Massively high-throughput profiling of the circulating antibody pool for identification of diagnostic signatures with utility for stroke triage

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PROJECT SUMMARY/ABSTRACT: Stroke is currently the third leading cause of death and leading cause of permanent disability in the United States. Due to the time-efficacy relationship associated with acute stroke interventions, tools which allow for accurate stroke diagnosis during triage have the potential to streamline care and improve patient outcomes. Early transport, transfer, and referral decisions are often made by emergency medical services personnel, triage nurses, and emergency physicians with limited neurological expertise using symptom-based stroke recognition scales. Unfortunately, these assessments exhibit limited accuracy in triage scenarios, and it is currently estimated that up to 30% of patients experiencing stroke are misdiagnosed at first clinician contact, leading to life threatening delays in care. As a result, there has been a push for the identification of stroke-specific blood biomarkers which could be rapidly measured at the point-of-care to help clinicians without extensive neurological expertise make better-informed early triage decisions. It is becoming increasingly evident that the peripheral immune system is intricately involved in stroke pathology, and may be targetable for the development of stroke diagnostics. Not only is there a rapid systemic inflammatory response to the acute injury, but emerging evidence suggests that peripheral immune changes may precede symptom onset and in some cases trigger the acute event itself. The peripheral blood contains up to 1018 unique antibodies targeting antigens associated with nearly every adaptive immune response an individual has experienced in their lifetime, and the repertoire of antibodies found in an individual’s blood can serve as a detailed molecular fingerprint of their immune history as well as current immune status. In the proposed investigation, we aim to identify stroke-associated alterations to the circulating antibody pool which could be used to aid in stroke recognition during triage. To address this aim, peripheral blood will be sampled from a group of consecutive patients suspected of stroke at emergency department admission. Upon final clinical diagnosis, patients will be divided into either a confirmed stroke group or a stroke mimic group. Peptide arrays comprised of 330,000 unique probes will be used to comprehensively assess the binding characteristics of each patient’s peripheral blood antibody pool, and a machine-learning approach will be used to identify a pattern of binding which can optimally discriminate between groups. This work will be the first ever to take a comprehensive approach to profiling the circulating antibody pool in stroke to globally search for disease-specific patterns of alterations; the level of throughput, in combination with the use of powerful machine-learning methods, will increase the odds of identifying diagnostically robust biomarker profiles. Furthermore, diagnostically useful probes identified via peptide array can be readily adapted for use at the point-of-care, providing a clear path to clinical use. This novel, innovative, and highly translational workflow will address an area of dire clinical need; we fully expect to identify a set of candidate peptide probes which will provide the immediate foundation for the development of a rapid point-of-care stroke triage diagnostic.