NCounter (NanoString) expression analysis of CD19+CD27-IgD- and CD19+CD27+IgD- B cells cultured with plasmacytoid dendritic cells and RNA containing immune complexes in vitro for 6 days
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Maija-Leena Eloranta
PROVIDER: E-MTAB-5740 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:SummaryThe NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise.Availability and implementationThe NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9805552 | biostudies-literature
Project description:The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.
| S-EPMC6614807 | biostudies-literature
Project description:The NanoString nCounter System has been widely used in basic science and translational science research for the past decade. The System consists of two instruments: the PrepStation and the Digital Analyzer, and both have been continuously improved with evolving technologies. A great amount of research data have been generated at multiple research laboratories with the employment of different generations of the System. With the need of integrating multiple datasets, researchers are interested to know whether signals are comparable between different generations of the System. Toward this end, we designed a profiling study to compare performance of two generations of the NanoString nCounter System using a common set of biological samples. Using graphical tools and statistical models, we found that two different generations of NanoString nCounter System produced equivalent signals and signal deviations are in the range of random background noises for the medium-high expression levels.
| S-EPMC6874068 | biostudies-literature
Project description:Immunocompromised patients are considered high-risk and prioritized for vaccination against COVID-19. We aimed to analyze B-cell subsets in these patients to identify potential predictors of humoral vaccination response. Patients (n=120) suffering from hematologic malignancies or other causes of immunodeficiency and healthy controls (n=79) received a full vaccination series with an mRNA vaccine. B-cell subsets were analyzed prior to vaccination. Two independent anti-SARS-CoV-2 immunoassays targeting the receptor-binding domain (RBD) or trimeric S protein (TSP) were performed three to four weeks after the second vaccination. Seroconversion occurred in 100% of healthy controls, in contrast to 67% (RBD) and 82% (TSP) of immunocompromised patients, while only 32% (RBD) and 22% (TSP) achieved antibody levels comparable to those of healthy controls. The number of circulating CD19+IgD+CD27- naïve B cells was strongly associated with antibody levels (ρ=0.761, P<0.001) and the only independent predictor for achieving antibody levels comparable to healthy controls (OR 1.07 per 10-µL increase, 95%CI 1.02-1.12, P=0.009). Receiver operating characteristic analysis identified a cut-off at ≥61 naïve B cells per µl to discriminate between patients with and without an optimal antibody response. Consequently, measuring of naïve B cells in immunocompromised hematologic patients could be useful in predicting their humoral vaccination response.
| S-EPMC8688243 | biostudies-literature
Project description:Diagnostic histopathology of soft tissue tumors can be troublesome as many entities are quite rare and have overlapping morphologic features. Many soft tissue tumors harbor tumor-defining gene translocations, which may provide an important ancillary tool for tumor diagnosis. The NanoString nCounter platform enables multiplex detection of pre-defined gene fusion transcripts in formalin-fixed and paraffin-embedded tissue. A cohort of 104 soft tissue tumors representing 20 different histological types was analyzed for the expression of 174 unique gene fusion transcripts. A tumor-defining gene fusion transcript was detected in 60 cases (58%). Sensitivity and specificity of the NanoString assay calculated against the result of an alternative molecular method were 85% and 100%, respectively. Highest diagnostic coverage was obtained for Ewing sarcoma, synovial sarcoma, myxoid liposarcoma, alveolar rhabdomyosarcoma, and desmoplastic small round cell tumor. For these tumor types, the NanoString assay is a rapid, cost-effective, sensitive, and specific ancillary screening tool for molecular diagnosis. For other sarcomas, additional molecular testing may be required when a translocation transcript is not identified with the current 174 gene fusion panel.
| S-EPMC7079105 | biostudies-literature
Project description:SummaryThe NanoStringTM nCounter® is a platform for the targeted quantification of expression data in biofluids and tissues. While software by the manufacturer is available in addition to third parties packages, they do not provide a complete quality control (QC) pipeline. Here, we present NACHO ('NAnostring quality Control dasHbOard'), a comprehensive QC R-package. The package consists of three subsequent steps: summarize, visualize and normalize. The summarize function collects all the relevant data and stores it in a tidy format, the visualize function initiates a dashboard with plots of the relevant QC outcomes. It contains QC metrics that are measured by default by the manufacturer, but also calculates other insightful measures, including the scaling factors that are needed in the normalization step. In this normalization step, different normalization methods can be chosen to optimally preprocess data. Together, NACHO is a comprehensive method that optimizes insight and preprocessing of nCounter® data.Availability and implementationNACHO is available as an R-package on CRAN and the development version on GitHub https://github.com/mcanouil/NACHO.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9883715 | biostudies-literature
Project description:MotivationNon-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency.ResultsWe propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency.Availability and implementationR Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.
| S-EPMC10826904 | biostudies-literature
Project description:BackgroundDespite genomic simplicity, recent studies have reported at least 3 major atypical teratoid rhabdoid tumor (ATRT) subgroups with distinct molecular and clinical features. Reliable ATRT subgrouping in clinical settings remains challenging due to a lack of suitable biological markers, sample rarity, and the relatively high cost of conventional subgrouping methods. This study aimed to develop a reliable ATRT molecular stratification method to implement in clinical settings.MethodsWe have developed an ATRT subgroup predictor assay using a custom genes panel for the NanoString nCounter System and a flexible machine learning classifier package. Seventy-one ATRT primary tumors with matching gene expression array and NanoString data were used to construct a multi-algorithms ensemble classifier. Additional validation was performed using an independent gene expression array against the independently generated dataset. We also analyzed 11 extra-cranial rhabdoid tumors with our classifier and compared our approach against DNA methylation classification to evaluate the result consistency with existing methods.ResultsWe have demonstrated that our novel ensemble classifier has an overall average of 93.6% accuracy in the validation dataset, and a striking 98.9% accuracy was achieved with the high-prediction score samples. Using our classifier, all analyzed extra-cranial rhabdoid tumors are classified as MYC subgroups. Compared with the DNA methylation classification, the results show high agreement, with 84.5% concordance and up to 95.8% concordance for high-confidence predictions.ConclusionsHere we present a rapid, cost-effective, and accurate ATRT subgrouping assay applicable for clinical use.
| S-EPMC10825849 | biostudies-literature
Project description:RNA microarray profiling analysis was performed on splenic B cell subsets: “IgD+CD27-” (naive B cells) and “IgD+CD27+” (MZB B cells) isolated from splenic samples of 6 adults
2021-09-23 | E-MTAB-9620 | biostudies-arrayexpress
Project description:Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (prediction analysis of microarray 50 gene set, PAM50). The present study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction (SSP)) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair κ statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, human epidermal growth factor receptor II (HER2)-enriched, luminal A and luminal B subtypes predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It is not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross-platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.
| S-EPMC8385191 | biostudies-literature