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PubMed2006

Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis.

In this study, we sought to explore the merit of proteomic profiling strategies in patients with cancer before and during radiotherapy in an effort to discover clinical biomarkers of radiation exposure. Patients with a diagnosis of cancer provided informed consent for enrollment on a study permitting the collection of serum immediately before and during a course of radiation therapy. High-resolution surface-enhanced laser desorption and ionization-time of flight (SELDI-TOF) mass spectrometry (MS) was used to generate high-throughput proteomic profiles of unfractionated serum samples using an immobilized metal ion-affinity chromatography nickel-affinity chip surface. Resultant proteomic profiles were analyzed for unique biomarker signatures using supervised classification techniques. MS-based protein identification was then done on pooled sera in an effort to begin to identify specific protein fragments that are altered with radiation exposure. Sixty-eight patients with a wide range of diagnoses and radiation treatment plans provided serum samples both before and during ionizing radiation exposure. Computer-based analyses of the SELDI protein spectra could distinguish unexposed from radiation-exposed patient samples with 91% to 100% sensitivity and 97% to 100% specificity using various classifier models. The method also showed an ability to distinguish high from low dose-volume levels of exposure with a sensitivity of 83% to 100% and specificity of 91% to 100%. Using direct identity techniques of albumin-bound peptides, known to underpin the SELDI-TOF fingerprints, 23 protein fragments/peptides were uniquely detected in the radiation exposure group, including an interleukin-6 precursor protein. The composition of proteins in serum seems to change with ionizing radiation exposure. Proteomic analysis for the discovery of clinical biomarkers of radiation exposure warrants further study.

Biomarkers, Tumor↗
PubMed2026

Transpulmonary proteomic gradient analysis in women with pulmonary arterial hypertension associated with systemic sclerosis.

This study investigated proteomic alterations in the pulmonary circulation of patients with pulmonary arterial hypertension associated with systemic sclerosis (PAH-SSc) by analyzing the transpulmonary protein gradient and comparing the proteomic profiles with systemic sclerosis (SSc) without PAH. Twenty women were included (10 PAH-SSc, 64.6 ± 10.8 years; 10 SSc, 62.8 ± 11.5 years). The transpulmonary gradient was defined as the difference in biomarker concentrations between wedge-position and pulmonary artery blood samples. Peptides were analysed using liquid chromatography-mass spectrometry, and differentially abundant proteins were identified with Proteome Discoverer. Protein-protein interaction networks were generated with STRING and visualized in Cytoscape. A total of 270 proteins were detected, with no significant transpulmonary gradient alterations. However, patients with PAH-SSc showed distinct proteomic profiles compared to SSc. Multivariate analysis identified 48 differentially abundant proteins in pulmonary artery plasma, with 15 overrepresented and 33 downregulated in PAH-SSc. Among these, the downregulation of transforming growth factor-beta-induced protein ig-h3 (TGFβI/ig-h3) points to a potential involvement of the TGF-β-related extracellular matrix remodelling pathway in PAH-SSc. However, further validation in larger and independent cohorts is required before its relevance as a biomarker or therapeutic target can be established. In conclusion, while no transpulmonary proteomic gradient was observed, the proteomic profiles of PAH-SSc and SSc were different. The profile in PAH-SSc was characterized by differences in immune response, lipid metabolism, and hemostatic proteins. SIGNIFICANCE: This study offers the first proteomic characterization of the transpulmonary gradient in PAH-SSc and SSc. Although no differences in the gradient were found, the pulmonary artery plasma proteome of PAH-SSc patients showed a distinct pattern compared to SSc. Several proteins associated with immune function, haemostasis, and cellular processes were altered, which may indicate specific pathophysiological features of PAH-SSc or suggest how lung dysfunction develops in SSc. Targeting dysregulated proteins like TGFβI/ig-h3 or addressing immune-coagulation imbalances may support future research studies. Overall, these findings refine the molecular profile of PAH-SSc and provide a basis for future large-scale studies aimed at clarifying disease mechanisms and identifying clinically relevant molecular signatures.

Humans↗
PubMed2026

Small extracellular vesicles reflect senescence progression in human bone marrow-derived mesenchymal stem cells during hollow fiber bioreactor culture.

Prolonged three-dimensional culture exposes stem cells to sustain microenvironmental and mechanical stresses that can promote aging- and senescence-associated phenotypic alterations. This study examined how long-term expansion of human bone marrow-derived mesenchymal stem cells (BMSCs) in a hollow fiber bioreactor (HFB) influences cellular senescence and the molecular composition of secreted small extracellular vesicles (sEVs). During extended HFB culture, BMSCs exhibited progressive morphological flattening and cytoskeletal disorganization, accompanied by increased senescence-associated β-galactosidase activity and immunophenotypic remodeling characterized by reduced fluorescence intensity and spatial redistribution of canonical MSC markers, consistent with a stress-adapted, early senescence-associated cellular state. In parallel, sEVs were collected longitudinally over 40 days and characterized by nanoparticle tracking analysis, immunoblotting, and quantitative proteomics. While vesicle size, marker expression, and yield remained stable throughout culture, proteomic profiling revealed pronounced, phase-dependent remodeling of sEV cargo, including coordinated alterations in oxidative stress-related processes, lysosomal and extracellular matrix-associated pathways, and relative depletion of cytoskeletal and translational components. Notably, these vesicular signatures closely mirrored senescence-associated changes observed at the cellular level. The strong correspondence between cellular phenotypes and sEV proteomic profiles establishes vesicle analysis as a convergent and noninvasive readout of BMSC aging, enabling sensitive monitoring of senescence progression while reducing reliance on parallel, labor-intensive cellular assays. Collectively, these findings indicate that prolonged HFB culture promotes a controlled, stress-associated senescence program in BMSCs and position sEV proteomic profiling as a robust approach for assessing stem cell aging dynamics during long-term three-dimensional bioreactor culture.

Mesenchymal Stem Cells↗
PubMed2002

Proteomic approaches to tumor marker discovery.

CONTEXT: Current tumor markers for ovarian cancer still lack adequate sensitivity and specificity to be applicable in large populations. High-throughput proteomic profiling and bioinformatics tools allow for the rapid screening of a large number of potential biomarkers in serum, plasma, or other body fluids. OBJECTIVE: To determine whether protein profiles of plasma can be used to identify potential biomarkers that improve the detection of ovarian cancer. DESIGN: We analyzed plasma samples that had been collected between 1998 and 2001 from patients with sporadic ovarian serous neoplasms before tumor resection at various International Federation of Gynecology and Obstetrics stages (stage I [n = 11], stage II [n = 3], and stage III [n = 29]) and from women without known neoplastic disease (n = 38) using proteomic profiling and bioinformatics. We compared results between the patients with and without cancer and evaluated their discriminatory performance against that of the cancer antigen 125 (CA125) tumor marker. RESULTS: We selected 7 biomarkers based on their collective contribution to the separation of the 2 patient groups. Among them, we further purified and subsequently identified 3 biomarkers. Individually, the biomarkers did not perform better than CA125. However, a combination of 4 of the biomarkers significantly improved performance (P < or =.001). The new biomarkers were complementary to CA125. At a fixed specificity of 94%, an index combining 2 of the biomarkers and CA125 achieves a sensitivity of 94% (95% confidence interval, 85%-100.0%) in contrast to a sensitivity of 81% (95% confidence interval, 68%-95%) for CA125 alone. CONCLUSIONS: The combined use of bioinformatics tools and proteomic profiling provides an effective approach to screen for potential tumor markers. Comparison of plasma profiles from patients with and without known ovarian cancer uncovered a panel of potential biomarkers for detection of ovarian cancer with discriminatory power complementary to that of CA125. Additional studies are required to further validate these biomarkers.

Adult↗
PubMed2024

Revealing novel protein interaction partners of glyphosate in Escherichia coli.

Despite all debates about its safe use, glyphosate remains the most widely applied active ingredient in herbicide products, with renewed approval in the European Union until 2033. Non-target organisms are commonly exposed to glyphosate as a matter of its mode of application, with its broader environmental and biological impacts remaining under investigation. Glyphosate displays structural similarity to phosphoenolpyruvate (PEP), thereby competitively inhibiting the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), crucial for the synthesis of aromatic amino acids in plants, fungi, bacteria, and archaea. Most microbes, including the gut bacterium Escherichia coli (E. coli), possess a glyphosate-sensitive class I EPSPS, making them vulnerable to glyphosate's effects. Yet, little is known about glyphosate's interactions with other bacterial proteins or its broader modes of action at the proteome level. Here, we employed a quantitative proteomics and thermal proteome profiling (TPP) approach to identify novel protein binding partners of glyphosate in the E. coli proteome. Glyphosate exposure significantly altered amino acid synthesizing pathways. The abundance of shikimate pathway proteins was increased, suggesting a compensatory mechanism. Extracellular riboflavin concentrations were elevated upon glyphosate exposure, while intracellular levels remained stable. Beyond the target enzyme EPSPS, thermal proteome profiling indicated an effect of glyphosate on the thermal stability of certain proteins, including AroH and ProA, indicating interactions. Similar to the competitive binding between PEP and glyphosate at EPSPS, one reason for the interaction of AroH and ProA with the herbicide could be a high structural similarity between their substrates and glyphosate. Overall, glyphosate induced metabolic disturbances in E. coli, extending beyond its primary target, thereby providing new insights into glyphosate's broader impact on microbial systems.

Glyphosate↗
PubMed2004

Association between ductal fluid proteomic expression profiles and the presence of lymph node metastases in women with breast cancer.

BACKGROUND: Proteomic analysis of nipple aspiration fluid (NAF) is a noninvasive method for studying the local biologic microenvironment of the breast ducts where carcinoma originates. METHODS: NAF samples from each breast of 23 women with stage I or II unilateral invasive breast carcinoma were collected, and protein expression was analyzed comprehensively by using protein arrays and time-of-flight mass spectrometry. Blinded hierarchical clustering analysis was performed to identify potential associations between protein expression patterns and clinicopathologic factors. RESULTS: After analysis of all spectra, 463 distinct peaks in the mass range of 7 to 30 kD were identified in NAF samples. Blinded hierarchical clustering analysis of protein expression patterns demonstrated a conservation of these patterns between the breasts of individual patients (P=.0003 x 10(-12)). Hierarchical clustering revealed an association between protein expression patterns, and the presence and absolute number of axillary lymph nodes containing metastases (P=.038). CONCLUSIONS: Protein expression patterns are highly conserved between cancerous and noncancerous breasts in women with unilateral invasive breast cancer; unique expression patterns may be associated with extent of disease. High-throughput proteomic methods may reveal biologically relevant proteins involved in carcinogenesis and progression of disease.

Adult↗
PubMedDate not supplied

The evolving role of proteomics in the early detection of breast cancer.

There has been emerging interest in the examination of tumor protein expression (proteomics) as a means to identify novel diagnostic and therapeutic targets in women with breast cancer. Specifically, several investigators have examined biological fluids (serum and breast ductal fluid) and breast tissue in an attempt to detect novel proteomic profiles in women with breast carcinoma. The current tools of proteomic research are evolving, but include two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Initial studies have identified several unique biomarkers and proteomic profiles that were able to discriminate between non-cancer and breast cancer patients. In the future, the application of large-scale proteomic technology may provide a means of early detection, surveillance, and identification of potential therapeutic targets.

Biomarkers, Tumor↗
PubMed2025

Proximity Proteomics to Profile Ebola Virus Protein Interactome in Its Functional Context.

Proximity labeling-based proteomics (proximity proteomics) has emerged as a popular and versatile approach to illuminate the molecular interactions between viruses and their hosts. In this approach, a proximity labeling enzyme tag is fused to a bait protein and labels neighboring proteins with a chemical handle such as biotin, allowing for downstream affinity purification. Compared to another widely used technique, affinity purification coupled mass spectrometry, proximity proteomics enables the detection of low affinity or transient interactors that might have important functions in the viral life cycle. Further, proximity proteomics can identify interactors of a labile bait protein, of which affinity purification is technically challenging. Here, we describe a proximity proteomic protocol to identify cellular interactors of the Ebola virus polymerase. A similar strategy is readily applicable to elucidate the virus-host interactions for Marburg virus.

Ebolavirus↗
PubMed2004

Molecular classification and survival prediction in human gliomas based on proteome analysis.

The biological features of gliomas, which are characterized by highly heterogeneous biological aggressiveness even in the same histological category, would be precisely described by global gene expression data at the protein level. We investigated whether proteome analysis based on two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry can identify differences in protein expression between high- and low-grade glioma tissues. Proteome profiling patterns were compared in 85 tissue samples: 52 glioblastoma multiforme, 13 anaplastic astrocytomas, 10 atrocytomas, and 10 normal brain tissues. We could completely distinguish the normal brain tissues from glioma tissues by cluster analysis based on the proteome profiling patterns. Proteome-based clustering significantly correlated with the patient survival, and we could identify a biologically distinct subset of astrocytomas with aggressive nature. Discriminant analysis extracted a set of 37 proteins differentially expressed based on histological grading. Among them, many of the proteins that were increased in high-grade gliomas were categorized as signal transduction proteins, including small G-proteins. Immunohistochemical analysis confirmed the expression of identified proteins in glioma tissues. The present study shows that proteome analysis is useful to develop a novel system for the prediction of biological aggressiveness of gliomas. The proteins identified here could be novel biomarkers for survival prediction and rational targets for antiglioma therapy.

Amino Acid Sequence↗
PubMed2003

[Establishment of two-dimensional gel electrophoresis profiles of proteome from CD34(+) hematopoietic stem/progenitor cells].

OBJECTIVE: To investigate the differential expression of proteins in bone marrow (BM) and mobilized peripheral blood (MPB) CD34+ cells. METHODS: Immunomagnetic beads were used to separate and purify CD34+ cells from the BM and the MPB mononuclear cells (MNCs) mobilized by granulocyte colony-stimulating factor (G-CSF) of normal subjects. The whole cell proteins in CD34+ cells were extracted by freeze-thaw lysis, and applied for two-dimensional electrophoresis (2-DE), the results analyzed with image analysis software. RESULTS: The average purity of CD34+ cells was 92.33%+/-2.65% in output, with an average cell number of (1.12+/-0.42) x 10(6). 2-DE techniques were optimized, which yielded satisfactory 2-DE profiles of the proteome, showing the different expressions of the proteins between different CD34+ cells. CONCLUSIONS: Immunomagnetic beads in combination with 2-DE is applicable for research of hematopoietic stem/progenitor cells proteome. There are differences in the protein expressions between BM- and MPB-derived CD34+ cells.

Antigens, CD34↗
PubMed2005

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy.

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera from each patient were analyzed before and after treatment, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite the small number of patients, highly significant and consistent differences were observed before and after response to therapy in all patients. Of 282 spectral peaks identified, 23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences were observed regardless of whether patients responded to conventional therapy or to MRA. These peaks represent potential biomarkers of active disease. One such peak was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles from serum samples obtained at the time of active disease were compared between the two patient groups. Nine peaks had mean signal intensities significantly different (P < 0.001) between active disease in patients who responded to conventional therapy and in patients who failed to respond, suggesting a possible profile predictive of response. Collectively, these data demonstrate the presence of serum proteomic profiles in SJIA that are reflective of active disease and suggest the feasibility of using the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of novel biomarkers in autoimmune diseases.

Anti-Inflammatory Agents↗
PubMed2004

Impact of 'ome' analyses on inverse metabolic engineering.

Genome-wide or large-scale methodologies employed in functional genomics such as DNA sequencing, transcription profiling, proteomics, and metabolite profiling have become important tools in many metabolic engineering strategies. These techniques allow the identification of genetic differences and insight into their cellular effects. In the field of inverse metabolic engineering mapping of differences between strains with different degree of a certain desired phenotype and subsequent identification of factors conferring that phenotype are an essential part. Therefore, the tools of functional genomics in particular have the potential to promote and expand inverse metabolic engineering. Here, we review the use of functional genomics methods in inverse metabolic engineering, examples are presented, and we discuss the identification of targets for metabolic engineering with low fold changes using these techniques.

Animals↗
PubMed2000

Proteome profiling-pitfalls and progress.

In this review we examine the current state of analytical methods in proteomics. The conventional methodology using two-dimensional electrophoresis gels and mass spectrometry is discussed, with particular reference to the advantages and shortcomings thereof. Two recently published methods which offer an alternative approach are presented and discussed, with emphasis on how they can provide information not available via two-dimensional gel electrophoresis. These two methods are the isotope-coded affinity tags approach of Gygi et al. and the two-dimensional liquid chromatography-tandem mass spectrometry approach as presented by Link et al. We conclude that both of these new techniques represent significant advances in analytical methodology for proteome analysis. Furthermore, we believe that in the future biological research will continue to be enhanced by the continuation of such developments in proteomic analytical technology.

Chromatography, Liquid↗
PubMed2005

Functional ingredient production: application of global metabolic models.

The biotechnology industry continuously explores new ways to improve the performance of microbial strains in fermentation processes. Recent focus has been on new genome-wide modelling approaches in functional genomics, which aim to take full advantage of genome sequence data, transcription profiling, proteomics and metabolite profiling for strain improvement. The integration of global metabolic models with genetic and regulatory models will be essential for the practice of metabolic engineering for strain improvement to move forward, simply because we cannot rely on our intuition to grasp the complexity of the biological systems involved.

Bacterial Physiological Phenomena↗
PubMed2026

Early Cardiomyopathy in Prediabetic NDPK-B-Deficient Mice Is Associated with Remodeling of the Mitochondrial O-GlcNAc Proteome.

Diabetic cardiomyopathy (DCM) is characterized by myocardial remodeling that may already be evident during prediabetes, yet the molecular alterations accompanying these early changes remain poorly understood. The present study examined mouse models of Nucleoside diphosphate kinase B (NDPK-B)-deficient prediabetes and streptozotocin-induced diabetes using O-GlcNAc-associated proteomic profiling to define stage-specific molecular alterations during the progression from prediabetic to diabetic cardiomyopathy. Both models exhibited increased left ventricular extracellular matrix deposition and impaired diastolic function, together with activation of the hexosamine biosynthesis pathway. Profiling of O-GlcNAc-associated proteins uncovered extensive remodeling of the mitochondrial proteome already at the prediabetic stage, with respiratory complex I among the most prominently altered targets, alongside changes in substrate metabolism and inflammatory signaling. In overt DCM, the putative O-GlcNAc proteomic profile was associated with a shift toward wider lipid-dependent metabolic reprogramming and remodeling of mitochondrial proteins. These findings identify early remodeling of the mitochondrial O-GlcNAc-associated proteome as a molecular signature of prediabetic cardiomyopathy and highlight respiratory complex I proteins as candidate targets for future mechanistic investigations.

Animals↗
PubMed2026

Multi-Omics Analysis Reveals Molecular Networks and Key Pathways Associated with Cysteine- and Methionine-Mediated Biosynthesis of Sulfur-Containing Flavor Metabolites in Lentinula edodes.

Lentinula edodes is renowned for its unique aroma, which is characterized by various volatile sulfur-containing flavor metabolites (SCFMs). Cysteine and methionine could enhance the SCFMs biosynthesis in L. edodes; however, the underlying metabolic pathways remain unclear. To bridge this gap, integrated proteomic and metabolomic analysis were performed to decipher pathways through which cysteine and methionine regulate SCFM biosynthesis. Results showed that exogenous cysteine and methionine supplementation significantly increased the content of lenthionine, the key aroma compound of shiitake mushrooms. Both treatments induced substantial changes in the proteomic and metabolomic profiles. Proteomic analysis revealed that differentially expressed proteins were predominantly enriched in cysteine and methionine metabolism and sulfur metabolism following cysteine treatment, whereas methionine treatment mainly affected proteins associated with tryptophan metabolism and sulfur metabolism. Metabolomic analysis showed that differentially accumulated metabolites were significantly enriched in D-amino acid metabolism and cysteine and methionine metabolism, with glutathione metabolism specifically enriched under cysteine treatment. Integrated omics analysis further uncovered distinct sulfur metabolite-protein regulatory networks under different sulfur nutrition and identified treatment-specific hub proteins. These findings establish a molecular regulatory framework linking SCFM biosynthesis with broader primary metabolic pathways involved in sulfur intermediate generation and regulation, providing new insights into the potential regulatory networks underlying SCFM formation in L. edodes.

Methionine↗
PubMed2003

Profiling core proteomes of human cell lines by one-dimensional PAGE and liquid chromatography-tandem mass spectrometry.

Protein expression profiles vary considerably between human cell lines and tissues, which is in part a reflection of their specialized roles within an organism. It is of considerable practical use to establish which proteins constitute the primary components of the respective proteomes. When compiled into databases, such information can facilitate the assessment of selectivity and specificity of a wide range of proteomic experiments. Here we describe the major constituents of proteomes of six human immortalized cell lines. By employing a combination of one-dimensional SDS-PAGE and nanocapillary liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified up to 1785 non-redundant cytoplasmic and nuclear proteins from a single cell line using 50 and 30 microg of total protein from the corresponding fractions. Up to 38 proteins could be identified from a single band in one liquid chromatography-MS/MS experiment. When combined with systematic gridding of gel lanes into 48 slices, a dynamic range for protein identification of approximately 1:2000 can be envisaged for this approach. Identified proteins range from 4-553 kDa in size, cover the pI range between 3.4 and 12.8, and include 255 proteins with predicted transmembrane domains. Repeated analysis of peptides derived from the same gel band showed that the reproducibility of nanocapillary liquid chromatography-MS/MS of such complex mixtures is about 60-70% suggesting that a particular analytical experiment would need to be repeated about three times to arrive at a representative estimate of the set of highly abundant proteins in a given proteome. Given its technical simplicity, sensitivity, and wealth of generated information, we have adopted this experimental approach to characterize every cell line and tissue that is the subject of experimentation in our laboratory. The combined dataset for the six cell lines consists of 2341 non-redundant human proteins and thus constitutes one of the largest collections of human proteomic data published to date.

Cell Line↗
PubMed2005

Quality control and quality assessment of data from surface-enhanced laser desorption/ionization (SELDI) time-of flight (TOF) mass spectrometry (MS).

BACKGROUND: Proteomic profiling of complex biological mixtures by the ProteinChip technology of surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) is one of the most promising approaches in toxicological, biological, and clinic research. The reliable identification of protein expression patterns and associated protein biomarkers that differentiate disease from health or that distinguish different stages of a disease depends on developing methods for assessing the quality of SELDI-TOF mass spectra. The use of SELDI data for biomarker identification requires application of rigorous procedures to detect and discard low quality spectra prior to data analysis. RESULTS: The systematic variability from plates, chips, and spot positions in SELDI experiments was evaluated using biological and technical replicates. Systematic biases on plates, chips, and spots were not found. The reproducibility of SELDI experiments was demonstrated by examining the resulting low coefficient of variances of five peaks presented in all 144 spectra from quality control samples that were loaded randomly on different spots in the chips of six bioprocessor plates. We developed a method to detect and discard low quality spectra prior to proteomic profiling data analysis, which uses a correlation matrix to measure the similarities among SELDI mass spectra obtained from similar biological samples. Application of the correlation matrix to our SELDI data for liver cancer and liver toxicity study and myeloma-associated lytic bone disease study confirmed this approach as an efficient and reliable method for detecting low quality spectra. CONCLUSION: This report provides evidence that systematic variability between plates, chips, and spots on which the samples were assayed using SELDI based proteomic procedures did not exist. The reproducibility of experiments in our studies was demonstrated to be acceptable and the profiling data for subsequent data analysis are reliable. Correlation matrix was developed as a quality control tool to detect and discard low quality spectra prior to data analysis. It proved to be a reliable method to measure the similarities among SELDI mass spectra and can be used for quality control to decrease noise in proteomic profiling data prior to data analysis.

Female↗