Radiation Effect on Immune Cells and the Microbiome

总结 在过去的几十年里，我们已经提高了对这些疾病背后的分子机制的理解。 RT的疗效，并取得了进展，以提高RT的效率，同时减少副作用 与附近正常组织的损伤有关。然而，全面了解RT如何在 一个给定的解剖领域，显着改变癌细胞，正常组织，并基于浸润免疫细胞， 每个细胞的“独特性”仍然没有解决。此外，重要的是，这些组合的RT依赖性效应 对不同细胞类型的会聚影响治疗结果仍然知之甚少。公 认识到免疫适应性在控制癌症生长和进展中是重要的;因此，任何治疗方法， 默认情况下，损害免疫细胞会损害免疫系统控制恶性细胞的能力。 项目1关注的是结直肠癌中肿瘤和肿瘤微环境对RT的反应， 在这个项目（项目2）中，我们专注于RT对免疫细胞和微生物组的影响。 项目2的总体假设是免疫系统是癌症反应的重要组成部分 RT和先天性和适应性免疫反应引起的RT是关键，以限制癌症的进展。 然而，RT的这些免疫学益处被RT对免疫细胞的有害作用抵消， 健康和生存，随之而来的白细胞减少和免疫功能障碍。因此，定量的理解 直接或间接（作为旁观者）受影响的免疫细胞的百分比和亚群 RT诱导的损伤是完全理解RT在癌症治疗结果中的作用的基础。 在项目2中，首先，我们将量化外周血单核细胞（PBMC）的百分比，以及 位于辐射野内外的淋巴结内的免疫细胞受到RT的影响。其次， 我们将通过计算单个细胞水平上的累积RT暴露量， 以及相关的细胞反应（即，细胞凋亡，ER线粒体应激，炎症通路的激活， 免疫适应性）。使用单细胞RNA测序和空间转录组学，我们将生成一条 RT对不同免疫细胞类型（T和B细胞亚群，树突细胞， 单核细胞、巨噬细胞、NK细胞）与RT剂量暴露的关系。此外，我们将分析定性 以及RT前后收集的样品中微生物组的定量变化，以推断RT介导的 肠道微生物组的变化可能会影响免疫反应。在项目2的第二部分，一台机器 将利用学习方法整合所有获得的数据，以更全面地了解 RT如何影响免疫反应和治疗结果。

SUMMARY Over the last decades, we have enhanced our understanding of the molecular mechanisms that underlie the efficacy of RT and have also made progress to improve the efficiency of RT while reducing side effects associated with damage to nearby normal tissues. Nevertheless, a comprehensive understanding of how RT, in a given anatomical field, distinctively alters cancer cells, normal tissue, and infiltrating immune cells based on each cell’s “uniqueness" is still unresolved. Further, and importantly, how these combined RT-dependent effects on different cell types converge to influence therapeutic outcomes remains poorly understood. It is well recognized that immune fitness is important in controlling cancer growth and progression; as such, any therapy compromising immune cells, by default, can compromise the immune system’s ability to control malignant cells. Whereas Project 1 focuses on the tumor and tumor microenvironment response to RT in colorectal cancer, in this project (Project 2), we focus on the impact of RT on immune cells and the microbiome. The overall hypothesis of Project 2 is that the immune system is an important component of the cancer response to RT and that innate and adaptive immune responses elicited by RT are pivotal to restrict cancer progression. However, these immunological benefits of RT are counterbalanced by deleterious effects of RT on immune cell fitness and survival with consequent leukopenia and immune dysfunction. As such, a quantitative understanding of the percentage and sub-populations of immune cells that are directly or indirectly (as bystanders) affected by RT-induced damage is fundamental to completely understand the role of RT in cancer treatment outcomes. In Project 2, firstly, we will quantify the percentage of peripheral blood mononuclear cells (PBMC), as well as immune cells within lymph nodes located inside or outside the radiation field that are affected by RT. Secondly, we will quantify the RT-induced damage by calculating both the accumulated RT exposure at the single cell level and related cellular responses (i.e., apoptosis, ER mitochondrial stress, activation of inflammatory pathways, immunological fitness). Using single cell RNA-sequencing and spatial transcriptomics, we will generate a road map of the effects of RT on the different immune cell types (T and B cell subpopulations, dendritic cells, monocytes, macrophages, NK cells) in relation to RT dose exposure. Additionally, we will analyze qualitative and quantitative changes in the microbiome in samples collected before and after RT to infer how RT-mediated changes in the intestinal microbiome could affect immune responses. In the second part of Project 2, a machine learning approach will be utilized to integrate all of the acquired data to develop a more comprehensive view of how RT influences immune responses and treatment outcomes.