Additional file 2 of Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke
Creators: name: Carmona-Mora, Paulina
name: Knepp, Bodie
name: Jickling, Glen C.
name: Zhan, Xinhua
name: Hakoupian, Marisa
name: Hull, Heather
name: Alomar, Noor
name: Amini, Hajar
name: Sharp, Frank R.
name: Stamova, Boryana
name: Ander, Bradley P.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Molecular Biology
subject: Neuroscience
subject: Physiology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Mental Health
subject: Computational Biology

description: Additional file 2: Supplemental Figure 1. Percentage of DEGs classified per biotype. For every time point and comparison between time points, the percentage of biotype categories for DEGs are shown in the bar plots and corresponding table for (A) monocytes, (B) neutrophils and (C) whole blood. Ig: immunoglobulin; TcR: T-cell receptor; TEC: To be Experimentally Confirmed. Supplemental Figure 2. Predicted upstream regulators for monocytes. (A) Venn diagram represents all upstream regulators predicted at 0-24 h, 24-48 h, and &gt;48 h (Fisher’s p-value&lt;0.05). (B) Top over-represented regulators common to all time points (Fisher’s p-value&lt;0.05). (C) Top over-represented regulators that are specific for each time point (Fisher’s p-value&lt;0.05). Up arrows indicate predicted significant activation (z ≥ 2) or down arrow - significant inhibition (z ≤ −2). White cells indicate no direction can be predicted. (*= Benjamini-Hochberg corrected p-value&lt;0.05). R: receptor; N.R: nuclear receptor. Supplemental Figure 3. Predicted upstream regulators for neutrophils. (A) Venn diagram represents all upstream regulators predicted at 0-24 h, 24-48 h, and &gt;48 h (Fisher’s p-value&lt;0.05). (B) Top over-represented regulators shared at all time points (Fisher’s p-value&lt;0.05). (C) Top over-represented regulators that are specific for each time point (Fisher’s p-value&lt;0.05). Up arrows indicate predicted significant activation (z ≥ 2) or down arrow - significant inhibition (z ≤ −2). White cells indicate no direction can be predicted. (*= Benjamini-Hochberg corrected p-value&lt;0.05). R: receptor; endo: endogenous; reg: regulator. Supplemental Figure 4. Predicted upstream regulators for whole blood. (A) Venn diagram represents all upstream regulators predicted at 0-24 h, 24-48 h, and &gt;48 h (Fisher’s p-value&lt;0.05). (B) Only 2 predicted regulators (Fisher’s p-value&lt;0.05) are shared for the 3 time points. (C) Top over-represented regulators that are specific for each time point (Fisher’s p-value&lt;0.05). Up arrows indicate predicted significant activation (z ≥ 2) or down arrow - significant inhibition (z ≤ −2). White cells indicate no direction can be predicted. R: receptor; reg: regulator; endo: endogenous. Supplemental Figure 5. tPA administration is not associated with differential expression. A) Characteristics of the five cases where tPA was administered. Principal Component Analyses (PCA) using the DEGs from the time points 0-24 and &gt;24 h in LV and SV strokes in B) monocytes, C) neutrophils, and D) whole blood samples. Cases where tPA was administered are colored in orange. Supplemental Figure 6. Co-expression network construction. WGCNA Soft-thresholding power scale free topology fit (left panel) and mean connectivity (right panel) plots for all genes analyzed in (A) monocytes, (B) neutrophils and (C) whole blood. Reference lines are at 0.8 (red) and 0.9 (green) on the left panels and at 100 (red) and 200 (green) on the right panels. Supplemental Figure 7. Workflow of the study. Monocytes and neutrophils were isolated by flow cytometry (scatter plots adapted in part from Carmona-Mora et al. [18]. RNA-seq from the isolated cell samples and whole blood was used for two parallel approaches: differential expression and co-expression networks. For the first one, patients were split into time point groups and self-organizing maps were constructed with the differentially expressed genes. For the construction of co-expression networks, time was considered as a continuous variable.