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

对循环抗体库进行大规模高通量分析，用于识别诊断特征并用于中风分类

• 批准号: 10302835
• 负责人: Grant C O'Connell
• 金额: $ 20.13万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-27 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10302835
• 关键词: Accident and Emergency department; Acute; Address; Admission activity; Ambulances; Antibodies; Antibody Repertoire; Antigen Targeting; Area; Back; Binding; Biological Assay; Biological Markers; Blood; Blood Tests; Cardiovascular Diseases; Caring; Cause of Death; Cells; Characteristics; Clinical; Clinical Research; Complex; Country; Custom; Detection; Development; Diagnosis; Diagnostic; Disease; Emergency Department Physician; Emergency medical service; Event; Exhibits; Foundations; Goals; Human Resources; Immune; Immune Targeting; Immune system; Immunoassay; Immunologic Markers; Individual; Injury; Intervention; Investigation; Ischemic Stroke; Life; Machine Learning; Measures; Messenger RNA; Molecular; Molecular Profiling; Myocardial Infarction; National Institute of Nursing Research; Nervous System Trauma; Neurologic; Neurons; Nurses; Outcome; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Peptides; Peripheral; Phase; Play; Prehospital Emergency Care; Proteins; Proteome; Proteomics; Recording of previous events; Role; Sampling; Severities; Source; Stroke; Symptoms; Testing; Time; Triage; Troponin; United States; Validation; Work; acute stroke; adaptive immune response; antibody detection; base; biomarker discovery; biomarker panel; candidate marker; clinical Diagnosis; clinical implementation; clinical translation; cohort; density; diagnostic accuracy; disability; experience; genome-wide; immunological status; improved; individual response; innovation; machine learning method; nervous system disorder; novel; peripheral blood; point of care; point-of-care detection; precision medicine; screening; stroke intervention; stroke patient; symptom cluster; symptom science; systemic inflammatory response; tool; transcriptomics

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.

项目摘要/摘要： 中风目前是美国死亡的第三大主要原因，是美国永久残疾的主要原因。 由于与急性中风干预相关的时间效能关系，工具可以准确 分类过程中的中风诊断有可能简化护理并改善患者预后。早期的 运输，转移和推荐决定通常由紧急医疗服务人员，分类 使用基于症状的中风识别的护士和具有有限神经专业知识的急诊医生 秤。不幸的是，这些评估在分类方案中的准确性有限，目前是 据估计，在初次临床接触时，多达30％的患者患有中风的患者被诊断出来，导致 威胁生命的延迟护理。结果，一直在推动识别中风特异性血液 生物标志物可以在护理点上快速测量，以帮助临床医生没有广泛的神经系统 专业知识做出了更好的早期分类决策。越来越多的证据表明外围 免疫系统错综复杂地参与中风病理学，可能是中风发展的目标 诊断。不仅对急性损伤有快速的全身性炎症反应，而且有新兴的证据 表明外周免疫变化可能在症状发作之前，在某些情况下触发急性 事件本身。外周血最多包含1018种独特的抗体，该抗原几乎与 一个人一生中经历的每种自适应免疫反应，以及抗体的曲目 在个人的血液中发现可以作为其免疫史的详细分子指纹以及 当前的免疫学状况。在拟议的调查中，我们旨在确定与中风相关的变化 循环抗体池，可用于在分类过程中有助于中风识别。为了解决这个目标， 外周血将从一组涉嫌中风的连续患者中取样 部门入场。在最终的临床诊断中，将将患者分为确认的中风组 或中风模拟组。完成330,000个独特探针完成的肽阵列将用于全面 评估每个患者外周血抗体库的结合特征和机器学习 方法将用于确定可以在组之间最佳区分的绑定模式。这项工作 将是有史以来第一个采用全面方法来分析循环抗体池 全球搜索特定疾病的改变模式；吞吐量的水平，结合使用 功能强大的机器学习方法将增加识别诊断性稳健生物标志物的几率 概况。此外，可以很容易地适应通过肽数组确定的诊断有用的项目 护理点，为临床使用提供了清晰的途径。这部小说，创新且翻译高度的工作流程 将满足一个可怕的临床需求；我们完全期望确定一组候选肽问题 为开发快速护理中风分诊诊断提供了直接基础。