The global microRNA profile was obtained using NanoString human microarrays (human V2 miRNA array >800 probes, Nanostring Technologies, Seattle, WA). To account for differences in hybridization and purification, data were normalized to the average counts for all control spikes in each sample using proprietary bioinformatics software (nSolver Analysis Software 2.5, Nanostring Technologies, Seattle, WA). Briefly, we calculated a background level of expression for each sample using the mean level of the negative controls plus 2 SD of the mean. MiRNAs expressing less than 2 SD from the mean were set to 0 expression. Those miRNAs that were considered non-zero expression, were normalized using a scaling factor based on the top 100 expressing miRNAs across all samples. For each sample, the average of the geometric means of the top 100 expressing miRNAs across all samples was divided by the geometric mean of each sample (http://www.NanoString.com/media/pdf/MAN_nCounter_Gene_Expression_Data_Analysis_Guidelines.pdf ).
Nsolver analysis software 2
Manufactured by NanoString
NSolver Analysis Software 2.5 is a data analysis software tool designed for use with NanoString's gene expression analysis platforms. The software provides core functions for processing, visualizing, and interpreting data generated from NanoString's nCounter Analysis System.
Automatically generated - may contain errors
4 protocols using nsolver analysis software 2
1
Profiling miRNA Expression Patterns
2
Normalization Techniques for NanoString Gene Expression
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Using the proprietary software for the instrument (nSolver analysis software 2.5, NanoString), all samples were subjected to technical normalization to the positive spike-in RNA (present in the CodeSet). A lane-specific value representative of positive control counts was calculated, that is, a sum of positive control counts. The geometric means of these calculated values across all lanes were used as the references against which each lane was normalized. A scaling factor was then calculated for each of the lanes based on the calculated value for the positive controls in each lane relative to the average of this value for the positive controls across all lanes. This normalization factor was used to adjust the counts for each gene target and negative controls in the associated lane. Data were further normalized to a set of selected housekeeping genes (Actb, B2m, Gapdh, Hprt, Gusb, Ppia, and Rps18). The geometric mean of the count for these genes across all lanes was used as the reference against which each lane was normalized. A scaling factor was then calculated for each of the lanes based on the calculated value for the housekeeping genes in each lane relative to the geometric average of this value for the house keeping genes across all lanes.
3
Extracellular Vesicle RNA Profiling
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The RNA contained in extracellular vesicles/exosomes was isolated and purified using a phenol-free lysis buffer and rapid spin columns (SeraMir kit System Biosciences, Mountain View, CA). We performed RNA separation, detection and quantitation with the Agilent Small RNA Kit and a Bioanalyzer instrument (2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA). The global microRNAs (miRs) profile was obtained using NanoString human microarrays (human V2 miRNA array >800 probes, Nanostring Technologies, Seattle, WA). To account for differences in hybridization and purification, data were normalized to the average counts for all control spikes in each sample using a proprietary bioinformatics software (nSolver™ Analysis Software 2.5, Nanostring Technologies, Seattle, WA). Briefly, we calculated a background level of expression for each sample using the mean level of the negative controls plus two standard deviations of the mean. MiRNA expressing less than two standard deviations from the mean were set to 0 expression. Those miRNAs that were considered non-zero expression, were normalized using a scaling factor based on the top 100 expressing miRNAs across all samples. For each sample, the average of the geometric means of the top 100 expressing miRNAs across all samples was divided by the geometric mean of each sample.[17 ]
4
Isolation and Analysis of Foxp3+ Tregs
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Foxp3+ Tregs were isolated from spleen and lymph nodes of naive and nephritic FIR-tiger and FIR-tiger-PD-L1−/− mice. Therefore, CD4+ T cells were enriched from single cell suspensions by using the CD4+ T Cell Isolation Kit (Miltenyi Biotec). Thereafter, CD4+ Foxp3+ (mRFP+) Tregs were purely isolated by FACS. RNA of 1–2 × 106 Tregs was isolated by the RNeasy Mini Kit (Quiagen) and digested with RNase-free DNase I (Quiagen) according to the manufacturer’s instructions. Gene expression analysis was performed using the NanoString assay with the nCounter Immunology Panel profiling 561 immunology-related genes ( NanoString Technologies, Hamburg, Germany) according to the manufacturer’s instructions. The results were analyzed with the nSolver Analysis Software 2.5 (NanoString Technologies). Data were calculated in x-fold changes compared to the corresponding control mRNA and displayed as heat map using the web server Heatmapper48 (link). Clustering was done using the average linkage and the Pearson distance.
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