Nsolver 4

Manufactured by NanoString

Sourced in United States

NSolver 4.0 is a software package designed for the analysis of data generated by NanoString's nCounter Analysis System. The software provides a comprehensive set of tools for the processing, normalization, and statistical analysis of gene expression data.

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Lab products found in correlation

Ncounter sprint profiler, by NanoString (8 mentions) Ncounter analysis system, by NanoString (6 mentions) Ncounter human immunology v2 panel, by NanoString (5 mentions) Ncounter sprint, by NanoString (4 mentions) Advanced analysis 2, by NanoString (4 mentions) Rneasy kit, by Qiagen (4 mentions) Ncounter digital analyzer, by NanoString (4 mentions) Ncounter myeloid innate immunity panel, by NanoString (3 mentions) Ncounter flex system, by NanoString (3 mentions) Nanodrop nd 1000, by Thermo Fisher Scientific (3 mentions) Rneasy mini kit, by Qiagen (3 mentions) Ds 11 spectrophotometer, by DeNovix (3 mentions) Ncounter platform, by NanoString (3 mentions) Qubit 3.0 fluorometer, by Thermo Fisher Scientific (3 mentions) Pancancer immune profiling panel, by NanoString (3 mentions) Ncounter technology, by NanoString (3 mentions) Prism software, by GraphPad (3 mentions) Ncounter human v3 mirna panel, by NanoString (2 mentions) Nsolveradvanced analysis 2, by NanoString (2 mentions) Microsoft excel office for mac version 14, by Qiagen (2 mentions)

Close spelling variants of this product

NSolver Analysis Software 4.0 (30 citations) NSolver Analysis Software version 4.0 (26 citations) NSolver 4.0 Analysis Software (22 citations) NSolver version 4.0 (13 citations) NSolver software 4.0 (11 citations) NSolver Software version 4.0 (7 citations) NCounter nSolver 4.0 (3 citations) NSolver software platform 4.0 (3 citations) NCounter Analysis System –with nSolver 4.0 software (2 citations) NSolver V.4.0 analysis software (2 citations)

102 protocols using nsolver 4

Comprehensive NR Gene Expression Profiling

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To take an unbiased approach to assess NR expression, we generated a comprehensive list of NR genes, adapting from Iwama and Gojobori 2002 (link) (Iwama and Gojobori, 2002 (link)), and including additional genes identified through further literature review. Working with representatives from NanoString, this gene list was developed into a set of 179 NR targeting probes and 6 probes targeting housekeeping genes (see Supplementary Table 3 for gene list and probe details). Our probeset (available for purchase under the label CodeSet NR-Mm-20113) arrived as part of the NanoString nCounter Gene Expression Assay kit, which includes all necessary reagents. To prepare a NanoString reaction, briefly, the provided reporter probes were diluted in hybridization buffer and added to the provided 12 well plate. ~100 ng of isolated RNA per sample was applied to the probes and heated to 65°C. The capture probes were then added immediately before beginning hybridization for 12 hours. Resulting expression data were analyzed using nSolver 4.0 software (NanoString). Background thresholding was applied using the mean of the negative control wells.

Rosenberg K.M, & Singh N.J. (2019). Mouse T cells express a neurotransmitter-receptor signature that is quantitatively modulated in a subset and activation dependent manner. Brain, behavior, and immunity, 80, 275-285.

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Comprehensive Gene Expression Profiling

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A corrected input of 50 ng of RNA from each sample was hybridized with the nCounter® CodeSet using the nCounter® MAX analysis system. Analysis of the expression profiles for more than 700 genes was performed using the nanoString nCounter® PanCancer Pathways Panel and analyzed with the accompanying nSolver™ 4.0 software as previously reported (nanoString Technologies, Seattle, WA, USA).^{13 ,14}

Monibi F.A., Pannellini T., Croen B., Otero M., Warren R, & Rodeo S.A. (2021). Targeted Transcriptomic Analyses of RNA Isolated from Formalin-Fixed and Paraffin-Embedded Human Menisci. Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 40(5), 1104-1112.

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RNA Quantification by NanoString nCounter

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RNA was extracted from HCT116 and TRAMR cells using an RNAse Easy kit (QIAGEN, Hilden, Germany) as per the manufacturers protocol, normalised and equal amounts of the purified RNA, 100 ng were used as input for amplification‐free RNA quantification by the NanoString nCounter Analysis System with the Human PanCancer pathways panels as previously described [37 (link)]. Raw counts were normalised to the internal positive controls and housekeeping genes, using the nsolver 4.0 software (NanoString, Seattle, WA, USA).

Dawson J.C., Munro A., Macleod K., Muir M., Timpson P., Williams R.J., Frame M., Brunton V.G, & Carragher N.O. (2021). Pathway profiling of a novel SRC inhibitor, AZD0424, in combination with MEK inhibitors for cancer treatment. Molecular Oncology, 16(5), 1072-1090.

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Multiplexed NanoString miRNA Profiling

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The multiplexed NanoString nCounter Human v3 miRNA expression assay (NanoString Technologies, Seattle, WA, USA) was used to profile 800 human miRNAs of the discovery cohort. Sample preparation involved a multiplexed annealing of the specific tags to their target miRNA, a ligation reaction, and an enzymatic purification to remove the unligated tags, according to manufacturer’s protocol. The product was then diluted, denatured, combined with Reporter and Capture CodeSet, and hybridized to the Target-Probe Complex. Complexes were then immobilized on the cartridge for data collection. Digital images were processed and the barcode counts were tabulated in a comma separated value (CSV) format.
MiRNA raw data were subjected to a first quality control evaluation and underwent background subtraction and normalization using the five most stable miRNAs [62 (link)] for total plasma and EVs, respectively, using the nSolver 4.0 Software (NanoString Technologies inc., Seattle, WA, USA). Normalized RCC files were then loaded and analyzed by Rosalind nCounter Data Analysis Software (San Diego, CA, USA). The resulted DE miRNAs were filtered using p-value < 0.05 and ±1.5-Fold change (FC), as threshold of significance.

Filardi T., Catanzaro G., Grieco G.E., Splendiani E., Trocchianesi S., Santangelo C., Brunelli R., Guarino E., Sebastiani G., Dotta F., Morano S, & Ferretti E. (2022). Identification and Validation of miR-222-3p and miR-409-3p as Plasma Biomarkers in Gestational Diabetes Mellitus Sharing Validated Target Genes Involved in Metabolic Homeostasis. International Journal of Molecular Sciences, 23(8), 4276.

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Comparative Analysis of Aptamer vs. Antibody-based Cell Isolation

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Thawed cell pellets were resuspended in RLT lysis buffer with β-mercaptoethanol at 3,500 cells/μL, and overnight hybridization reactions with the nCounter Immunology Panel (Human V2) Reporter CodeSet and Capture ProbeSet were ran according to the manufacturer’s instructions. Samples were run on the nCounter SPRINT Profiler (NanoString), and mRNA counts were normalized in groups by day and cell type (D0, S1R1D14 mocks, S1R1D14 CD19 CAR) using nSolver 4.0 software (NanoString) and the Advanced Analysis 2.0 software (NanoString), which selects the housekeeping genes that minimize the pairwise variation statistic. Each group has 6 samples, 3 biological replicates for antibody-based isolation and 3 biological replicates for aptamer-based isolation. Using Excel (Microsoft), mRNA probes that gave normalized counts less than 25 for more than 50% of the samples in a group (i.e. 4 or more samples) were removed from the analysis due to being mostly below background. The unadjusted P-values of the LOG₂ fold changes in the probe counts of aptamer-isolated cells over antibody-isolated cells were determined using a paired two-tailed t-test in Excel, and the threshold for significance was calculated using the Benjamini-Yekutieli multiple-testing correction in R software.

Kacherovsky N., Cardle I.I., Cheng E.L., Yu J.L., Baldwin M.L., Salipante S.J., Jensen M.C, & Pun S.H. (2019). Traceless aptamer-mediated isolation of CD8+ T cells for CAR-T cell therapy. Nature biomedical engineering, 3(10), 783-795.

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Immune Profiling of FFPE Samples

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Total RNA was isolated from archival FFPE blocks using RNeasy Mini Kit (Qiagen) and the expression of 730 target genes was evaluated with nCounter PanCancer Immune Profiling Panel (NanoString Technologies), as reported previously^{42 (link)}.
For each analysed sample, background correction and normalization against global mean were performed as described^{42 (link)}, using nSolver 4.0 software (NanoString Technologies). PT and LNM samples were normalized together. In brief, the background level was estimated by thresholding over the mean plus 2 standard deviations of the negative control counts. Subsequently, the data were normalized according to the global mean of the counts of positive controls and 4 most stably expressed housekeeping genes—ABCF1, EDC3, HDAC3, and CNOT4. The negative and positive control probes were included in the assay. Following normalization, low-expression genes (log2 mean count in all samples < 6) were excluded, leaving 593 target genes for analysis.

Popeda M., Markiewicz A., Stokowy T., Szade J., Niemira M., Kretowski A., Bednarz-Knoll N, & Zaczek A.J. (2021). Reduced expression of innate immunity-related genes in lymph node metastases of luminal breast cancer patients. Scientific Reports, 11, 5097.

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Subtyping of Breast Cancer Samples

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FFPE sections with at least 50% tumor content were used for isolation. As previously described, RNA was isolated with Maxwell Promega RNA purification kits (Promega). The mRNA expression of target genes was determined via nCounter^® MAX/FLEX system (NanoString Technologies^®, USA). To differentiate luminal, basal and double-negative phenotypes, a customized 21-gene-containing nCounter^® PlexSet^™ (NanoString Technologies^®) according to the MD Anderson Cancer Center (MDA) subtyping approach was applied as described previously [5 (link), 17 (link)]. Selected genes are summarized in Supplementary Table 2. Gene counts were normalized using two reference genes (SDHA, HPRT1) and log2-transformed for further analysis using the nSolver 4.0 software (NanoString).

Weyerer V., Stoehr R., Bertz S., Lange F., Geppert C.I., Wach S., Taubert H., Sikic D., Wullich B., Hartmann A, & Eckstein M. (2021). Prognostic impact of molecular muscle-invasive bladder cancer subtyping approaches and correlations with variant histology in a population-based mono-institutional cystectomy cohort. World Journal of Urology, 39(11), 4011-4019.

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Custom Nanostring Codeset Design for Salmon

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Custom nanostring codesets were designed by Nanostring Technologies Inc. using the Atlantic Salmon reference genome (Cigene), accession numbers and target sequences are shown in S2 Table. Codesets were processed by the Univerisity of Manchester Genomic Technologies Core Facility. This technology is based on the use of fluorescent barcoded probes which bind specifically to the target molecule. Importantly these barcodes should only bind one at a time to each target molecule, therefore the number of fluorescent barcodes reflects the number of RNA molecules of your target gene. A spike control with a known number of RNA molecules was also used to normalise across samples and runs. Therefore the units are counts normalised to spike-in positive controls. Data was processed using nSolver 4.0 software (Nanostring). Data can be accessed on GEO under the project identifier GSE146530.

West A.C., Iversen M., Jørgensen E.H., Sandve S.R., Hazlerigg D.G, & Wood S.H. (2020). Diversified regulation of circadian clock gene expression following whole genome duplication. PLoS Genetics, 16(10), e1009097.

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Differential Gene Expression Analysis

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Gene expression was measured on a commercially available gene panel (PanCancer Human IO 360) containing a total of 770 unique genes (NanoString Technologies, Seattle, WA, USA). Raw data were normalized using nSolver 4.0 software (NanoString Technologies), based on the geometric mean of the negative controls, internal housekeeping genes, and positive controls. Normalized counts from genes were log2-transformed and used for further analysis. Genes that were changed up or down by at least 50% with false discovery rate (FDR)-adjusted p < 0.05 were considered differentially expressed. We performed gene ontology (GO) analyses using the ToppGene Suite online tool (https://toppgene.cchmc.org/, accessed on 9 June 2022).

Kim S.S., Doherty C., Moghe M., Rait A., Pirollo K.F., Harford J.B, & Chang E.H. (2022). Nanomedicine-Based Gene Delivery for a Truncated Tumor Suppressor RB94 Promotes Lung Cancer Immunity. Cancers, 14(20), 5092.

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Myeloid Innate Immunity Gene Expression

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Gene expression values from stimulated BMDM cultures were measured using the nCounter® Myeloid Innate Immunity Panel (NanoString Technology), which analyzes 770 genes occurring in 19 different pathways and processes them across seven different myeloid cell types. The samples were tested using an nCounter Analysis System (NanoString Technologies). Raw data were processed and checked for quality using the R/Bioconductor NanoStringQCPro software package⁷². Expression values were normalized to the geometric mean of housekeeping genes and log2-transformed using nSolver 4.0 software (NanoString Technologies). False Discovery Rates for ratio data were calculated from the p-values returned by the t-tests using the Benjamini-Yekutieli method.

Zhang F., Parayath N.N., Ene C.I., Stephan S.B., Koehne A.L., Coon M.E., Holland E.C, & Stephan M.T. (2019). Genetic programming of macrophages to perform anti-tumor functions using targeted mRNA nanocarriers. Nature Communications, 10, 3974.

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