Computational protocol: Human Primary Macrophages Derived In Vitro from Circulating Monocytes Comprise Adherent and Non-Adherent Subsets with Differential Expression of Siglec-1 and CD4 and Permissiveness to HIV-1 Infection

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Protocol publication

[…] RNA was isolated from uninfected and HIV-1 infected MDM using the RNeasy Mini Kit (Qiagen) and the concentration was determined using Nanodrop 2000 (Thermo Scientific). The eluted RNA had a 260/280 of greater than 1.8. The samples were analyzed for quality on an Agilent BioAnalyzer and all samples had a RNA Integrity Number (RIN) value of greater than 9.5. Samples were then prepared for sequencing with the Clontech SMARTer system, indexed, pooled, and sequenced as a single 1 × 50 bp lane on an Illumina HiSeq 3000. RNA-seq reads were demultiplexed and aligned to the Ensembl release 76 top-level assembly with STAR version 2.0.4b. Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.4.5. Sequencing performance was assessed for total number of aligned reads, total number of uniquely aligned reads, and genes detected. The ribosomal fraction (Figure S2 in Supplementary Material), known junction saturation (Figure S3 in Supplementary Material), and read distribution over known gene models (Figure S4 in Supplementary Material) were quantified with RSeQC version 2.3.All gene counts were then imported into the R/Bioconductor package EdgeR and TMM normalization size factors were calculated to adjust for samples for differences in library size. Ribosomal genes and genes not expressed in any sample greater than one count-per-million were excluded from further analysis. The TMM size factors and the matrix of counts were then imported into R/Bioconductor package Limma. Performance of the samples was assessed with a Spearman correlation matrix. Sample outliers with confounding levels of variance found in the correlation plot were removed from further analysis (Figure S5 in Supplementary Material). Weighted likelihoods based on the observed mean-variance relationship of every gene and sample were then calculated for all samples with the voom WithQualityWeights function and gene performance was assessed with plots of residual standard deviation of every gene to their average log-count with a robustly fitted trend line of the residuals (Figure S6 in Supplementary Material). Generalized linear models were then created to test for gene level differential expression and the results were filtered for only those genes with p-values ≤0.05 and log 2 fold-changes greater than an absolute value of 2.For each contrast extracted with Limma, global perturbations in known Gene Ontology (GO) terms and KEGG pathways were detected using the R/Bioconductor packages GAGE to test for changes in expression of the reported log 2 fold-changes reported by Limma in each term versus the background log 2 fold-changes of all genes found outside the respective term. The R/Bioconductor package heatmap3 was used to display heatmaps of genes across samples for each GO terms with a p-value ≤0.05. The R/Bioconductor package Pathview was then used to generate annotated pathway maps on perturbed KEGG signaling and metabolism pathways. The logFC values reported in column B in Table S1 in Supplementary Material are the fold-changes as reported by Limma’s weighted generalized linear model likelihood ratio test for the contrast of adherent and non-adherent MDM’s (–). […]

Pipeline specifications

Software tools Subread, RSeQC, edgeR, limma, voom
Application RNA-seq analysis
Organisms Homo sapiens, Human immunodeficiency virus 1
Diseases Infection, Keratoderma, Palmoplantar, HIV Infections