Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

O’Connell, Grant; Treadway, Madison B; Tennant, Connie S.; Lucke-Wold, Noelle; Chantler, Paul D.; Barr, Taura L.

doi:10.1007/s12975-018-0623-1

Cited by 13 publications

(9 citation statements)

References 36 publications

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“…Several prior studies have performed genome-wide transcriptomic profiling of peripheral whole blood with the goal of identifying clinically-useful stroke biomarkers [2, 3, 4, 5, 6]. Recent work by our group suggests that similar to other conditions [7], several of the gene expression changes observed between stroke patients and controls in these investigations were likely artifacts of underlying changes in leukocyte counts, and not true changes in transcription at the cellular level [8,9]. Transcriptomic deconvolution is a process which leverages such phenomena to informatically infer the cellular composition of complex biological samples based on aggregate gene expression through the analysis of cell-specific transcripts [10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of early stroke-induced changes in circulating leukocyte counts using transcriptomic deconvolution

O’Connell

Chang

2018

Translational Neuroscience

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Growing evidence suggests that stroke alters the phenotype of the peripheral immune system; better characterization of this response could provide new insights into stroke pathophysiology. In this investigation, we employed a deconvolution approach to informatically infer the cellular composition of the circulating leukocyte pool at multiple timepoints following stroke onset based on whole blood mRNA expression. Microarray data generated from the peripheral blood of 23 cardiovascular disease controls and 23 ischemic stroke patients at 3, 5, and 24 hours post-symptom onset were obtained from a public repository. Transcriptomic deconvolution was used to estimate the relative counts of nine leukocyte populations based on the expression of cell-specific transcripts, and cell counts were compared between groups across timepoints. Inferred counts of lymphoid cell populations including B-cells, CD4+ T-cells, CD8+ T-cells, γδ T-cells, and NK-cells were significantly lower in stroke samples relative to control samples. With respect to myeloid cell populations, inferred counts of neutrophils and monocytes were significantly higher in stroke samples compared to control samples, however inferred counts of eosinophils and dendritic cells were significantly lower. These collective differences were most dramatic in samples collected at 5 and 24 hours post-symptom onset. Findings were subsequently confirmed in a second dataset generated from an independent population of 24 controls and 39 ischemic stroke patients. Collectively, these results offer a comprehensive picture of the early stroke-induced changes to the complexion of the circulating leukocyte pool, and provide some of the first evidence that stroke triggers an acute decrease in eosinophil counts.

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Section: Introductionmentioning

confidence: 99%

Analysis of early stroke-induced changes in circulating leukocyte counts using transcriptomic deconvolution

O’Connell

Chang

2018

Translational Neuroscience

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“…Notably, the 10 genes used as stroke predictors in [7, 8] are differentially expressed between myeloid and lymphoid leukocytes, so that the change in relative cell count explains most of the observed variation [40]. Among these genes is ID3 , which is also a predictor gene in the present study.…”

Section: Discussionmentioning

confidence: 91%

Discovery of stroke-related blood biomarkers from gene expression network models

Theofilatos¹,

Korfiati²,

Mavroudi

et al. 2019

BMC Med Genomics

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Background Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body’s response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke. Methods Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene’s significance in the network was assessed according to the differences in their network’s pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm – support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. Results A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes ( ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1 ), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10–25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes. Conclusions Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers. Electronic supplementary material The online version of this article (10.1186/s12920-019-0566-8) contains supplementary material, which is available to authorized users.

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“…It has become increasingly evident that stroke triggers a general activation of the innate arm of the peripheral immune system, and a general suppression of the adaptive arm of the peripheral immune system [ 11 , 12 ]. Accordingly, numerous prior studies of both ischemic and hemorrhagic pathology have reported that stroke induces an increase in circulating numbers of neutrophils and monocytes, and a simultaneous decrease in the number of circulating lymphocytes [ 13 , 28 – 31 , 35 – 37 ]. Our results are highly consistent with these prior reports, as some of the most statistically significant differences we observed between stroke patients and controls were in these three cell populations.…”

Section: Discussionmentioning

confidence: 99%

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts

et al. 2022

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Background The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential. Methods Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups. Results Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis. Conclusions Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

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Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

Cited by 13 publications

References 36 publications

Analysis of early stroke-induced changes in circulating leukocyte counts using transcriptomic deconvolution

Analysis of early stroke-induced changes in circulating leukocyte counts using transcriptomic deconvolution

Discovery of stroke-related blood biomarkers from gene expression network models

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts

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