomic regions with levels of circulating proteins. We conditioned protein-local ancestry association models on variants previously found to be associated with those proteins in genome-wide association studies (GWASs). We replicated findings in 196 AA participants from the Multi-Ethnic Study of Atherosclerosis (MESA). 62 proteins were associated with local African ancestry. 21 of 62 remained statistically significant after conditioning on protein-associated variants observed in previous GWASs. 48 of 54 available protein-local ancestry associations were replicated in the MESA. Proteins associated with local African ancestry included chemokines, factors associated with vascular biology and inflammation, and other biologically interesting proteins. Admixture associations unexplained by previously reported protein-associated variants in conditional analysis suggest the existence of causal variants missed by standard GWAS techniques.

Aged↗
PubMed2005

Antibody-based proteomics for human tissue profiling.

Here, we describe the use of antibody-based proteomics involving the generation of protein-specific antibodies to functionally explore the human proteome. The antibodies can be used for analysis of corresponding proteins in a wide range of assay platforms, including i) immunohistochemistry for detailed tissue profiling, ii) specific affinity reagents for various functional protein assays, and iii) capture ("pull-down") reagents for purification of specific proteins and their associated complexes for structural and biochemical analyses. In this review, the use of antibodies for such analysis will be discussed with focus on the possibility to create a descriptive and comprehensive protein atlas for tissue distribution and subcellular localization of human proteins in both normal and disease tissues.

Antibodies↗
PubMed2005

Combined proteome and metabolite-profiling analyses reveal surprising insights into yeast sulfur metabolism.

Metabolomics is considered as an emerging new tool for functional proteomics in the identification of new protein function or in projects aiming at modeling whole cell metabolism. When combined with proteome studies, metabolite-profiling analyses revealed unanticipated insights into the yeast sulfur pathway. In response to cadmium, the observed overproduction of glutathione, essential for the detoxification of the metal, can be entirely accounted for by a marked drop in sulfur-containing protein synthesis and a redirection of sulfur metabolite fluxes to the glutathione pathway. A kinetic analysis showed sequential and dramatic changes in intermediate sulfur metabolite pools within the first hours of the treatment. Strikingly, whereas proteome and metabolic data were positively correlated under cadmium conditions, proteome and metabolic data were negatively correlated during other growth conditions, i.e. methionine supplementation or sulfate starvation. These differences can be explained by alternative mechanisms in the regulation of Met4, the activator of the sulfur pathway. Whereas Met4 activity is controlled by the cellular cysteine content in response to sulfur source and availability, the present study suggests that Met4 activation under cadmium conditions is cysteine-independent. The results clearly indicate that the metabolic state of a cell cannot be safely predicted based solely on proteomic and/or gene expression data. Combined metabolome and proteome studies are necessary to draw a comprehensive and integrated view of cell metabolism.

Cadmium↗
PubMed2006

Overcoming the dynamic range problem in mass spectrometry-based shotgun proteomics.

Protein profiling using mass spectrometry technology has emerged as a powerful method for analyzing large-scale protein-expression patterns in cells and tissues. However, a number of challenges are present in proteomics research, one of the greatest being the high degree of protein complexity and huge dynamic range of proteins expressed in the complex biological mixtures, which exceeds six orders of magnitude in cells and ten orders of magnitude in body fluids. Since many important signaling proteins have low expression levels, methods to detect the low-abundance proteins in a complex sample are required. This review will focus on the fundamental fractionation and mass spectrometry techniques currently used for large-scale shotgun proteomics research.

Animals↗
PubMed2003

Affinity proteomics for systematic protein profiling of chromosome 21 gene products in human tissues.

Here we show that an affinity proteomics strategy using affinity-purified antibodies raised against recombinant human protein fragments can be used for chromosome-wide protein profiling. The approach is based on affinity reagents raised toward bioinformatics-designed protein epitope signature tags corresponding to unique regions of individual gene loci. The genes of human chromosome 21 identified by the genome efforts were investigated, and the success rates for de novo cloning, protein production, and antibody generation were 85, 76, and 56%, respectively. Using human tissue arrays, a systematic profiling of protein expression and subcellular localization was undertaken for the putative gene products. The results suggest that this affinity proteomics strategy can be used to produce a proteome atlas, describing distribution and expression of proteins in normal tissues as well as in common cancers and other forms of diseased tissues.

Antibodies↗
PubMed2002

Profiling the alkaline membrane proteome of Caulobacter crescentus with two-dimensional electrophoresis and mass spectrometry.

Attempts at protein profiling in the alkaline pH region using isoelectric focusing have often proved difficult, greatly limiting the scope of proteome analysis. We investigated several parameters using custom pH 8-11 immobilized pH gradients to separate a Caulobacter crescentus membrane preparation. These included sample application, quenching endoosomotic flow and gel matrix composition. Among these factors, the sample application position was the predominant parameter to affect two-dimensional gel quality. Separated proteins were silver stained and profiled using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. The use of a prototype MALDI-Q-Tof mass spectrometer assisted identification of several proteins by providing highly informative peptide fragmentation data from the sample digests. Thirty-two unique alkaline proteins were identified in this study, which complements our previously described C. crescentus membrane proteome. Our experiments point towards new options for proteomic researchers aiming to both extend the scope of analysis, and simplify methods of identifying proteins with high confidence.

2-Propanol↗
PubMed2005

Integrating gene and protein expression data: pattern analysis and profile mining.

Proteomics and functional genomics are emerging new research fields devoted to the study of the entire collection of proteins and mRNA transcripts (collectively known as gene products) that define a biological system. DNA microarrays are now a popular platform for measuring changes in messenger RNA transcript levels on a genome-wide scale, while gel-free shotgun profiling methods based on tandem mass spectrometry are increasingly being used to determine the identity, modification states, and relative abundance of large numbers of proteins. By defining the behavior of entire biological pathways and networks under various physiological states, these studies aim to extend traditional reductionist molecular genetic approaches regarding the biological roles of the vast array of uncharacterized gene products. A key goal is to determine how the information encoded by the myriad of expressed gene products is integrated at the molecular, cellular, and even whole organism level to create the dynamic biochemical processes and complex physiological controls that sustain life. While comparison of the complementary information contained in proteomic and mRNA data sets poses considerable analytical challenges, these efforts should provide added insight into the fundamental mechanisms underlying physiology, development, and the emergence of disease. Here, we outline several analytical approaches, methods, and tools that have proven to be helpful in the face of this important challenge.

Algorithms↗
PubMed2006

A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies.

Proteomic research includes the characterization of protein mixtures in order to understand complex biological systems and determine relationships between proteins, their function, and protein-protein interactions. Often the goal of such research is to monitor changes of proteins in perturbed systems, a type of study referred to as differential expression analysis. To perform these studies requires the ability to execute some type of differential comparison of a given protein state in reference to some type of a control. The iTRAQ reagents are a set of isobaric reagents which are amine specific and allow for the identification and quantitation of up to four different samples simultaneously. The amine specificity of these reagents makes most peptides in a sample amenable to this labeling strategy with no loss of information from samples involving post-translational modifications, such as the scrutiny of signal transduction pathways that often involve phosphorylation phenomena. In addition, the multiplexing capacity of these reagents allows for information replication within certain LC-MS/MS experimental regimes, providing additional statistical validation within any given experiment. The results presented herein demonstrate a few examples of the wide variety of quantitative information that can be realized when undertaking such experimental approaches. These include temporal analysis of drug-induced-protein expression, discovery and elucidation of disease markers, and protein-protein interactions in multi-protein complexes.

Animals↗
PubMedDate not supplied

Cancer proteomics: from biomarker discovery to signal pathway profiling.

In the post-genomic era of science, the field of proteomics promises the discovery of new molecular targets for therapy, biomarkers for early detection, and new endpoints for therapeutic efficacy and toxicity. Patient-specific targeted therapeutics with reduced toxicity and increased efficacy, the ultimate goal for rational drug development, can only be achieved if direct analyses of the tissue cells in the actual human malignancy are analyzed. To that end, technologies such as Laser Capture Microdissection (LCM), is providing unparalleled access to the purified diseased human cells directly from tissue specimens. However, limited availability of patient material is a challenge towards the development of new highly sensitive proteomic methodologies. Two-dimensional gel electrophoresis (2D-PAGE), still the mainstay of most proteomic analysis of disease, is being complemented, and in some instances replaced by new exciting approaches to multiparametric protein characterization. The use of rapid, high throughput mass spectrometric-based fingerprints of peptides and proteins may prove to be valuable for new molecular classification of human tumors and disease stages. Coupled with LCM, high-density protein arrays, antibody arrays, and small molecular arrays, could have a substantial impact on proteomic profiling of human malignancies. Finally, detailed real-time knowledge about the states of intracellular signaling circuitry and pathways in the normal and malignant cells before, during and after therapy will yield invaluable information about mechanism of action and efficacy of existing and novel therapeutics for the treatment of human cancer.

Biomarkers↗
PubMed2005

The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile.

Several studies have been published in which holistic approaches were used to characterise the proteome and transcriptome of human platelets. The key intent being that a deeper understanding of the normal and aberrant physiological functions of platelets can only be achieved if most biomolecular building blocks are mapped. Here we present the application of recently developed novel technologies that overcome some of the shortcomings of gel-based proteomics. Central in our approach is the so-called combined fractional diagonal chromatography (COFRADIC)-technology in which sets of representative peptides are sorted in a diagonal RP chromatographic system through a specific modification of their side chain. In this study we combined three different COFRADIC sorting techniques to analyse the proteome of human platelets. Methionyl, cysteinyl and amino terminal peptides were isolated and analysed by MS/MS. Merging the peptide identifications obtained after database searching resulted in a core set of 641 platelet proteins, which comprises the largest set identified today. In comparison to previously published platelet proteomes, we identified 404 novel platelet proteins containing a high number of hydrophobic membrane proteins and hypothetical proteins. Furthermore we discuss the observed characteristics and potential benefits of each of the different COFRADIC technologies for proteome analysis and highlight important issues that need to be considered when searching sequence databases using data obtained in peptide-centric, non-gel proteomics studies.

Animals↗
PubMed2002

Enhanced analysis of human breast cancer proteomes using micro-scale solution isoelectrofocusing combined with high resolution 1-D and 2-D gels.

Current methods for quantitatively comparing proteomes (protein profiling) have inadequate resolution and dynamic range for complex proteomes such as those from mammalian cells or tissues. More extensive profiling of complex proteomes would be obtained if the proteomes could be reproducibly divided into a moderate number of well-separated pools. But the utility of any prefractionation is dependent upon the resolution obtained because extensive cross contamination of many proteins among different pools would make quantitative comparisons impractical. The current study used a recently developed microscale solution isoelectrofocusing (musol-IEF) method to separate human breast cancer cell extracts into seven well-resolved pools. High resolution fractionation could be achieved in a series of small volume tandem chambers separated by thin acrylamide partitions containing covalently bound immobilines that establish discrete pH zones to separate proteins based upon their pIs. In contrast to analytical 2-D gels, this prefractionation method was capable of separating very large proteins (up to about 500 kDa) that could be subsequently profiled and quantitated using large-pore 1-D SDS gels. The pH 4.5-6.5 region was divided into four 0.5 pH unit ranges because this region had the greatest number of proteins. By using very narrow pH range fractions, sample amounts applied to narrow pH range 2-D gels could be increased to detect lower abundance proteins. Although 1.0 pH range 2-D gels were used in these experiments, further protein resolution should be feasible by using 2-D gels with pH ranges that are only slightly wider than the pH ranges of the musol-IEF fractions. By combining musol-IEF prefractionation with subsequent large pore 1-D SDS-PAGE (>100 kDa) and narrow range 2-D gels (<100 kDa), large proteins can be reliably quantitated, many more proteins can be resolved, and lower abundance proteins can be detected.

Electrophoresis, Polyacrylamide Gel↗
PubMed2025

Extracellular Vesicles Profiling in Acute Myeloid Leukemia Cell Lines: A Proteomic Characterization.

Extracellular vesicles (EVs) express features of parental cells and are fundamental in modulating the crosstalk between cancer cells and their environment. Increasing evidence suggests that EVs have a pivotal role in tumorigenesis, cancer development, and drug resistance. EVs are also involved in controlling the communication between hematopoietic stem cells and the surrounding microenvironment in the bone marrow (BM), during several processes such as self-renewal, mobilization, and lineage differentiation. Proteins expressed in cancer cell-derived EVs can be useful to further understand the regulation of hematopoietic stem cell fate, a fundamental mechanism in acute myeloid leukemia (AML). Furthermore, EVs are implicated in transmitting drug-resistance mechanisms in solid and not-solid cancer types. Here, using a proteomic approach, we analyze and validate the protein profile of EVs from three AML cell lines with different genotypes, namely OCI-AML-2, OCI-AML-3, and HL-60. The majority of the identified proteins were significantly enriched in the Gene Ontology category 'Extracellular Exosome'. Network model analysis of EV proteins revealed several significantly modulated pathways, including inflammation activation and metastatic processes in AML cell-derived EVs. The EVs proteomic profiling allows us to identify the EVs-associated molecules and pathways that could impact cancer progression and drug resistance.

Humans↗
PubMed2004

Identification of hypoxia-regulated proteins in head and neck cancer by proteomic and tissue array profiling.

Hypoxia within solid tumors decreases therapeutic efficacy, and identification of hypoxia markers may influence the choice of therapeutic modality. Here, we used a proteomic approach to identify hypoxia-regulated proteins and validated their use as endogenous indicators of tumor hypoxia. Using two-dimensional gel electrophoresis and PowerBlot (antibody-based array), we identified a group of 20 proteins that are increased >/=1.5-fold during hypoxia. The majority of these proteins such as IkappaB kinase beta (IKKbeta), MKK3b, highly expressed in cancer (HEC), density-regulated protein 1, P150(glued), nuclear transport factor 2, binder of ARL 2, Paxillin, and transcription termination factor I have not been previously reported to be hypoxia inducible. The increase in these proteins under hypoxia was mediated through posttranscriptional mechanisms. We additionally characterized the role of IKKbeta, a regulator of the nuclear factor-kappaB transcription factor, during hypoxia. We demonstrated that IKKbeta mediates cell survival during hypoxia and is induced in a variety of squamous cell carcinoma cell lines. Furthermore, we showed that IKKbeta expression from tumor specimens correlated with tumor oxygenation in patients with head and neck squamous cell carcinomas. These data suggest that IKKbeta is a novel endogenous marker of tumor hypoxia and may represent a new target for anticancer therapy.

Carcinoma, Squamous Cell↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2003

Proteomic comparison of two-dimensional gel electrophoresis profiles from human lung squamous carcinoma and normal bronchial epithelial tissues.

Differential proteome profiles of human lung squamous carcinoma tissue compared to paired tumor-adjacent normal bronchial epithelial tissue were established and analyzed by means of immobilized pH gradient-based two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS). The results showed that well-resolved, reproducible 2-DE patterns of human lung squamous carcinoma and adjacent normal bronchial epithelial tissues were obtained under the condition of 0.75-mg protein-load. The average deviation of spot position was 0.733+/-0.101 mm in IEF direction, and 0.925+/-0.207 mm in SDS-PAGE direction. For tumor tissue, a total of 1241+/-88 spots were detected, 987+/-65 spots were matched with an average matching rate of 79.5%. For control, a total of 1190+/-72 spots were detected, and 875+/-48 spots were matched with an average matching rate of 73.5%. A total of 864+/-34 spots were matched between tumors and controls. Forty-three differential proteins were characterized: some proteins were related to oncogenes, and others involved in the regulation of cell cycle and signal transduction. It is suggested that the differential proteomic approach is valuable for mass identification of differentially expressed proteins involved in lung carcinogenesis. These data will be used to establish human lung cancer proteome database to further study human lung squamous carcinoma.

Amino Acid Sequence↗
PubMed2006

MALDI-TOF serum protein profiling for the detection of breast cancer.

BACKGROUND: Proteomic expression profiling has been suggested as a potential tool for the early diagnosis of cancer and other diseases. The objective of our study was to assess the feasibility of this approach for the detection of breast cancer. MATERIALS AND METHODS: In a randomized block design pre-operative serum samples obtained from 78 breast cancer patients and 29 controls were used to generate high-resolution MALDI-TOF protein profiles. The spectra generated using C8 magnetic beads assisted mass spectrometry were smoothed, binned and normalized after baseline correction. Linear discriminant analysis with double cross-validation, based on principal component analysis, was used to classify the protein profiles. RESULTS: A total recognition rate of 99%, a sensitivity of 100%, and a specificity of 97.0% for the detection of breast cancer were shown. The area under the curve of the classifier was 98.3%, which demonstrates the separation power of the classifier. The first 2 principal components account for most of the between- group separation. CONCLUSIONS: Double cross-validation showed that classification could be attributed to actual information in the protein profiles rather than to chance. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of breast cancer.

Adult↗