DOI: 10.5281/zenodo.6977556
title: Gene expression in monocytes, neutrophils and whole blood after stroke

description: Dataset from: Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1 All methods available in the publication above. Abstract Background After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy. Methods The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNAseq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and &gt;48 h following stroke. Results Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils and whole blood with enrichment of interleukin signaling pathways for different timepoints and stroke etiologies. Compared to control subjects, gene expression was generally up-regulated in neutrophils and generally down- regulated in monocytes over all times for cardioembolic, large vessel and small vessel strokes. Self-Organizing Maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co- expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood. Conclusions Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets. clinical_parameters_MON.txt: Clinical parameters from cohort used from monocyte samples. clinical_parameters_NEU.txt: Clinical parameters from cohort used from neutrophil samples. clinical_parameters_WB.txt: Clinical parameters from cohort used from whole blood samples. MON_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from monocyte samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum &lt;=40 reads were excluded). NEU_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from neutrophil samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum &lt;=40 reads were excluded). WB_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from whole blood samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum &lt;=80 reads were excluded). MON_gene_raw_counts.txt: raw counts for cohort used of monocyte samples. NEU_gene_raw_counts.txt: raw counts for cohort used of neutrophil samples. WB_gene_raw_counts.txt: raw counts for cohort used of whole blood samples. MON_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in monocyte samples. (TPM normalized, filtered features where maximum &lt;=30 reads were excluded). NEU_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in neutrophil samples. (TPM normalized, filtered features where maximum &lt;=30 reads were excluded). WB_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in whole blood samples. (TPM normalized, filtered features where maximum &lt;=30 reads were excluded). All methods to generate the above files are available in the publication: Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1
description: These studies were supported by grants from the National Institutes of Health (NS097000, NS101718, NS075035, NS079153, and NS106950 to FRS, BSS, BPA, and GCJ). The funding agencies had no influence on the study design, analyses, interpretation of data or in writing the manuscript.