Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02129, USA.
Annu Rev Immunol. 2018 Apr 26;36:813-842. doi: 10.1146/annurev-immunol-042617-053035.
Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.
鉴于人类免疫系统的众多细胞类型和分子组成，以及个体之间的巨大差异，我们应该如何着手对免疫进行因果关系和预测性的解释呢？在人类研究中，一个核心策略是利用自然变异来发现变量之间的关系，包括 DNA 变体、表观遗传状态、免疫表型、临床描述符等。在这里，我们重点关注如何利用自然变异来发现模式、推断原则，并为两个领域开发预测模型：（a）免疫细胞激活——单细胞分析如何提高我们发现免疫细胞类型和状态的能力，以及（b）抗原呈递和识别——如何生成模型来预测 MHC 分子上抗原的呈递及其被 T 细胞受体的检测。这两个例子说明了我们如何寻找免疫的驱动因素和靶点的转变，特别是在健康和疾病背景下的人类系统中。
