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Sample GSM4703716

Query DataSets for GSM4703716

Status

Public on Jul 30, 2020

Title

L25

Sample type

SRA

Source name

Lymph node

Organism

Mus musculus

Characteristics

strain: (NZBxNZW)F1
tissue: Lymph node
age: 18 weeks old

Extracted molecule

total RNA

Extraction protocol

Illumina: Total RNA samples were prepared for sequencing using the Qiagen Allprep total RNA kit (Qiagen). Briefly, tissue sections (20-30µg) taken from the middle of the kidneys were lyzed and homogenized in 600 µl Buffer RLT in 2 mL Magna Lyser green beads tubes (Roche Life sciences, Norway) at 600 rpm for 30 sec using the Precellys 24 tissue homogenizer (Bertin Thechnologies, France). The lysates were centrifuged at full speed for 3 min, the supernatant was carefully transferred to the AllPrep spin column, and RNA isolation was performed according to the manufacturer’s protocol. The Isolated RNA were analyzed by Agilent. Only RNA samples of high quality (RNA Integrity number (RIN) ≥8.0) were used.
Ion Torrent: Total RNA of lymph nodes of four and eight weeks, proteinuric mice, immune aggregates, dissected manually under the loupe (magnification:1:10) of protein uric kidney and kidney tissue from the same kidney purified by TRIzol regent (Ambion, Thermo Fisher, Oslo, Norway) according to manufacturer’s instructions. Total RNA of lymph nodes from anti-dsDNA antibody positive mice were isolated using miRNeasy mini kit (Qiagen, Oslo, Norway). The concentration and quality of extracted total RNA were assessed using the AgilentR RNA 6000 nano kit with the AgilentR 2100 BioanalyzerR instrument (Agilent, Matriks AS, Norway). All RNA used in this study had RNA integrity value (RIN value) ≥ 7.
Illumina: RNA libraries were prepared for sequencing using standard Illumina protocols
Ion Torrent: Polyadenylated (poly(A)) mRNA was isolated from total RNA using Dynabeads mRNA Direct Micro Kit (Ambion, Thermo Fisher) followed by its fragmentation using RNase III with an average sizes of 100-200 nt. The fragmented mRNA was cleaned up and determined the yield and size distribution using the AgilentR RNA 6000 Pico kit with the AgilentR 2100 BioanalyzerR instrument. After adapter ligation at both ends of fragmented mRNA, performed reverse transcription, followed by PCR amplification. After purification of amplified cDNA analyzed to assess the yield and size distribution using AgilentR High Sensitivity DNS kit with AgilentR 2100 BioanalyzerR instrument.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Ion Torrent PGM

Description

Ab positive <5 weeks
deseq_out_counts.tab

Data processing

Illumina: Alignments and assignment of reads to genes. Eurofins Genomics performed alignments and assignment of reads to genes. The alignment of reads to a reference sequence was performed using the BWA-backtrack v0.6.2-r126, http:// bio-bwa.sourceforge.net/(Date of last excess 18.06.2019) . Raw read counts were created using HTSeq with python. Reads with unique mapping positions were considered for read counting. Paired-end reads that were mapped to the same reference with about the expected insert size were counted as one read. Paired-end reads that were mapped to different references or with an unexpected insert size were counted as two reads. If only one read of a pair was mapped, it was counted as one read. Only reads overlapping exon-features were counted. All reads mapping to features with the same identifier were summed. The gene-attribute was used as feature identifier. Reads mapping to multiple features with different identifier was ignored for read counting. The mean read length was 100.0 (Eurofins genomics).
Illumina: Filtering and normalization for composition bias. The CPM function from the edgeR library was used to generate the counts per million (cpm) values, the CPM values were further filtered. The ratio of RNA production was estimated by using a weighted trimmed mean of log expression ratios, trimmed mean of M values (TMM). The calcNormFactors function from edgeR package calculated the normalization factors between libraries. These normalization factors are re- scaled by the mean of the normalized library sizes. Normalized read counts were obtained by dividing raw read counts by these re-scaled normalization factors. This was performed to eliminate composition biases between libraries 27. Multidimensional scaling plot (MDS) was generated with the plotMDS function from limma package. Distances between samples was calculated with leading fold change defined as the root-mean-square of the largest 500 log2-fold changes between samples.
Illumina: Differential expression analysis. Differential expression analysis was performed using the edgeR package 28. Differential expression data was filtered to contain FDR<0.05. An empirical Bayes procedure was used to shrink the dispersions towards a consensus value, borrowing information between genes. The results were tested for DE using the GLM likelihood ratio test. The likelihood ratio test was performed by estimating two groups and by comparing the fit of one group to the fit of the other. To deal with multiple tests, individual tests with separate computations were made to test for different contrasts. Three different contrasts were made to test the hypothesis that the different coefficients in each contrast, i.e the three different groups, were equal. The glmLRT function from edgeR was used to conduct likelihood ratio tests for the coefficients in the linear model, like Fishers exact test, and adapted for over dispersed data
Ion Torrent: All sequences were imported and analyzed in CLC Genomics Workbench 8 (CLCbio, Aarhus Denmark). Adaptor sequences were trimmed and we conducted RNA-Seq tool using Mus_musculus GRCm 38.80 as a reference genome by default
Ion Torrent: Filtering and normalization for composition bias. The CPM function from the edgeR library was used to generate the counts per million (cpm) values, the CPM values were further filtered. The ratio of RNA production was estimated by using a weighted trimmed mean of log expression ratios, trimmed mean of M values (TMM). The calcNormFactors function from edgeR package calculated the normalization factors between libraries. These normalization factors are re- scaled by the mean of the normalized library sizes. Normalized read counts were obtained by dividing raw read counts by these re-scaled normalization factors. This was performed to eliminate composition biases between libraries 27. Multidimensional scaling plot (MDS) was generated with the plotMDS function from limma package. Distances between samples was calculated with leading fold change defined as the root-mean-square of the largest 500 log2-fold changes between samples.
Ion Torrent: Differential expression analysis. Differential expression analysis was performed using the edgeR package 28. Differential expression data was filtered to contain FDR<0.05. An empirical Bayes procedure was used to shrink the dispersions towards a consensus value, borrowing information between genes. The results were tested for DE using the GLM likelihood ratio test. The likelihood ratio test was performed by estimating two groups and by comparing the fit of one group to the fit of the other. To deal with multiple tests, individual tests with separate computations were made to test for different contrasts. Three different contrasts were made to test the hypothesis that the different coefficients in each contrast, i.e the three different groups, were equal. The glmLRT function from edgeR was used to conduct likelihood ratio tests for the coefficients in the linear model, like Fishers exact test, and adapted for over dispersed data
Genome_build: mm9
Supplementary_files_format_and_content: GEN141016_F Profiling table.xlsx
Supplementary_files_format_and_content: Differential expressed TLS_kidney_LN genes.xlsx

Submission date

Jul 29, 2020

Last update date

Jul 30, 2020

Contact name

Kristin Andreassen Fenton

E-mail(s)

kristin.fenton@uit.no

Phone

90775154

Organization name

UiT Norges arktiske universitet