Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines.

OBJECTIVES: Cellular deconvolution is a valuable computational process that can infer the cellular composition of heterogeneous tissue samples from bulk RNA-sequencing data. Benchmark testing is a crucial step in the development and evaluation of new cellular deconvolution algorithms, and also plays a key role in the process of building and optimizing deconvolution pipelines for specific experimental applications. However, few in vivo benchmarking datasets exist, particularly for whole blood, which is the single most profiled human tissue. Here, we describe a unique dataset containing whole blood gene expression profiles and matched circulating leukocyte counts from a large cohort of human donors with utility for benchmarking cellular deconvolution pipelines. DATA DESCRIPTION: To produce this dataset, venous whole blood was sampled from 138 total donors recruited at an academic medical center. Genome-wide expression profiling was subsequently performed via next-generation RNA sequencing, and white blood cell differentials were collected in parallel using flow cytometry. The resultant final dataset contains donor-level expression data for over 45,000 protein coding and non-protein coding genes, as well as matched neutrophil, lymphocyte, monocyte, and eosinophil counts.

Brain Expression Levels of Commonly Measured Blood Biomarkers of Neurological Damage Differ with Respect to Sex, Race, and Age.

It is increasingly evident that blood biomarkers have potential to improve the diagnosis and management of both acute and chronic neurological conditions. The most well-studied candidates, and arguably those with the broadest utility, are proteins that are highly enriched in neural tissues and released into circulation upon cellular damage. It is currently unknown how the brain expression levels of these proteins is influenced by demographic factors such as sex, race, and age. Given that source tissue abundance is likely a key determinant of the levels observed in the blood during neurological pathology, understanding such influences is important in terms of identifying potential clinical scenarios that could produce diagnostic bias. In this study, we leveraged existing mRNA sequencing data originating from 2,642 normal brain specimens harvested from 382 human donors to examine potential demographic variability in the expression levels of genes which code for 28 candidate blood biomarkers of neurological damage. Existing mass spectrometry data originating from 26 additional normal brain specimens harvested from 26 separate human donors was subsequently used to tentatively assess whether observed transcriptional variance was likely to produce corresponding variance in terms of protein abundance. Genes associated with several well-studied or emerging candidate biomarkers including neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCH-L1), neuron-specific enolase (NSE), and synaptosomal-associated protein 25 (SNAP-25) exhibited significant differences in expression with respect to sex, race, and age. In many instances, these differences in brain expression align well with and provide a mechanistic explanation for previously reported differences in blood levels.

Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood.

Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.

Donor white blood cell differential is the single largest determinant of whole blood gene expression patterns.

It has become widely accepted that sample cellular composition is a significant determinant of the gene expression patterns observed in any transcriptomic experiment performed with bulk tissue. Despite this, many investigations currently performed with whole blood do not experimentally account for possible inter-specimen differences in cellularity, and often assume that any observed gene expression differences are a result of true differences in nuclear transcription. In order to determine how confounding of an assumption this may be, in this study, we recruited a large cohort of human donors (n = 138) and used a combination of next generation sequencing and flow cytometry to quantify and compare the underlying contributions of variance in leukocyte counts versus variance in other biological factors to overall variance in whole blood transcript levels. Our results suggest that the combination of donor neutrophil and lymphocyte counts alone are the primary determinants of whole blood transcript levels for up to 75% of the protein-coding genes expressed in peripheral circulation, whereas the other factors such as age, sex, race, ethnicity, and common disease states have comparatively minimal influence. Broadly, this infers that a majority of gene expression differences observed in experiments performed with whole blood are driven by latent differences in leukocyte counts, and that cell count heterogeneity must be accounted for to meaningfully biologically interpret the results.

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

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.

Newly-identified blood biomarkers of neurological damage are correlated with infarct volume in patients with acute ischemic stroke.

Our group recently performed a genome-wide informatic analysis that highlighted eight brain-enriched proteins with strong potential to serve as blood biomarkers of neurological injury (GFAP, MBP, ß-synuclein, OPALIN, MT-3, SNAP-25, KIF5A, MOBP), including six that have yet to be widely investigated. In this study, our aim was to determine whether the circulating levels of these proteins could be used to approximate the extent of neural tissue damage in ischemic stroke. To address this aim, blood was collected from 43 ischemic stroke patients immediately upon hospital admission. The serum levels of the eight candidate proteins were measured via ELISA, infarct volume was assessed via manual tracing of neuroradiological images, and correlational analysis was performed to examine potential associative relationships. The serum levels of all eight proteins exhibited positive correlations with infarct volume, however the strongest associations were observed in a subset of four proteins known to originate from neurons specifically (MT-3, SNAP-25, KIF5A, ß-synuclein). Combining the serum levels of these neuron-originating proteins using principal components analysis produced a single composite value that was more strongly correlated with infarct volume than the levels of any single protein considered in isolation (r = 0.48, p < 0.001). Measures of these proteins could potentially be used to provide a minimally invasive approximation of lesion size when advanced imaging techniques are not available, or when imaging results are inconclusive.

Large-scale informatic analysis to algorithmically identify blood biomarkers of neurological damage.

The identification of precision blood biomarkers which can accurately indicate damage to brain tissue could yield molecular diagnostics with the potential to improve how we detect and treat neurological pathologies. However, a majority of candidate blood biomarkers for neurological damage that are studied today are proteins which were arbitrarily proposed several decades before the advent of high-throughput omic techniques, and it is unclear whether they represent the best possible targets relative to the remainder of the human proteome. Here, we leveraged mRNA expression data generated from nearly 12,000 human specimens to algorithmically evaluate over 17,000 protein-coding genes in terms of their potential to produce blood biomarkers for neurological damage based on their expression profiles both across the body and within the brain. The circulating levels of proteins associated with the top-ranked genes were then measured in blood sampled from a diverse cohort of patients diagnosed with a variety of acute and chronic neurological disorders, including ischemic stroke, hemorrhagic stroke, traumatic brain injury, Alzheimer's disease, and multiple sclerosis, and evaluated for their diagnostic performance. Our analysis identifies several previously unexplored candidate blood biomarkers of neurological damage with possible clinical utility, many of which whose presence in blood is likely linked to specific cell-level pathologic processes. Furthermore, our findings also suggest that many frequently cited previously proposed blood biomarkers exhibit expression profiles which could limit their diagnostic efficacy.

Bioinformatic analysis of brain-specific miRNAs for identification of candidate traumatic brain injury blood biomarkers.

BACKGROUND: Detection of brain-specific miRNAs in the peripheral blood could serve as a surrogate marker of traumatic brain injury (TBI). Here, we systematically identified brain-enriched miRNAs, and tested their utility as TBI biomarkers in the acute phase of care. METHODS: Publically available microarray data generated from 29 postmortem human tissues were used to rank 1,364 miRNAs in terms of their degree of brain-specific expression. Levels of the top six ranked miRNAs were then prospectively measured in serum samples collected from 10 Patients with TBI at hospital admission, as well as from 10 controls. RESULTS: The top six miRNAs identified in our analysis (miR-124-3p, miR-219a-5p, miR-9-5p, miR-9-3p, miR-137, and miR-128-3p) were enriched 70 to 320-fold in brain relative to other tissues, and exhibited dramatically greater brain specificity compared to several miRNAs previously proposed as biomarkers. Furthermore, their levels were elevated in serum from patients with TBI compared to controls, and could collectively discriminate between groups with 90% sensitivity and 100% specificity. Interestingly, subsequent informatic pathway analysis revealed that their target transcripts were enriched for components of signaling pathways active in peripheral organs involved in common post-TBI complications. CONCLUSIONS: The six candidate miRNAs identified in this preliminary study have promise as blood biomarkers of TBI, and could also be molecular contributors to systemic physiologic changes commonly observed post-injury.

Diagnosis of ischemic stroke using circulating levels of brain-specific proteins measured via high-sensitivity digital ELISA.

Limited lower detection ranges associated with traditional immunoassay techniques have prevented the use of brain-specific proteins as blood biomarkers of stroke in the acute phase of care, as these proteins are often only present in circulation at low concentrations. Digital ELISA is a newly developed technique with allows for quantification of proteins in biofluids with up to 1000 times greater sensitivity than conventional ELISA techniques. The purpose of this study was to determine whether the extended lower limits of detection associated with digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage. Blood was sampled from ischemic stroke patients (n = 14) at emergency department admission, as well as from neurologically normal controls matched in terms of risk factors for cardiovascular disease (n = 33). Plasma levels of two brain-specific axonal proteins, neurofilament light chain (NfL) and tau, were measured via digital ELISA, and receiver-operating characteristic analysis was used to determine their ability to discriminate between groups. Plasma levels of NfL and tau were both significantly elevated in stroke patients versus controls, and could respectively discriminate between groups with 92.9% sensitivity / 84.9% specificity, and 85.7% sensitivity / 54.6% specificity. Furthermore, adjustment of measured NfL and Tau levels according to the lower-limits of detection associated with commercially-available conventional ELISA assays resulted in a dramatic and statistically significant decrease in diagnostic performance. Collectively, our results suggest that the increased analytical sensitivity of digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage.

Optimized methodology for product recovery following emulsion PCR: applications for amplification of aptamer libraries and other complex templates.

Bias and background issues make efficient amplification of complex template mixes such as aptamer and genomic DNA libraries via conventional PCR methods difficult; emulsion PCR is being increasingly used in such scenarios to circumvent these problems. However, before products generated via emulsion PCR can be used in downstream workflows, they need to be recovered from the water-in-oil emulsion. Often, emulsions are broken following amplification using volatile organic solvents, and product is subsequently isolated via precipitation. Unfortunately, the use of such solvents requires the implementation of special environmental controls, and the yield and purity of DNA isolated by precipitation can be highly variable. Here, we describe the optimization of a simple protocol which can be used to recover products following emulsion PCR using a 2-butanol extraction and subsequent DNA isolation via a commercially available clean-up kit. This protocol avoids the use of volatile solvents and precipitation steps, and we demonstrate that it can be used to reliably recover DNA from water-in-oil emulsions with efficiencies as high as 90%. Furthermore, we illustrate the practical applicability of this protocol by demonstrating how it can be implemented to recover a complex random aptamer library following amplification via emulsion PCR.

Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage.

Background: Historically, limited sensitivity associated with traditional immunoassay methods has prevented the use of brain-specific proteins as blood biomarkers of traumatic brain injury (TBI) during triage, as these proteins exhibit low circulating concentrations. Digital ELISA is a newly-developed technique that is up to 1000 times more sensitive than conventional ELISA methods. The purpose of this study was to determine whether the use of digital ELISA over conventional ELISA improves the performance of brain-specific proteins as blood biomarkers of TBI during triage.Methods: Blood was sampled from TBI patients (n = 13) at emergency department admission, as well as from neurologically normal controls (n = 72). Serum levels of two brain-specific proteins, neurofilament light chain (NfL) and Tau, were measured via digital ELISA. Estimated conventional ELISA measures were generated by adjusting values according to the lower limits of detection achievable with commercially available conventional ELISA assays, and receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of digital ELISA measures to estimated conventional ELISA measures in terms of their ability to discriminate between TBI patients and controls.Results: Used in combination, digital ELISA measures of NfL and Tau could discriminate between groups with 100% sensitivity and 91.7% specificity. Estimated conventional ELISA measures could only discriminate between groups with 7.7% sensitivity and 94.4% specificity. This difference in diagnostic performance was statistically significant when comparing areas under ROC curves.Conclusions: The use of digital ELISA over conventional ELISA methods improves the diagnostic performance of circulating brain-specific proteins for detection of TBI during triage.

Neuro biomarker levels measured with high-sensitivity digital ELISA differ between serum and plasma.

Aim: Digital ELISA-based assays for blood biomarkers of neurological disease are on the verge of clinical use. Here, we aimed to determine whether different preanalytical blood processing techniques influence results. Materials & methods: Concentrations of neurofilament light chain (NfL), Tau and amyloid beta (Aß) were measured in human plasma and serum specimens using digital ELISA and compared between blood products. Measured levels of NfL were highly equivalant between serum and plasma in all analyses, however, measured levels of Tau and Aß were consistently lower in serum relative to plasma. Conclusion: Tau and Aß are likely lost during clotting in serum preparations, and should be assayed in plasma to get an accurate measure of circulating levels.

High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood.

Accurate stroke recognition during triage can streamline care and afford patients earlier access to life-saving interventions. However, the tools currently available to clinicians for prehospital and early in-hospital identification of stroke are limited. The peripheral immune system is intricately involved in stroke pathology and thus may be targetable for the development of immunodiagnostics. In this preliminary study, we sought to determine whether the circulating antibody pool is altered early in stroke, and whether such alterations could be leveraged for diagnosis. One hundred microliters of peripheral whole blood was sampled from 19 ischemic stroke patients, 17 hemorrhagic stroke patients, and 20 stroke mimics in the acute phase of care. A custom-fabricated high-density peptide array comprising 125,000 unique probes was used to assess the binding characteristics of blood-borne antibodies, and a random forest-based approach was used to select a parsimonious set of probes with an optimal ability to discriminate between groups. The coordinate antibody binding intensities of the top 17 probes identified in our analysis displayed an ability to differentiate the total pool of stroke patients from stroke mimics with 92% sensitivity and 90% specificity, as well as detect hemorrhage with 88% sensitivity and 87% specificity, as determined using a same-set cross-validation. These preliminary findings suggest that stroke-associated alterations in the circulating antibody pool may have clinical utility for diagnosis during triage, and that such a possibility warrants further investigation.

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

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood.

Analysis of Early Stroke-induced Changes in Circulating Leukocyte Counts using Transcriptomic Deconvolution.

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.

High-throughput profiling of the circulating proteome suggests sexually dimorphic corticosteroid signaling following ischemic stroke.

Increasing evidence suggests that there are innate differences between sexes with respect to stroke pathophysiology; however, the molecular mechanisms underlying these differences remain unclear. In this investigation, we employed a shotgun approach to broadly profile sex-associated differences in the plasma proteomes of a small group of male ( n = 6) and female ( n = 4) ischemic stroke patients. Peripheral blood was sampled during the acute phase of care, and liquid chromatography electrospray ionization mass spectrometry was used to quantify plasma proteins. We observed widespread differences in plasma composition, as 77 out of 294 detected proteins were significantly differentially expressed between sexes. Corticosteroid-binding globulin (CBG), a negative acute-phase reactant that inversely regulates levels of bioactive free cortisol, was the most dramatically differentially regulated, exhibiting 16-fold higher abundance in plasma from women relative to men. Furthermore, functional annotation analysis revealed that the remaining differentially expressed proteins were significantly enriched for those involved in response to corticosteroid signaling. Plasma CBG levels were further examined in an additional group of male ( n = 19) and female ( n = 28) ischemic stroke patients, as well as a group of male ( n = 13) and female ( n = 18) neurologically normal controls. CBG levels were significantly reduced in male stroke patients relative to male controls; however, no differences were observed between female stroke patients and female controls, suggesting that women may exhibit an attenuated cortisol response to stroke. Collectively, our findings reinforce the idea that there are sex-associated differences in stroke pathophysiology and suggest that cortisol signaling should be investigated further as a potential molecular mediator.

Leukocyte Dynamics Influence Reference Gene Stability in Whole Blood: Data-Driven qRT-PCR Normalization Is a Robust Alternative for Measurement of Transcriptional Biomarkers.

BACKGROUND: The use of reference genes for normalization of whole blood qRT-PCR data may be problematic in conditions such as stroke which induce alterations in white blood cell differential. In this study, we assessed the influence of stroke on the stability of commonly employed reference genes, and we evaluated data-driven normalization as an alternative. METHODS: Peripheral whole blood was sampled from 33 stroke patients and 29 controls, and qRT-PCR was used to measure the expression levels of 10 target genes whose transcripts are known stroke biomarkers. Target gene expression levels were normalized via those of 2 frequently cited reference genes (ACTB and B2M) as well as with the NORMA-Gene data-driven normalization algorithm. RESULTS: Whole blood expression levels of reference genes were significantly altered in stroke patients relative to controls. In comparison to normalization via reference genes, NORMA-Gene produced more robust target gene expression data in terms of differential expression dynamics, variance properties, and diagnostic performance. CONCLUSIONS: Our findings suggest that whole blood expression levels of commonly used reference genes may be sensitive to changes in white blood cell differential, and that data-driven qRT-PCR normalization approaches offer a powerful alternative.

Monocyte-lymphocyte cross-communication via soluble CD163 directly links innate immune system activation and adaptive immune system suppression following ischemic stroke.

CD163 is a scavenger receptor expressed on innate immune cell populations which can be shed from the plasma membrane via the metalloprotease ADAM17 to generate a soluble peptide with lympho-inhibitory properties. The purpose of this study was to investigate CD163 as a possible effector of stroke-induced adaptive immune system suppression. Liquid biopsies were collected from ischemic stroke patients (n = 39), neurologically asymptomatic controls (n = 20), and stroke mimics (n = 20) within 24 hours of symptom onset. Peripheral blood ADAM17 activity and soluble CD163 levels were elevated in stroke patients relative to non-stroke control groups, and negatively associated with post-stroke lymphocyte counts. Subsequent in vitro experiments suggested that this stroke-induced elevation in circulating soluble CD163 likely originates from activated monocytic cells, as serum from stroke patients stimulated ADAM17-dependant CD163 shedding from healthy donor-derived monocytes. Additional in vitro experiments demonstrated that stroke-induced elevations in circulating soluble CD163 can elicit direct suppressive effects on the adaptive immune system, as serum from stroke patients inhibited the proliferation of healthy donor-derived lymphocytes, an effect which was attenuated following serum CD163 depletion. Collectively, these observations provide novel evidence that the innate immune system employs protective mechanisms aimed at mitigating the risk of post-stroke autoimmune complications driven by adaptive immune system overactivation, and that CD163 is key mediator of this phenomenon.

High Interspecimen Variability in Nucleic Acid Extraction Efficiency Necessitates the Use of Spike-In Control for Accurate qPCR-based Measurement of Plasma Cell-Free DNA Levels.

OBJECTIVE: To assess the interspecimen variability associated with plasma DNA extraction in order to provide insight regarding the necessity to use an exogenous spike-in control when measuring cell-free DNA (cfDNA) levels using quantitative polymerase chain reaction (qPCR). METHODS: Plasma specimens were obtained from 8 healthy individuals, 20 patients with cardiovascular disease risk factors, and 54 patients diagnosed with acute stroke. Specimens were spiked with an exogenous oligonucleotide fragment, and total DNA was extracted via automated solid phase anion exchange. We determined recovery of the exogenous fragment via qPCR and used this information to calculate DNA extraction efficiency. RESULTS: Plasma DNA extraction efficiencies varied dramatically between specimens, ranging from 22.9% to 88.1%, with a coefficient of variance of 28.9%. No significant differences in DNA extraction efficiencies were observed between patient populations. CONCLUSIONS: We strongly recommend the use of an exogenous spike-in control to account for variance in plasma DNA extraction efficiency when assessing cell free DNA (cfDNA) levels by qPCR in future biomarker investigations.

Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population.

Our group recently employed genome-wide transcriptional profiling in tandem with machine-learning based analysis to identify a ten-gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publicly available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 h post-symptom onset, as well from 23 cardiovascular disease controls, were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) were extracted, compared between groups, and evaluated for their discriminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in an independent patient population, and further suggest that it is temporally stable over the first 24 h of stroke pathology.