Role of Myeloid Derived Suppressor Cells in the Immune Response to Surgery

描述(申请人提供)：外科创伤会产生一种严重的炎症反应，当放松管制时，会导致不良的手术结果，包括长期康复、感染和器官功能障碍。对手术的免疫反应涉及复杂的多细胞机制，人们对此知之甚少。这项建议的长期目标是：1)使用全系统的方法，逐个细胞地从手术患者的全血样本中列举和描述主要的免疫细胞亚群；2)了解旨在改善手术结果的免疫调节干预措施的机制基础(例如，补充L精氨酸)；3)了解先天免疫和获得性免疫之间的相互作用，以便确定对患者手术后恢复至关重要的具体机制。为了实现这些目标，Gaudilliere博士将使用下一代流式细胞仪平台(Cytomery by Time of Flight或CyTOF)，该平台最近在Garry Nolan博士(斯坦福大学微生物学和免疫学教授，K23奖项的主要导师)的实验室中开创并投入实用。结合流式细胞术和质谱仪，可以读出标记在抗体上的稀土金属同位素的读数。与传统的基于荧光的细胞术相比，检测信号之间没有重叠使得可以在单细胞水平上测量的参数数量急剧增加(目前高达45个)。在Garry Nolan博士和Martin Anst博士(斯坦福大学麻醉学教授)的指导下进行的一项先导性研究中，Gaudilliere博士建立了一种定量的、可重复性的质量细胞分析方法，用于监测髋关节置换(即全髋关节置换)患者的免疫反应。这项研究的数据为Gaudilliere博士的核心假设奠定了基础，即手术诱导骨髓细胞(SIMCs)复制MDSCs，并通过L精氨酸依赖机制抑制CD8+T细胞对手术的适应性免疫反应。在目标1中，Gaudilliere博士将建立一个体外系统来研究SIMCs是否通过L精氨酸依赖的机制抑制终末效应CD8+T细胞(CD8+T细胞)的功能。在目标2中，Gaudilliere博士提出了第一项干预性临床试验，该试验将使用质量细胞术来研究在THA患者中补充L精氨酸是否会在体内恢复CD8+T细胞应答。在目标3中，Gaudilliere博士将调整和实施统计工具和学习算法(例如，最小绝对收缩和选择运算符或套索)，以研究患者特定的免疫特征是否可以预测手术诱导的SIMCs扩张和CD8+TJeff细胞的抑制。Gaudilliere博士是斯坦福大学医学院的麻醉师，具有工程学、生物化学和分子生物学的背景，因此非常有资格实现这些目标。诺兰实验室在将质量细胞术应用于单细胞分析方面被公认为是世界级的，它将为高迪列尔博士提供机会和环境，让他获得技能，成为这项技术的领先专家。此外，Gaudilliere博士还得到了一个多学科的协作团队的支持，该团队拥有信号生物学、人类免疫学、统计学和生物信息学以及临床实验科学和试验设计方面的专业知识。他还将受益于斯坦福大学麻醉学、免疫学和统计学系提供的综合力量和资源。为了实现他的研究目标，并为他作为一名独立研究员的职业生涯做好准备，Gaudilliere博士创建了一个多学科的职业发展计划，其中包括：1)人类免疫学和免疫监测方面的高级培训；2)流行病学的研究生水平教学和临床研究设计指导培训；3)生物统计学、数据挖掘和机器学习方法的应用方面的研究生水平教学和指导培训，用于分析来自大规模细胞分析的复杂数据集。综上所述，单细胞质量细胞术将被用来监测体内系统水平的手术免疫反应。这一方法不仅将阐明特定的机制(例如，依赖精氨酸的SiMC介导的CD8+T细胞抑制)，而且还将在整个免疫系统的背景下描述这些机制。与先前提出的假设相比，数据的多维属性必然会产生更深层次且潜在更具临床相关性的假设。这一建议的结果构成了一种数据驱动的战略，以指导未来的研究工作和R01应用，以确定预测手术结果的患者特有的免疫特征，并探索新的免疫调节策略，以提高手术后的恢复。

DESCRIPTION (provided by applicant): Surgical trauma produces a profound inflammatory response that, when deregulated, leads to adverse surgical outcomes including protracted recovery, infection, and organ dysfunction. The immune response to surgery involves complex, multi-cellular mechanisms that are poorly understood. The long-term goals of this proposal are to: 1) use a systems-wide approach to enumerate and characterize the major immune cell subsets, on a cell-by-cell basis, from whole blood samples taken from patients undergoing surgery; 2) understand the mechanistic basis of immune-modulatory interventions designed to improve surgical outcomes (e.g., L-arginine supplementation); 3) understand the interplay between innate and adaptive immunity in order to identify specific mechanisms that are critical for patients' recovery from surgery. To achieve these goals, Dr. Gaudilliere will use a next-generation flow cytometry platform (Cytometry by Time of Flight or CyTOF), recently pioneered and brought to practical utility in the laboratory of Dr. Garry Nolan (Professor of Microbiology and Immunology, Stanford University and primary mentor for this K23 award). Uniting flow cytometry with mass spectrometry enables readouts from rare earth metal isotopes tagged to antibodies. In contrast to traditional fluorescence-based cytometry, the absence of overlap between detection signals allows for a dramatic increase in the number of parameters that can be measured at the single-cell level (currently up to 45). In a pilot study and working under the guidance of Drs. Garry Nolan and Martin Angst (Professor of Anesthesiology, Stanford University), Dr. Gaudilliere established a quantitative and reproducible mass cytometry assay with which to monitor the immune response in patients undergoing hip replacement (i.e., total hip arthroplasty). The data from this study form the groundwork for Dr. Gaudilliere's core hypothesis that Surgery-induced Myeloid Cells (SiMCs) phenocopy MDSCs and suppress the CD8+ T cell adaptive immune response to surgery via an L-arginine-dependent mechanism. In Aim 1, Dr. Gaudilliere will build an in vitro system to investigate whether SiMCs suppress terminal effector CD8+ T cell (CD8+Teff) function via an L- arginine-dependent mechanism. In Aim 2, Dr. Gaudilliere proposes a first interventional clinical trial that will use mass cytometry o investigate whether L-arginine supplementation in patients undergoing THA will restore CD8+Teff response in vivo. In Aim 3, Dr. Gaudilliere will adapt and implement statistical tools and learning algorithms (e.g., the least absolute shrinkage and selection operator or LASSO) to investigate whether patient- specific immune features predict surgery-induced expansion of SiMCs and suppression of CD8+Teff cells. Dr. Gaudilliere is an anesthesiologist at Stanford University School of Medicine with a background in engineering, biochemistry, and molecular biology, and is therefore exceptionally well qualified to address these aims. The Nolan Lab, acknowledged as world-class in the application of mass cytometry to single-cell analysis, will provide Dr. Gaudilliere with the opportunity and environment to acquire the skills for him to become a leading expert in this technology. Furthermore, Dr. Gaudilliere is supported by a multidisciplinary and collaborative team with expertise in signaling biology, human immunology, statistics and bio-informatics, and clinical experimental science and trial design. He will also benefit from the combined strength and resources provided by the Stanford Departments of Anesthesiology, Immunology, and Statistics. To accomplish his research goals and prepare him for a career as an independent investigator, Dr. Gaudilliere has created a multi-disciplinary career development plan incorporating: 1) advanced training in human immunology and immune monitoring with mass cytometry; 2) graduate level didactics in epidemiology and mentored training in clinical study design; and 3) graduate level didactics and mentored training in biostatistics, data mining, and application of machine learning methods for the analysis of complex datasets derived from mass cytometry. In summary, single-cell mass cytometry will be utilized to monitor immune responses to surgery at the systems level in vivo. This approach will not only elucidate specific mechanisms (e.g., arginine-dependent SiMC-mediated suppression of CD8+ T cells) but will also characterize these mechanisms as they occur in the context of the entire immune system. The multidimensional attribute of the data will necessarily generate deeper and potentially more clinically relevant hypotheses than previously posed. The output of this proposal constitutes a data-driven strategy to guide future research efforts and R01 applications to identify patient- specific immune traits predictive of surgical outcomes and explore novel immune-modulatory strategies to improve recovery from surgery.