RNA-seq Analysis
Altuna Akalin in Computational Genomics with R, 2020
An MA plot is useful to observe if the data normalization worked well (Figure 8.6). The MA plot is a scatter plot where the x-axis denotes the average of normalized counts across samples and the y-axis denotes the log fold change in the given contrast. Most points are expected to be on the horizontal 0 line (most genes are not expected to be differentially expressed).
LILRB4 promotes tumor metastasis by regulating MDSCs and inhibiting miR-1 family miRNAs
Published in OncoImmunology, 2022
Mei-Tzu Su, Sakiko Kumata, Shota Endo, Yoshinori Okada, Toshiyuki Takai
Approximately 14–20 million reads were obtained per library. RNA-seq raw reads were quality checked by FastQC (version 0.11.9), and were then pre-processed by filtering out sequencing adapters, short-fragment reads (<10 nt), and other low-quality reads. Unique Molecular Identifier analysis was performed on GeneGlobe data analysis center (Qiagen), and the processed reads was mapped to the mouse [Mus musculus, GRCm38 (mm10) assembly] reference genome. The mapped reads were normalized using Strand NGS with the Trimmed Mean of M value (TMM) method. Normalized reads containing miRNA sequences were annotated compliantly with existing sequences in the miRBase database (http://www.mirbase.org/). The fold-change was calculated from the normalized reads, and an MA plot showing base-2 log fold-change (Log2[gp49B−/−]-Log2[WT]) along the y-axis and normalized average expression of each miRNA ({Log2[gp49B−/−] +Log2[WT]}/2) along the x-axis was generated using GraphPad Prism version 10 software. Additionally, base-2 log fold-change data from two independent miRNA sequencing were analyzed with a comparison analysis using Ingenuity Pathway Analysis tools (Qiagen).
Characterization of DNA hydroxymethylation profile in cervical cancer
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2019
Jing Wang, Yi Su, Yongju Tian, Yan Ding, Xiuli Wang
Next, genomic DNA from four CSCC tissues of two I–IIa and two IIb–IV stage as well as two cervicitis were isolated and conducted 5mC and 5hmC immunoprecipitation approach combined with deep sequencing to map genome-wide 5mC and 5hmC profiles in cervical cancers. Approximately, 29.6 M reads of 5mC and 28.8 M reads of 5hmC sequencing data were collected, respectively (Table 1). To compare the individuals in cervical cancer and control groups, 5mC and 5hmC densities were normalized. Also, MA plot for a genome-wide comparison of 5mC/5hmC levels between IIb and IV stage CSCC and cervicitis groups displayed that most of the scatter plots are symmetrically distributed at the two sides of the 0 axis, which means that 5mC/5hmC sites were similar between the groups. Thus, no significant genome-wide differences in the 5mC/5hmC densities between the CSCC and cervicitis groups were observed (Figure 2(A,B)). After calling peaks by MACS software (p < .01), we mapped the (h)MeDIP-seq signals of 5mC and 5hmC peaks according to their genomic location and we did observe that 5mC was highly enriched at gene body and TES more than promoter regions (Figure 2(C)), while 5hmC was majorly enriched at the promoter and TSS regions (Figure 2(D)). The variation trend of the 5mC/5hmC levels had no significant difference among the three groups.
B cells require licensing by dendritic cells to serve as primary antigen-presenting cells for plasmid DNA
Published in OncoImmunology, 2023
Ichwaku Rastogi, Douglas G. McNeel
Next, we wished to understand the changes occurring in B cells at the gene expression level, following their interaction with DCs. DNA-loaded B cells were cultured for 3 days with DCs and CD8 T cells and then separated into individual populations by flow cytometry. B cells were then analyzed by RNAseq. Upon principal component analysis, the biological replicates demonstrated minimal variance, however large variation was observed between B cells cultured with DCs and those not cultured with DC (Figure 6a). This was indicative of vastly different gene expression signatures. This was confirmed by MA plot showing log fold change (M) of each gene plotted against its mean average intensity/expression (A) (Figure 6b). Similar analysis was also performed to analyze the gene expression variation in DCs before and after co-culture with DCs. PC plots and MA plots of the analysis showed that like B cells, DCs also demonstrated significant gene expression changes (6A-B). There were 6845 genes that were significantly (p < 0.05, adjusted for multiple comparisons) differentially regulated in B cells between the two groups. The top upregulated genes in B cells after co-culture with DCs were classified under the category of cytokine and chemokine related to immune system responses, more specifically related to inflammation type responses (Figures 6c and Supplemental Figure 4). We then performed gene set enrichment analysis (GSEA)20,21 to match this gene data set against prior defined B-cell related gene sets. Based on the enrichment scores and the relevance to APC function of B cells, we identified two prior defined gene sets most associated with DC-licensed B cells: B cells cultured with TLR7 agonist (imiquimod) versus TLR4 agonist (monophosphoryl lipid A) (Figure 6d,f), and B cells simulated through IgG (Figure 6E,g). Together, these gene sets suggested that the B cells licensed by DC had a gene expression profile consistent with an activated phenotype, similar to B cells activated by TLR and/or the B-cell receptor.
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