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 disorders. 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 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.

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.