From SOMAmer-based biomarker discovery to diagnostic and clinical applications: a SOMAmer-based, streamlined multiplex proteomic assay.
Ontology highlight
ABSTRACT:
SUBMITTER: Kraemer S
PROVIDER: S-EPMC3195687 | biostudies-literature | 2011
REPOSITORIES: biostudies-literature
Publications
From SOMAmer-based biomarker discovery to diagnostic and clinical applications: a SOMAmer-based, streamlined multiplex proteomic assay.
Kraemer Stephan S Vaught Jonathan D JD Bock Christopher C Gold Larry L Katilius Evaldas E Keeney Tracy R TR Kim Nancy N Saccomano Nicholas A NA Wilcox Sheri K SK Zichi Dom D Sanders Glenn M GM
PloS one 20111017 10
Recently, we reported a SOMAmer-based, highly multiplexed assay for the purpose of biomarker identification. To enable seamless transition from highly multiplexed biomarker discovery assays to a format suitable and convenient for diagnostic and life-science applications, we developed a streamlined, plate-based version of the assay. The plate-based version of the assay is robust, sensitive (sub-picomolar), rapid, can be highly multiplexed (upwards of 60 analytes), and fully automated. We demonstr ...[more]
Similar Datasets
Project description:The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high-throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two-stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.
| S-EPMC2753590 | biostudies-literature
Project description:Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.
| S-EPMC3292708 | biostudies-literature
Project description:BackgroundThe interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.Methodology/principal findingsWe present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.Conclusions/significanceWe describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.
| S-EPMC3000457 | biostudies-literature
Project description:The recent advance in targeted label-free proteomics, SWATH-MS, can provide consistent protein detection and reproducible protein quantitation, which is a considerable advantage for biomarker study of urinary extracellular vesicles. We developed a SWATH-MS workflow with a curated spectral library of 1,145 targets. Application of the workflow across nine replicates of three sample types (exosome-like vesicles (ELVs), microvesicles (MVs) and urine proteins (UPs)) resulted in the quantitation of 888 proteins at FDR <1%. The median-coefficient of variation of the 888 proteins in the ELV sample was 7.7%, indicating excellent reproducibility. Data analysis showed common exosome markers, (i.e. CD9, CD63, ALIX, TSG101 and HSP70) were enriched in urinary ELVs as compared to MVs and UPs. The use of a multiplex biomarker screening assay focused on ELVs was investigated, and perspectives in future applications are discussed.
| S-EPMC6177456 | biostudies-literature
Project description:Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups—healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.
| S-EPMC9694769 | biostudies-literature
Project description:ObjectiveSurvival in epithelial ovarian cancer (EOC) remains poor. Most patients are diagnosed in late stages. Early diagnosis increases the chance of survival. We used the proximity extension assay from Olink Proteomics to search for new protein biomarkers with the potential to improve the diagnostic performance of CA125 and HE4 in patients with ovarian tumors.Material and methodsPlasma samples were obtained from 180 women with ovarian tumors; 30 cases of benign tumor, 28 cases with borderline tumors, 25 early EOC cases (FIGO stage I) and 97 advanced EOC cases (FIGO stages II-IV). Proteins were measured using the Olink® Oncology II and Inflammation panels. For statistical analyses, patients were categorized into benign tumors versus cancer and benign tumors versus borderline + cancer, respectively.ResultsWe analyzed 177 biomarkers. Thirty-four proteins had ROC AUC > 0.7 for discrimination between benign tumors and cancer. Fifteen proteins had ROC AUC > 0.7 for discrimination between benign tumors and borderline tumors + cancer. HE4 ranked highest for both comparisons. A reference model with HE4, CA125 and age (AUC 0.838 for benign tumors vs. cancer and AUC 0.770 for benign tumors vs. borderline tumors + cancer) was compared to the reference model with the addition of each of the remaining proteins with AUC > 0.7. ITGAV was the only individual biomarker found to improve diagnostic performance of the reference model, to AUC 0.874 for benign tumors vs. cancer and AUC 0.818 for benign tumors vs. borderline tumors + cancer (p < 0.05). Cross-validation and LASSO regression was combined to select multiple biomarker combinations. The best performing model for discrimination between benign tumors and borderline tumors + cancer was a 6-biomarker combination (HE4, CA125, ITGAV, CXCL1, CEACAM1, IL-10RB) and age (AUC 0.868, sensitivity 0.86 and specificity 0.82, p = 0.016 for comparison with the reference model).ConclusionHE4 was the best performing individual biomarker for discrimination between benign ovarian tumors and EOC including borderline tumors. The addition of other carcinogenesis-related biomarkers in a multiplex biomarker panel can improve the diagnostic performance of the established biomarkers HE4 and CA125.
| S-EPMC7571712 | biostudies-literature
Project description:Interest in biomarker patterns and disease has led to the development of immunoassays that evaluate multiple analytes in parallel while using little sample. However, there are no current standards for multiplex configuration, validation, and quality. Thus, validation by platform, population, and question of interest is recommended. We sought to determine the best blood fraction for multiplex evaluation of circulating biomarkers in post-menopausal women, and to explore body composition phenotype discrimination by biomarkers.Archived serum and plasma samples from a sample of healthy post-menopausal women with the highest (n=9) and lowest (n=11) percent lean mass, as determined by dual-energy X-ray absorptiometry, were used to measure 90 analytes using bead-based, suspension multiplex assays. Replicates of serum and plasma were analyzed in a random selection of four of these individuals.Ninety percent of the analytes were detectable for ? 50% of samples; when limited to these well detected analytes, mean replicate correlations for serum and plasma were 0.87 and 0.85, respectively. Serum had lower error rates discriminating phenotypes; seven serum vs. two plasma analytes discriminated extreme body phenotypes.Serum and plasma performed similarly for the majority of the analytes. Serum showed a slight advantage in predicting extreme body composition phenotypes in postmenopausal women using parallel evaluation of analytes.
| S-EPMC3749089 | biostudies-literature
Project description:BackgroundSmall extracellular vesicles (small-EVs) are membranous vesicles that contain unique information regarding the condition of cells and contribute to the recruitment and reprogramming of components associated with the tumor environment. Therefore, many researchers have suggested that small-EV proteins are potential biomarkers for diseases such as cancer. Colon cancer (CC) is one of the most common causes of cancer-related deaths worldwide. Biomarkers such as carcinoembryonic antigen (CEA) show low sensitivity (~?40%), and thus the demand for novel biomarkers for CC diagnosis is increasing.MethodsIn this study, we identified biomarkers for diagnosing CC through proteomic analysis of small-EVs from CC cell lines. These small-EVs were characterized by western blot analysis, nanoparticle tracking analysis, and transmission electron microscopy and analyzed using mass spectrometry.ResultsFive selected proteins were found to be upregulated in CC by western blot analysis. Among the candidate proteins, tetraspanin 1 (TSPAN1) was found to be upregulated in plasma EVs from CC patients compared to those from healthy controls (HCs) with 75.7% sensitivity.ConclusionsThese results suggest that TSPAN1 is a potent non-invasive biomarker for CC detection. Our experimental strategy provides useful insights into the identification of cancer-specific non-invasive biomarkers.
| S-EPMC6211419 | biostudies-literature
Project description:Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.
| S-EPMC3914447 | biostudies-other
Project description:Alcoholic liver disease (ALD) progresses from steatosis to alcoholic hepatitis to fibrosis and cirrhosis. Liver biopsy is considered as the gold standard method for diagnosis of liver cirrhosis and provides useful information about damaging process which is an invasive procedure with complications. Existing biomarkers in clinical practice have narrow applicability due to lack of specificity and lack of sensitivity. The objective of this article is to identify proteomic biomarker candidates for alcoholic liver cirrhosis by differential expression analysis between alcoholic liver cirrhotic and healthy subjects. Blood samples were collected from 20 subjects (10 alcoholic liver cirrhosis and 10 healthy) from R. L. Jalapa Hospital and Research Centre, Kolar, Karnataka, India. Differential protein analysis was carried out by two-dimensional electrophoresis after albumin depletion, followed by liquid chromatography-mass spectrometry. The image analysis found 46 spots in cirrhotic gel and 69 spots in healthy gel, of which 14 spots were identified with significant altered expression levels. Based on the protein score and clinical significance, among 14 spots, a total of 28 protein biomarker candidates were identified: 13 with increased expression and 15 with decreased expression were categorized in alcoholic liver cirrhosis compared to healthy subjects. Protein biomarker candidates identified by "-omics" approach based on differential expression between alcoholic liver cirrhotic subjects and healthy subjects may give better insights for diagnosis of ALD. Prioritization of candidates identified is a prerequisite for validation regimen. Biomarker candidates require verification that demonstrates the differential expression will remain detectable by assay to be used for validation.
| S-EPMC6050617 | biostudies-other