heatmaps and p-values  (GraphPad Software Inc)

Journal: Journal of Neuroinflammation

Article Title: TYROBP/DAP12 knockout in Huntington’s disease Q175 mice cell-autonomously decreases microglial expression of disease-associated genes and non-cell-autonomously mitigates astrogliosis and motor deterioration

doi: 10.1186/s12974-024-03052-4

Figure Lengend Snippet: Tyrobp deletion reduces C1Q and GFAP levels in Q175 mice. A RT-qPCR of C1q mRNA and western blot and densitometric analysis of C1Q protein in the striatum of WT and Q175 mice with and without Tyrobp (10 months of age), n = 4–6 mice per group. Quantitative analysis is shown as mean ± SEM. Each point represents data from an individual mouse. Statistical analysis was performed using Two-Way ANOVA. *p < 0.05; **p < 0.01. B Representative images showing GFAP (green) staining in the striatum of WT and Q175 mice with and without Tyrobp (upper panel). 4X magnification, Scale bar, 500 µm. Representative images (20X and inset, 40X) showing GFAP (green) staining in the striatum of WT and Q175 mice with and without Tyrobp . Quantification of the intensity is shown as mean ± SEM (n = 5–6 mice per group). Each point represents data from an individual mouse. Statistical analysis was performed using Two-Way ANOVA. *p < 0.05; **p < 0.01. Scale bar, 25 µm. C Heatmap showing Q175 vs WT (top) and Q175;Tyrobp(−/−) vs Q175 (middle) and Tyrobp(−/−) vs WT log 2 fold-change of reactive astrocyte marker genes. in the transcriptome nominal p-value from the differential expression analysis is shown. ****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05

Article Snippet: C Heatmap of binomial p-values for Bioplanet pathways upregulated in human and mouse datasets.

Techniques: Quantitative RT-PCR, Western Blot, Staining, Marker, Quantitative Proteomics

Journal: Journal of Neuroinflammation

Article Title: TYROBP/DAP12 knockout in Huntington’s disease Q175 mice cell-autonomously decreases microglial expression of disease-associated genes and non-cell-autonomously mitigates astrogliosis and motor deterioration

doi: 10.1186/s12974-024-03052-4

Figure Lengend Snippet: Analysis of the HD microglia-specific transcriptome and the consequences of Tyrobp deletion. RNA was purified from freshly isolated striatal microglia and then sequenced. A Analysis of global levels of cell type-specific transcripts from sorted mouse microglia RNA-seq samples using geometric mean of the normalized counts of cell type marker genes of Drd1 (direct)-MSNs, Drd2 (indirect) -MSNs, astrocytes and microglia. B Volcano plot illustrating the DEGs identified in Q175 vs WT comparison. Only the genes with FDR < 0.1 are shown. C Heatmap of binomial p-values for Bioplanet pathways upregulated in human and mouse datasets. D Predicted upstream regulators of upregulated genes from Q175 microglia. E Intersection between genes with H3K27me3 marks from striatal microglia and upregulated genes from Q175 microglia. F Pathways associated with upregulated DEGs with a H3K27me3 mark. Color denotes database (Brown: Bioplanet; Green: KEGG pathways; Blue: Wikpathways). G Volcano plot illustrating the DEGs identified in Q175; Tyrobp (−/−) vs Q175 comparison. Only the genes with FDR < 0.1 are shown. H Heatmap of binomial p-values for Bioplanet pathways decreased in Q175; Tyrobp (−/−) , and integration with increased pathways from specified human and mouse datasets. I KEGG pathways enriched for upregulated genes in the microglial cluster of HD human snRNA-seq (pink/gray), integrated with the pathways downregulated by Tyrobp deletion in Q175 mice (blue). J Western blot and densitometric analysis of phosphorylated and total Erk protein in striatal samples. n = 5–6 mice per group. Quantitative analysis is shown as mean ± SEM. Each point represents data from an individual mouse. Statistical analysis was performed using Two-Way ANOVA. *p < 0.05

Article Snippet: C Heatmap of binomial p-values for Bioplanet pathways upregulated in human and mouse datasets.

Techniques: Purification, Isolation, RNA Sequencing, Marker, Comparison, Western Blot

Journal: Journal of Neuroinflammation

Article Title: TYROBP/DAP12 knockout in Huntington’s disease Q175 mice cell-autonomously decreases microglial expression of disease-associated genes and non-cell-autonomously mitigates astrogliosis and motor deterioration

doi: 10.1186/s12974-024-03052-4

Figure Lengend Snippet: Tyrobp deletion reduces pathways activated in human HD brain but not in HD mouse models. Bulk RNA-seq was performed on whole striatal RNA. A The number of DEGs for each data set at adjusted p < 0.1 and nominal p < 0.05 is shown. Blue bars indicate downregulated DEGs, and red bars indicate upregulated DEGs. B GO terms (biological process) associated with genes from Q175 vs WT data sets after GSEA analysis. C Cell-type enrichment analysis for Q175; Tyrobp (−/−) vs Q175 DEGs (nominal p-value < 0.05). Lines represent − log 10 p-value of chi-square calculation of the cell type enrichment. D GO terms (biological process) associated to genes from Q175; Tyrobp (−/−) vs Q175 data sets after GSEA analysis. dSNP = direct (Drd1) spiny neurons; iSNP = indirect (Drd2) spiny neurons ( E ) Heatmap of Normalized Enrichment Score for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways detected in human and mouse datasets. Pathways have been ranked based on descending normalized enrichment score from the Human HD BA9 dataset (Agus et al., 2019 ). Column #1: Human HD Brodmann area 9 (BA9) (Agus et al., 2019 ); #2: Human HD Cingulate Cortex (Al-dalahmah et al., 2020 ); #3 Presymptomatic human HD BA9 vs control (Agus et al., 2019 ); #4: Human HD caudate (CAU) v Control (Elorza et al., 2021 ); #5 Presymptomatic Human Caudate (Agus et al., 2019 ); #6: Q175; Tyrobp (−/−) vs Q175; #7: Q175 vs WT. F GSEA results for Cytokine-Cytokine receptor interactions and Complement and coagulation cascade pathways. Normalized gene scores are shown

Article Snippet: C Heatmap of binomial p-values for Bioplanet pathways upregulated in human and mouse datasets.

Techniques: RNA Sequencing, Control, Coagulation

Journal: Tissue Engineering. Part C, Methods

Article Title: Characterizing the Macrophage Response to Immunomodulatory Biomaterials Through Gene Set Analyses

doi: 10.1089/ten.tec.2019.0309

Figure Lengend Snippet: Heatmap of (A) phenotype control samples (n = 3 donors, each with n = 2 experimental replicates) and (B) cell-seeded scaffold macrophage samples (n = 1 donor, n = 3 experimental replicates per treatment per time point) with hierarchical clustering of genes and samples using the Euclidean distance metric and unweighted pair-group method with arithmetic mean clustering. Row-wise z-scores were used to normalize each gene across the sample space. The left dendrogram indicates clustering of genes and the top dendrogram indicates clustering of samples. Dendrograms are color coded according to gene set and sample group. Genes present within the dataset that are not part of either the M1 or M2a gene sets are denoted as “Other genes.” Color images are available online.

Article Snippet: To summarize our results, we generated p -value heatmap matrices using GraphPad Prism 8 to indicate which gene sets were significantly differentially expressed between sample groups and the direction of significance.

Techniques: Control

Journal: Scientific Reports

Article Title: A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries

doi: 10.1038/s41598-017-14892-x

Figure Lengend Snippet: Differential Gene Expression Analysis between Conventional (Conv) and 3′-end Digital Gene Expression (3′-DGE) mRNA Sequencing Methods. ( a ) Control (CTRL) data are compared Sorafenib (SOR) or Sunitinib (SUN) to identify differentially expressed genes (DEGs) using EdgeR for both Conv and 3′-DGE datasets. A gene is defined as differentially expressed using a false discovery rate (FDR) cutoff of 0.1. ( b ) Comparison of statistical significance for the 3,136 shared differentially expressed genes (DEGs) from Sorafenib-treated samples in 3′-DGE and Conv methods with FDR <0.1. The negative base-10 logarithm of the p-value for differential expression is plotted for each technique, with depth of color indicating density of points. Pearson’s correlation coefficient is indicated with the inset text. ( c ) Identification of Differential Expression as a Function of Read Depth. The number of differentially expressed genes (DEGs, FDR < 0.1) was quantified after progressive downsampling of UMI counts from 3′-DGE datasets or read counts from conventional (Conv) datasets (for Sorafenib vs. CTRL). ( d ) Comparison of statistical significance for all genes identified from SOR-treated samples and SUN-treated samples by two methods. The negative base-10 logarithm of the p-value for differential expression is plotted for each technique, with depth of color indicating density of points. The Pearson correlation coefficient is calculated for each treatment. ( e ) Comparison of fold change for all genes identified from SOR-treated samples and SUN-treated samples by two methods. The log base two fold-change is plotted for each technique, with depth of color indicating density of points. The Pearson correlation coefficient is calculated for each treatment. ( f , g ) Rank-rank Hypergeometric tests for consistency of differential expression ranking and gene expression signatures. ( f ) All genes for which a p-value for differential expression was calculated were first sorted into up or down regulated genes (as compared to CTRL), and then ranked by statistical significance. The probability of overlap between two different such rank lists was calculated with Fisher’s Exact Test (aka hypergeometric test), and visualized with a heatmap, for all combinations of list cutoffs. ( g ) Pairwise comparisons of SUN- and SOR-treated data for 3′-DGE and conventional. SOR-treated samples show much higher relative statistical significance, as expected, because only SOR induced large changes in gene expression. Note the difference in p-value scales across the three panels, which indicate the relative statistical significance of the results.

Article Snippet: The resulting heatmap of p-values from Fisher’s Exact text was visualized with the MATLAB function imagesc.

Techniques: Gene Expression, Sequencing, Control, Comparison, Quantitative Proteomics

Journal: Scientific Reports

Article Title: A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries

doi: 10.1038/s41598-017-14892-x

Figure Lengend Snippet: Comparison between Conventional (Conv) and 3′-end Digital Gene Expression (3′-DGE) mRNA Sequencing Methods with an Independent Dataset. ( a ) Sensitivity of gene detection by two methods. Gene-wise reads are removed from every sample in a probability proportional to the abundance of the gene in a sample, to generate a set of the number of identified genes over a range of simulated read depths. The curves for individual replicate samples are shown with the thinner points, showing in general low variability. The average is shown with the solid line. ( b , c ) Quantitative gene-wise comparison between two methods. Density of points in scatter plots is indicated by depth of color. Inset text box shows Pearson correlation. In all plots, data are scaled so units are comparable. B. Scatterplots of UMI counts for DToxS’ 3′-DGE versus read counts for NeuroLINCS’ conventional, without normalization by average transcript length. CTRL or SMA refer to the genetic status of the iPS cells (see Methods). ( c ) Scatterplots of UMI counts for DToxS’ 3′-DGE versus transcript length-normalized read counts for NeuroLINCS’ conventional. ( d , e ) Comparison of statistical significance ( d ) or fold-change ( e ) for all genes identified from SMA samples by DToxS’ 3′-DGE method and NeuroLINCS’ Conv method. ( d ) The negative base-10 logarithm of the p-value for differential expression is plotted for each technique, with depth of color indicating density of points. (e) The log base two fold-change is plotted for each technique, with depth of color indicating density of points. (f) Rank-rank Hypergeometric tests for consistency of differential expression ranking and gene expression signatures. All genes for which a p-value for differential expression was calculated were first sorted into up or down regulated genes (as compared to CTRL), and then ranked by statistical significance. The probability of overlap between two different such rank lists was calculated with Fisher’s Exact Test (aka hypergeometric test), and visualized with a heatmap, for all combinations of list cutoffs. Shown here are lists from SMA vs. control for 3′-DGE and conventional.

Article Snippet: The resulting heatmap of p-values from Fisher’s Exact text was visualized with the MATLAB function imagesc.

Techniques: Comparison, Gene Expression, Sequencing, Quantitative Proteomics, Control