Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “proteomic profiling”

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 991 records · Page 55Linked to original sources

High-Fat Diet and a High Amyloid Load Interact to Induce PKC-α Dependent Synaptic Insulin Resistance.

A plethora of studies suggest that a high-fat diet in combination with a high amyloid load causes synaptic insulin resistance and is a risk factor for Alzheimer's disease. Our understanding of the underlying mechanisms is still fragmented. To gain new insights, we conducted integrated proteomic and phosphoproteomic profiling of hippocampal synaptosomes from WT and a transgenic mouse line with a high amyloid load (heterozygous TBA2.1 mice) that show no overt signs of neurodegeneration and dementia. Mice were fed with a regular or high-fat diet. Data-independent acquisition quantified over 5400 proteins, revealing a stable synaptic proteome across conditions. However, the combination of high amyloid load and high-fat diet triggered coordinated remodeling of lipid metabolism pathways, particularly mitochondrial and peroxisomal fatty acid catabolism. Phosphoproteomic analysis showed pronounced activation of lipid- and stress-responsive kinases, including protein kinase C-α, along with increased inhibitory phosphorylation of insulin receptor substrates (IRS1/2). In vitro experiments indicate that blocking protein kinase C-α indeed prevents synaptic insulin resistance in primary neurons. The findings suggest that this proteomic workflow, combined with kinase pathway analysis, can reveal nodal points for interventions in a complex disease state with a trajectory to Alzheimer's disease.

Animals↗

Multi-omics approaches in idiopathic pulmonary fibrosis: from molecular mechanisms to therapeutic targets and precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

biomarkers↗

Reverse-phase protein microarrays for tissue-based analysis.

The deciphering of the human genome has elucidated our biological structural design and has generated insights into disease development and pathogenesis. At the same time, knowledge of genetic changes during disease processes has demonstrated the need to move beyond genomics towards proteomics and a systems biology approach to science. Analyzing the proteome comprises more than just a numeration of proteins. In fact, it characterizes proteins within cells in the context of their functional status and interactions in their physiological micro- and macroenvironments. As dysregulated signaling often underpins most human diseases, an overarching goal of proteomics is to profile the working state of signaling pathways, to develop 'circuit maps' of normal and diseased protein networks and identify hyperactive, defective or inoperable transduction pathways. Reverse-phase protein microarrays represent a new technology that can generate a multiplex readout of dozens of phosphorylated events simultaneously to profile the state of a signaling pathway target even after the cell is lyzed and the contents denatured.

Animals↗

Bacterial proteomics and its role in antibacterial drug discovery.

Gene-expression profiling technologies in general, and proteomic technologies in particular have proven extremely useful to study the physiological response of bacterial cells to various environmental stress conditions. Complex protein toolkits coordinated by sophisticated regulatory networks have evolved to accommodate bacterial survival under ever-present stress conditions such as varying temperatures, nutrient availability, or antibiotics produced by other microorganisms that compete for habitat. In the last decades, application of man-made antibacterial agents resulted in additional bacterial exposure to antibiotic stress. Whereas the targeted use of antibiotics has remarkably reduced human suffering from infectious diseases, the ever-increasing emergence of bacteria that are resistant to antibiotics has led to an urgent need for novel antibiotic strategies. The intent of this review is to present an overview of the major achievements of proteomic approaches to study adaptation networks that are crucial for bacterial survival with a special emphasis on the stress induced by antibiotic treatment. A further focus will be the review of the, so far few, published efforts to exploit the knowledge derived from bacterial proteomic studies directly for the antibacterial drug-discovery process.

Anti-Bacterial Agents↗

Unraveling Plant Nuclear Envelope Composition Using Proximity Labeling Proteomics.

The nuclear envelope (NE) defines the eukaryotic cell and functions in a myriad of fundamental cellular processes including but not limited to signal transduction, lipid metabolism, chromatin organization, and nucleocytoplasmic transportation. Although the general structure of the NE is well-conserved across eukaryotic kingdoms, its composition and functions vary substantially between species and remain largely unknown in plants. In this chapter, we describe a proximity-labeling-based proteomic approach to profile novel NE components in the model organism Arabidopsis. This method is generally suitable for the identification of protein components in subcellular compartments or protein complexes that are poorly accessible to traditional mass spectrometry approaches and can be easily applied to other plant species. In addition to giving a step-by-step detailed description of the proximity labeling proteomics procedure in plant samples, we also provide guidelines on the appropriate use of controls and statistical analysis to achieve a highly specific selection of probed candidates.

Proteomics↗

A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.

Algorithms↗

Biomarker Analysis from Patients with Metastatic PDAC Treated with TGFβ Antibody NIS793 plus Abraxane + Gemcitabine versus Abraxane + Gemcitabine Alone in a Phase II, Open-Label, Randomized Study.

PURPOSE: Transforming growth factor β (TGFβ) plays a dual role in cancer, acting as a tumor suppressor early in the disease but promoting progression and immune evasion when dysregulated. In pancreatic ductal adenocarcinoma (PDAC), TGFβ-driven desmoplasia fosters chemoresistance and immunosuppression, limiting therapeutic efficacy. NIS793, a fully human mAb targeting TGFβ, demonstrated antifibrotic and immunomodulatory activity in preclinical models and early-phase trials. PATIENTS AND METHODS: We conducted a randomized, open-label, phase II study in treatment-naïve patients with metastatic PDAC (mPDAC) to evaluate NIS793 ± spartalizumab (anti-PD-1) combined with nab-paclitaxel (or Abraxane)/gemcitabine (ABRA/GEM) versus ABRA/GEM alone. The primary endpoint was progression-free survival (PFS); secondary endpoints included overall survival (OS), safety, pharmacokinetics, and biomarker analyses. Exploratory assessments included paired tumor RNA sequencing, cell-free DNA profiling, and plasma proteomics. RESULTS: NIS793 demonstrated target engagement and suppression of TGFβ signaling, confirmed by transcriptomic and proteomic analyses. Stromal remodeling was evident, with significant downregulation of cancer-associated fibroblast markers (Acta2, Fap) and collagen-related signatures. Despite proof of mechanism, clinical efficacy was not observed: Median PFS and OS were comparable or numerically worse in the NIS793 arm versus control (HR for OS in NIS793 + ABRA/GEM vs. ABRA/GEM: 1.32; 95% confidence interval, 0.84-2.07). The safety profile was manageable, with no unexpected toxicities. Biomarker data revealed increased expression of neutrophil-related genes after treatment, suggesting potential induction of tumor-promoting inflammation. CONCLUSIONS: NIS793 effectively inhibited TGFβ signaling and led to stromal remodeling but failed to improve outcomes in mPDAC. These findings highlight the complexity of TGFβ biology and caution against its blockade in combination with chemotherapy for PDAC. Future strategies should consider context-dependent effects of TGFβ inhibition.

Humans↗

Proteomic analysis of ursolic acid-induced apoptosis in cervical carcinoma cells.

Proteomic analyses can efficiently detect the variation of protein in high throughput screening. Using 2DE/MALDI-TOF-MS and SELDI-TOF-MS, we tried to search several cellular proteins that are responsive to ursolic acid (UA) in HeLa cervical carcinoma cells. Compared to control, UA-treated HeLa cells unfolded 25 proteins in significant changes by 2DE/MALDI-TOF-MS, most of which were involved in apoptosis. SELDI-TOF-MS with two types of protein chips profiled and analyzed proteomic features after administration of UA. Interestingly, eight polypeptide peaks can be detected. Further identification and characterization of these proteins may build the molecular basis of UA-induced apoptosis and provide insight into the anti-proliferative mechanism in cervical carcinoma cells.

Apoptosis↗

Pathogenicity of Cadophora luteo-olivacea on Quercus robur and multi-omics characterization of antagonism by Trichoderma atroviride.

Pedunculate oak (Quercus robur L.) is a foundation tree species in European forests and reforestation programs, but nursery propagated seedlings can harbor cryptic trunk diseases pathogens. Cadophora luteo-olivacea, known from grapevine trunk diseases, has been detected in oak nurseries, yet its pathogenicity on oak and interactions with antagonistic fungi remain unclear. We fulfilled Koch's postulates for C. luteo-olivacea isolate CZ_395 on Q. robur seedlings under experimental inoculation conditions and quantified growth reduction of C. luteo-olivacea by Trichoderma atroviride isolate CZ_180 in dual culture. Proteomic and metabolomic profiling of the contact zone was performed at two post contact sampling points, 4 and 8&#xa0;dpi, to identify candidate molecular signatures associated with the interaction. Inoculated seedlings developed extensive stem lesions (mean 11.9 cm), whereas controls showed minimal wound response (mean 0.9&#xa0;cm; p&#x2009;<&#x2009;0.001). In dual culture, T. atroviride reduced the visible colony development and radial growth of C. luteo-olivacea under the tested in vitro conditions. Contact zone proteomics revealed 257 differentially abundant proteins at 8&#xa0;days, including cell wall targeting hydrolases, secreted proteases, oxidoreductases (44 upregulated), and transporters. Metabolomics detected contact specific changes in amino acids, central carbon intermediates, and lipid-associated features, including reduced ergosterol. This study demonstrates that C. luteo-olivacea can induce necrotic lesions in Q. robur under experimental inoculation conditions and identifies proteomic and metabolomic signatures associated with the interaction between T. atroviride and C. luteo-olivacea, providing a basis for nursery risk assessment and future evaluation of biocontrol potential.

Quercus↗

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

Quantitative detection of phosphoproteins by combination of two-dimensional difference gel electrophoresis and phosphospecific fluorescent staining.

Here we combine a standard two-dimensional difference gel electrophoresis (DIGE) protocol with subsequent post-staining of gels with phosphospecific fluorescent Pro-Q Diamond dye. The combination of these two methods for fluorescence detection of proteins allows quantitative detection of phosphoproteins in 2-DE-gels. We established this protocol within a functional proteomics experiment. Mammary epithelial cells (EpH4) were stimulated in culture by epidermal growth factor (EGF), endosomal fractions prepared after subcellular fractionation and phosphorylated proteins successfully detected on endosomes. For instance, Endo A cytokeratin, known as phosphoprotein and differentiation marker inducible by MAPK signaling, was identified by matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS). With this protocol, all steps of combined proteome and phosphoproteome profiling experiments are significantly simplified and accelerated, taking full advantage of both methods in terms of specificity, sensitivity and accuracy of quantification.

Animals↗

Early proteomic and metabolic signatures of liver and eye in OAT-deficient mice.

Ornithine aminotransferase (OAT) deficiency causes hyperornithinemia and gyrate atrophy (GA) of the choroid and retina, a rare inherited retinal degeneration. To understand the early molecular changes that make the eye susceptible to damage, we performed quantitative proteomic and metabolomic profiling of liver, retina, and retinal pigment epithelium and choroid (RPE/Cho) from OAT-deficient (Oatrhg) mice prior to detectable vision impairment. In addition to reduced OAT expression and elevated ornithine, methylation-related metabolites such as N(6)-methyl-lysine were altered in all examined tissues of Oatrhg mice. In the liver, excess ornithine was directed into urea cycle metabolism, together with altered expression of detoxification enzymes and histone H2B proteins. In contrast, the retina showed minimal proteomic changes but pronounced alterations in amino acid pathways that support glutamate homeostasis. The RPE/Cho demonstrated the most extensive proteomic changes, particularly in mitochondrial metabolism, cytoskeleton, and extracellular matrix, along with changes in metabolites involved in lysine metabolism, energy metabolism, and antioxidant capacity. Incubation with 13C lysine demonstrated that lysine was primarily degraded in RPE/Cho but not the retina, and ornithine enhanced lysine degradation in an OAT-dependent manner. Together, these findings highlight common and tissue-specific impacts of OAT on the liver and ocular tissues and provide insight into early molecular changes that contribute to the selective vulnerability of the eye in GA. Proteomics data are available via ProteomeXchange (PXD063614) and metabolomics data via MassIVE repository (MSV000101103).

Animals↗

Protein expression profiles in an endosymbiotic cyanobacterium revealed by a proteomic approach.

Molecular mechanisms behind adaptations in the cyanobacterium (Nostoc sp.) to a life in endosymbiosis with plants are still not clarified, nor are the interactions between the partners. To get further insights, the proteome of a Nostoc strain, freshly isolated from the symbiotic gland tissue of the angiosperm Gunnera manicata Linden, was analyzed and compared with the proteome of the same strain when free-living. Extracted proteins were separated by two-dimensional gel electrophoresis and were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry combined with tandem mass spectrometry. Even when the higher percentage of differentiated cells (heterocysts) in symbiosis was compensated for, the majority of the proteins detected in the symbiotic cyanobacteria were present in the free-living counterpart, indicating that most cellular processes were common for both stages. However, differential expression profiling revealed a significant number of proteins to be down-regulated or missing in the symbiotic stage, while others were more abundant or only expressed in symbiosis. The differential protein expression was primarily connected to i) cell envelope-associated processes, including proteins involved in exopolysaccharide synthesis and surface and membrane associated proteins, ii) to changes in growth and metabolic activities (C and N), including upregulation of nitrogenase and proteins involved in the oxidative pentose phosphate pathway and downregulation of Calvin cycle enzymes, and iii) to the dark, microaerobic conditions offered inside the Gunnera gland cells, including changes in relative phycobiliprotein concentrations. This is the first comprehensive analysis of proteins in the symbiotic state.

Bacterial Proteins↗

Toxicogenomic approach for assessing toxicant-related disease.

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.

Animals↗

Applications of display technologies to proteomic analyses.

With the rapid accumulation of genetic information, development of general experimental approach suitable for large scale annotation and profiling of the whole proteome have become one of the major challenges in postgenomic era. Biomolecular display technologies, which allow expressing of a large pool of modularly coded biomolecules, are extremely useful for accessing and analyzing protein diversity and interaction profile on a large scale. Recent advances in protein display technologies and their applications to proteomic analyses have been discussed.

Cloning, Molecular↗

Pathogenesis of psoriasis and psoriatic arthritis: Insights from animal models and single-cell and spatial transcriptomic analyses of skin, synovium and entheses.

Psoriasis (PsO) and psoriatic arthritis (PsA) are immune-mediated diseases characterized by chronic systemic inflammation, including inflammation of the skin and joints. Recent advances in animal models, single-cell transcriptomics, spatial transcriptomics, and proteomics have greatly enhanced our understanding of disease pathogenesis. Mouse models exhibit key features of skin and joint inflammation, facilitating analysis of molecular pathways, and identification of therapeutic targets. Single-cell and spatial transcriptomic analyses have revealed cell-type-specific contributions to inflammation, highlighting interactions between keratinocytes, T cells, fibroblasts, and dendritic cells that drive psoriatic pathology. In psoriatic synovium, type 17 tissue-resident memory T cells, monocytes, and fibroblasts contribute to local inflammation and joint damage, whereas the roles of B cells and plasma cells are less clear. Proteomic and metabolomic profiling in patients with PsA has identified circulating protein signatures and metabolites associated with disease progression, sex-specific differences, and response to therapy. The integration of these multiomic approaches provides a detailed map of immune-stromal-epithelial crosstalk across skin, synovium, and entheses, uncovering mechanisms that were previously inaccessible. These insights have implications for predicting disease progression, identifying novel therapeutic targets, and optimizing treatment strategies. Collectively, advances in animal models and multiomic profiling are reshaping our understanding of PsO and PsA, providing a framework for future research, disease monitoring, and therapeutic development.

Animals↗

Proteomic analysis of Leishmania mexicana differentiation.

We have resolved the proteome of axenically differentiated Leishmania mexicana parasites by two-dimensional gel electrophoresis (2DE), employing optimised, robust and reproducible procedures, and visualised (by silver staining) approximately 2000 protein species in each of three developmental stages: procyclic promastigotes, metacyclic promastigotes and amastigotes. This analysis has used homogeneous populations of these parasite stages, characterised according to their morphology, protease and nuclease activity profiles and expression of stage-specific antigens. Following comparison of the whole proteome profiles between stages, 47 spots were found to be stage-specific, while a further 100 spots changed in intensity during differentiation. The majority of "unique" spots were expressed during the infective stages of parasite differentiation, metacyclic promastigotes and amastigotes. CapLC-QTOF mass spectrometry has allowed the identification of 47 protein species to date, including a number which are only detected in the amastigote stage. Proteins identified are members of eight functionally related groupings, some of which are implicated in infectivity and host-parasite interactions.

Animals↗

Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards.

A new method is presented for quantifying proteomic and metabolomic profile data by liquid chromatography-mass spectrometry (LC-MS) with electrospray ionization. This biotechnology provides differential expression measurements and enables the discovery of biological markers (biomarkers). Work presented here uses human serum but is applicable to any fluid or tissue. The approach relies on linearity of signal versus molecular concentration and reproducibility of sample processing. There is no use of isotopic labeling or chemically similar standard materials. Linear standard curves are reported for a variety of compounds introduced into human serum. As a measure of analytical reproducibility for proteome and metabolome sampling, median coefficients of variation of 25.7 and 23.8%, respectively, were determined for approximately 3400 molecular ions (not counting their numerous isotopes) from 25 independently processed human serum samples, corresponding to a total of 85000 individual molecular ion measurements.

Humans↗