Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “quantitative proteomics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29Linked to original sources
PubMed2003

Gaining confidence in high-throughput protein interaction networks.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.

Algorithms↗
PubMed2006

Mass spectrometry-based proteomics and its application to studies of Porphyromonas gingivalis invasion and pathogenicity.

Porphyromonas gingivalis is a Gram-negative anaerobe that populates the subgingival crevice of the mouth. It is known to undergo a transition from its commensal status in healthy individuals to a highly invasive intracellular pathogen in human patients suffering from periodontal disease, where it is often the dominant species of pathogenic bacteria. The application of mass spectrometry-based proteomics to the study of P. gingivalis interactions with model host cell systems, invasion and pathogenicity is reviewed. These studies have evolved from qualitative identifications of small numbers of secreted proteins, using traditional gel-based methods, to quantitative whole cell proteomic studies using multiple dimension capillary HPLC coupled with linear ion trap mass spectrometry. It has become possible to generate a differential readout of protein expression change over the entire P. gingivalis proteome, in a manner analogous to whole genome mRNA arrays. Different strategies have been employed for generating protein level expression ratios from mass spectrometry data, including stable isotope metabolic labeling and most recently, spectral counting methods. A global view of changes in protein modification status remains elusive due to the limitations of existing computational tools for database searching and data mining. Such a view would be desirable for purposes of making global assessments of changes in gene regulation in response to host interactions during the course of adhesion, invasion and internalization. With a complete data matrix consisting of changes in transcription, protein abundance and protein modification during the course of invasion, the search for new protein drug targets would benefit from a more comprehensive understanding of these processes than what could be achieved prior to the advent of systems biology.

Bacterial Proteins↗
PubMed2006

2-D DIGE as a quantitative tool for investigating the HUPO Brain Proteome Project mouse series.

Brain development and aging is a complex process involving proliferation, differentiation and apoptosis. Elucidating proteome changes in these processes can help to understand the mechanisms of brain development and maintenance as well as neurodegenerative diseases. The research reported here is a contribution to the HUPO Brain Proteome Project mouse pilot study. Whole, frozen C57BL/6J mouse brain comprising three different developmental stages (embryonic day 16, postnatal day 7, and postnatal days 54-58) were processed by using 2-D DIGE. A total of 1999 spots were matched between all gels. Of these, 206 spots were differentially expressed between the different stages: 122 spots were highest in intensity in embryonic stage E16, 26 highest in the juvenile group P7 and 58 spots highest in P56, the adult stage. The results show a pattern of temporal expression. Based on the expression patterns we tentatively suggest that proteins involved in the establishment of primary structures in the brain are expressed highest in the embryonic mouse. Proteins involved in the development of the brain are expressed highest in the juvenile phase and proteins that make utilization of the brain possible by delivering energy are expressed highest in the adult mice.

Aging↗
PubMed2005

Development of a sample preparation method for fungal proteomics.

Since filamentous fungi including basidiomycetous fungi possess an exceptionally robust cell wall as in microorganisms, effective extraction of intracellular proteins is a key step for fungal proteomic studies. To overcome the experimental obstacle caused by cell walls, we utilized fungal protoplasts, prepared from the brown-rot basidiomycete, Tyromyces palustris. The amount and quality of proteins extracted from the protoplast cells were much higher than that from the mycelial cells. Quantitative comparisons of proteome maps prepared from mycelial and protoplast cells indicated protein spots with a wider range of molecular weights and pIs in the protoplast sample. Furthermore, no streaking or tailing was observed in the protoplasts, suggesting that effective extraction of intracellular proteins from protoplasts might help suppress degradation of proteins during this process. In addition to the efficiency of protein extraction, simple and efficient subcellular fractionation was also achieved using protoplast cells.

Basidiomycota↗
PubMed2004

Identifying and quantifying in vivo methylation sites by heavy methyl SILAC.

Protein methylation is a stable post-translational modification (PTM) with important biological functions. It occurs predominantly on arginine and lysine residues with varying numbers of methyl groups, such as mono-, di- or trimethyl lysine. Existing methods for identifying methylation sites are laborious, require large amounts of sample and cannot be applied to complex mixtures. We have previously described stable isotope labeling by amino acids in cell culture (SILAC) for quantitative comparison of proteomes. In heavy methyl SILAC, cells metabolically convert [(13)CD(3)]methionine to the sole biological methyl donor, [(13)CD(3)]S-adenosyl methionine. Heavy methyl groups are fully incorporated into in vivo methylation sites, directly labeling the PTM. This provides markedly increased confidence in identification and relative quantitation of protein methylation by mass spectrometry. Using antibodies targeted to methylated residues and analysis by liquid chromatography-tandem mass spectrometry, we identified 59 methylation sites, including previously unknown sites, considerably extending the number of in vivo methylation sites described in the literature.

Algorithms↗
PubMedDate not supplied

Quantitation using enhanced signal tags: a technique for comparative proteomics.

Differential amidination of N-termini and lysine residues provides the basis for a novel approach to protein quantitation using MALDI mass spectrometry. Because the amidination of lysines increases their basicity and therefore MALDI ionization yields, the method is called quantitation using enhanced signal tags (QUEST). Amidine labels differ by methylene groups, leading to 14 Da mass differentials. The utility of QUEST is demonstrated while analyzing the digests of two model proteins using MALDI-TOF mass spectrometry.

Amidines↗
PubMed2005

A proteomic investigation into a human gastric cancer cell line BGC823 treated with diallyl trisulfide.

Garlic is generally used as a therapeutic reagent against various diseases worldwide. Although a great effort is made to understand the pharmaceutical mechanisms of garlic and its derivatives, there are many mysteries to be uncovered. Using proteomic means, herein we have systematically studied the responses of protein expression in BGC823 cells, a gastric cancer cell line, induced by diallyl trisulfide (DATS), a major component of garlic derivatives. A total of 41 unique proteins in BGC823 were detected with significant changes in their expression levels corresponding with DATS administration. Of these proteins, five typical ones, glutathione S-transferase-pi (GST-pi), voltage-dependent anion channel-1 (VDAC-1), Annexin I, Galectin and S100A11, were further examined by Western blotting, resulting in coincident data with the proteomic evidence. Moreover quantitative real-time RT-PCR experiments offered dynamic data of mRNA expression, indicating the responses of Annexin I and GST-pi expression within a short period after DATS treatment. Interestingly, approximately 50% of DATS-sensitive proteins (19/41) in BGC823 are tightly associated with apoptotic pathways. These proteomic results presented, therefore, provide additional support to the hypothesis that garlic is a strong inducer of apoptosis in tumor cells.

Allyl Compounds↗
PubMed2006

Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

The modest correlation between mRNA expression and protein abundance in large-scale data sets is explained in part by experimental challenges, such as technological limitations, and in part by fundamental biological factors in the transcription and translation processes. Among various factors affecting the mRNA-protein correlation, the roles of biological factors related to translation are poorly understood. In this study, using experimental mRNA expression and protein abundance data collected from Desulfovibrio vulgaris by DNA microarray and liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) proteomic analysis, we quantitatively examined the effects of several translational-efficiency-related sequence features on mRNA-protein correlation. Three classes of sequence features were investigated according to different translational stages: (i) initiation, Shine-Dalgarno sequences, start codon identity, and start codon context; (ii) elongation, codon usage and amino acid usage; and (iii) termination, stop codon identity and stop codon context. Surprisingly, although it is widely accepted that translation initiation is the rate-limiting step for translation, our results showed that the mRNA-protein correlation was affected the most by the features at elongation stages, i.e., codon usage and amino acid composition (5.3-15.7% and 5.8-11.9% of the total variation of mRNA-protein correlation, respectively), followed by stop codon context and the Shine-Dalgarno sequence (3.7-5.1% and 1.9-3.8%, respectively). Taken together, all sequence features contributed to 15.2-26.2% of the total variation of mRNA-protein correlation. This study provides the first comprehensive quantitative analysis of the mRNA-protein correlation in bacterial D. vulgaris and adds new insights into the relative importance of various sequence features in prokaryotic protein translation.

Bacterial Proteins↗
PubMed2005

Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.

To estimate absolute protein contents in complex mixtures, we previously defined a protein abundance index (PAI) as the number of observed peptides divided by the number of observable peptides per protein (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome. Res. 12, 1231-1245). Here we report that PAI values obtained at different concentrations of serum albumin show a linear relationship with the logarithm of protein concentration in LC-MS/MS experiments. This was also the case for 46 proteins in a mouse whole cell lysate. For absolute quantitation, PAI was converted to exponentially modified PAI (emPAI), equal to 10PAI minus one, which is proportional to protein content in a protein mixture. For the 46 proteins in the whole lysate, the deviation percentages of the emPAI-based abundances from the actual values were within 63% on average, similar or better than determination of abundance by protein staining. emPAI was applied to comprehensive protein expression analysis and to a comparison study between gene and protein expression in a human cancer cell line, HCT116. The values of emPAI are easily calculated and add important quantitation information to proteomic experiments; therefore we suggest that they should be reported in large scale proteomic identification projects.

Animals↗
PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗
PubMed2026

Photocatalytic Golgi Proteomics Reveals Palmitoylation-Regulated Golgiphagy.

The Golgi apparatus (GA) orchestrates protein modification, trafficking, and secretion through highly dynamic remodeling, yet its proteomic complexity remains difficult to resolve in living systems. Here, we report CAT-Golgi, a genetically independent and light-controlled photocatalytic proximity labeling strategy for in situ spatiotemporal mapping of the Golgi-associated proteome. Combining a cysteine-conjugated eosin photocatalyst (GolgiCat) with an aniline probe, CAT-Golgi enables rapid and precise protein labeling within minutes under mild green light, requiring no genetic manipulation and operating efficiently in hard-to-transfect and primary cells. Leveraging our extensive efforts in organelle-targeted photocatalytic systems, we extended this chemistry to the highly dynamic and reversible Golgi apparatus. CAT-Golgi achieved quantitative and comparative proteomics in HeLa, K562, Jurkat and primary HEKa cells, revealing both conserved and cell-type-specific profiles. Under Brefeldin A-induced Golgiphagy, CAT-Golgi captured large-scale proteome remodeling and identified palmitoyl-protein thioesterase 1 (PPT1) as a potential regulatory component. PPT1 downregulation enhanced ULK1 and TRPML1 palmitoylation, disrupted redox balance, and activated Golgiphagy. CAT-Golgi provides a broadly applicable chemical platform for decoding organelle dynamics, offering both conceptual and technical foundations for extending photocatalytic proteomics to other transient organelles and illuminating molecular mechanisms of organelle plasticity and disease progression.

Golgi Apparatus↗
PubMed2026

Spectral-Proteomic Integration Analysis (SPIA) Deciphers Molecular Trajectories of Breast Cancer and Enables Multitarget Therapeutic Assessment.

Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)─a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC ≥ 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median ρ ∼ 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.

Animals↗
PubMed2005

Extraction and proteomic analysis of proteins from normal and multiple sclerosis postmortem brain.

In this study, a reproducible fractionation procedure was developed to reduce levels of the abundant cytoskeletal proteins that are present in normal and pathological central nervous system (CNS) tissues. The fractionation and proteomic analysis techniques employed greatly facilitated comparison of the spectrum of proteins in normal postmortem brain with proteins in samples from patients with multiple sclerosis, an inflammatory demyelinating disease in which complex changes in protein expression occur as lesions develop. This approach may be of value for the proteomic identification and quantitation of proteins which undergo disease-related changes in CNS disorders, and also for protein expression studies on normal adult and developing CNS tissues.

Aged↗
PubMed2004

Quantitative functional analysis of protein complexes on surfaces.

A major challenge in cell and molecular physiology research is to understand the mechanisms of biological processes in terms of the interactions, activities and regulation of the underlying proteins. Functional and mechanistic analyses of the large number of proteins that participate in the regulation of cellular processes will require new approaches and techniques for high throughput and multiplexed functional analyses of protein interactions, protein conformational dynamics and protein activity. In this review we focus on the development and application of proteomics and associated technologies for quantitative functional analysis of proteins and their complexes that include: (1) the application of surface plasmon resonance (SPR) imaging for multiplexed, label-free analyses of protein interactions, binding constants for biomolecular interactions and protein activities; and (2) high content analysis of protein motions within functional multiprotein complexes.

Animals↗
PubMedDate not supplied

Application of mass spectrometry in proteomics.

Mass spectrometry has arguably become the core technology in proteomics. The application of mass spectrometry based techniques for the qualitative and quantitative analysis of global proteome samples derived from complex mixtures has had a big impact in the understanding of cellular function. Here, we give a brief introduction to principles of mass spectrometry and instrumentation currently used in proteomics experiments. In addition, recent developments in the application of mass spectrometry in proteomics are summarised. Strategies allowing high-throughput identification of proteins from highly complex mixtures include accurate mass measurement of peptides derived from total proteome digests and multidimensional peptide separations coupled with mass spectrometry. Mass spectrometric analysis of intact proteins permits the characterisation of protein isoforms. Recent developments in stable isotope labelling techniques and chemical tagging allow the mass spectrometry based differential display and quantitation of proteins, and newly established affinity procedures enable the targeted characterisation of post-translationally modified proteins. Finally, advances in mass spectrometric imaging allow the gathering of specific information on the local molecular composition, relative abundance and spatial distribution of peptides and proteins in thin tissue sections.

Affinity Labels↗
PubMed2002

Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness.

By primarily measuring changes in transcript and protein abundance, conventional genomics and proteomics methods may fail to detect significant posttranslational events that regulate protein activity and, ultimately, cell behavior. To address these limitations, activity-based proteomic technologies that measure dynamics in protein function on a global scale would be of particular value. Here, we describe the application of a chemical proteomics strategy to quantitatively compare enzyme activities across a panel of human breast and melanoma cancer cell lines. A global analysis of the activity, subcellular distribution, and glycosylation state for the serine hydrolase superfamily resulted in the identification of a cluster of proteases, lipases, and esterases that distinguished cancer lines based on tissue of origin. Strikingly, nearly all of these enzyme activities were down-regulated in the most invasive cancer lines examined, which instead up-regulated a distinct set of secreted and membrane-associated enzyme activities. These invasiveness-associated enzymes included urokinase, a secreted serine protease with a recognized role in tumor progression, and a membrane-associated hydrolase KIAA1363, for which no previous link to cancer had been made. Collectively, these results suggest that invasive cancer cells share discrete proteomic signatures that are more reflective of their biological phenotype than cellular heritage, highlighting that a common set of enzymes may support the progression of tumors from a variety of origins and thus represent attractive targets for the diagnosis and treatment of cancer.

Biomarkers, Tumor↗
PubMed2025

ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.

MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Humans↗
PubMed2006

Proteomic analysis of mammalian basic proteins by liquid-based two-dimensional column chromatography.

To develop a standard method for separating highly basic proteins in mammalian cells, we established a 2-D LC separation system coupled with chromatofocusing/nonporous RP column chromatography (CF/NPRPC) in a ProteomeLab PF2D system. After standardizing conditions for 2-D LC, a 2-D liquid protein map of uninfected macrophage proteins with pH range 8.3-11.3 was constructed, and then compared with a macrophage protein map made after infection with Candida albicans. The results demonstrate that 2-D LC offers both high resolution and reproducibility for separation of highly basic, macrophage proteins. After protein identification using a nano 2-D LC-MS/MS Proteomics Solution System, quantitative determination of the changes in the differentially expressed proteins (e.g., galectin-3) in C. albicans-infected macrophages was also accomplished by measuring the peak area of the chromatogram in 2-D LC. The result from this measurement of galectin-3 expression shows a 3.41-fold decrease in the infected macrophage cells, which was further confirmed by that from the RT-PCR of mRNA of galectin-3. Thus, 2-D LC coupled with CF/NPRPC could be applicable to common analysis of highly basic proteins in a high-throughput manner.

Animals↗