Technologies for High-Throughput Mapping of Antigen Specificity to B-Cell-Receptor Sequence

B 细胞受体序列抗原特异性高通量作图技术

• 批准号: 10734412
• 负责人: Ivelin Georgiev
• 金额: $ 84.17万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-19 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734412
• 关键词: Address; Antibodies; Antibody Response; Antibody Specificity; Antigen Targeting; Antigens; Area; Autoimmunity; B-Cell Antigen Receptor; B-Lymphocytes; B-cell receptor repertoire sequencing; Bar Codes; Binding; Cardiovascular system; Cells; Cellular Structures; Communicable Diseases; Complex; Coronavirus; DNA; Data; Data Set; Development; Diagnostic; Disease; Effectiveness; Epitope Mapping; Epitopes; Event; Foundations; Generations; Goals; HIV-1; Hematology; High-Throughput Nucleotide Sequencing; Human; Immune; Immune response; Immune system; Immunodominant Antigens; Immunology; Individual; Infection; Libraries; Ligands; Light; Link; Malignant Neoplasms; Maps; Measures; Methods; Mining; Performance; Prevention; Process; Property; Resolution; Sampling; Specificity; System; Technology; Therapeutic antibodies; Time; Vaccines; Validation; adaptive immune response; antibody and antigen binding; antigen binding; experimental study; high throughput technology; nervous system disorder; new technology; next generation sequencing; novel; pathogen; screening; single cell technology; technology development; therapeutic target; virtual

Project Summary. The human immune system participates in complex interactions with virtually all other systems in the body. In particular, the B cell component of the adaptive immune response plays a role in various disease settings, including infectious disease, cancer, autoimmunity, cardiovascular, hematologic, neurologic diseases, and others. In addition, antibodies (a product of B cells) are effectively used in diagnostics, therapy, and prevention. Yet, despite decades of antibody discovery efforts, there is still very limited data linking human antibody sequence to antigen specificity (the preferential recognition of target antigens by a given antibody). One of the major reasons for such limited data is the fact that even high- throughput antibody sequence identification methods such as next-generation sequencing (NGS) of B cell receptor (BCR) sequences are generally decoupled from the process of antibody functional characterization. As a result, even though there are typically thousands to millions of antibody sequences within a single NGS dataset, functional information is obtained only for a handful of antibodies against not more than 2-3 target antigens at a time. To address these significant challenges for current technologies for B cell characterization and antibody discovery, our group has been focusing on the development of a single-cell technology that, for a given sample, enables the mapping of antibody sequence to antigen specificity from a single high-throughput experiment for a large number of antigens and B cells at a time. The technology, LIBRA-seq (LInking B-cell Receptor to Antigen specificity through sequencing), involves physically mixing a B cell sample with a (theoretically unlimited) pool of DNA-barcoded antigens, thus transforming B cell-antigen binding into a “sequenceable event”. In essence, LIBRA-seq offers all of the following features: (a) Characterization of thousands to tens of thousands of B cells at a time, at the single-cell level; (b) Screening against a large number of antigens at a time; (c) For each B cell, determination of the paired heavy-light chain BCR sequence; (d) For each B cell, generation of a high-resolution antigen specificity map. We initially validated LIBRA-seq in proof-of-concept studies in the context of HIV-1, and subsequently coronavirus, infection. These initial studies lay the foundation for generalizing the LIBRA-seq technology for application toward diverse antigen targets, and highlight areas for technology optimization, which will be the focus of this technology development proposal. In particular, here we propose to optimize LIBRA-seq for generalized application toward a broad diversity of antigen targets. Ultimately, the LIBRA-seq technology will have a long-lasting impact on both basic and applied immunology, helping revolutionize our understanding of antibody-antigen interactions and leading to the discovery of novel antibody therapeutics targeting a large variety of disease areas of biomedical significance.

项目摘要。人类免疫系统参与与几乎所有其他系统的复杂相互作用。 特别是，适应性免疫反应的 B 细胞成分在其中发挥着重要作用。 各种疾病环境，包括传染病、癌症、自身免疫、心血管、血液、 此外，抗体（B 细胞的产物）也可有效用于治疗神经系统疾病等。 然而，尽管进行了数十年的抗体发现工作，但仍然存在很大的困难。 将人类抗体序列与抗原特异性联系起来的数据有限（目标的优先识别） 数据如此有限的主要原因之一是，即使高- 高通量抗体序列鉴定方法，例如 B 细胞的下一代测序 (NGS) 受体（BCR）序列通常与抗体功能表征过程脱钩。 因此，即使单个 NGS 中通常有数千到数百万个抗体序列 数据集，仅针对不超过 2-3 个目标的少数抗体获得功能信息 为了解决当前 B 细胞表征技术面临的这些重大挑战。 和抗体发现，我们的团队一直专注于单细胞技术的开发， 给定的样品，能够从单个高通量将抗体序列映射到抗原特异性 一次对大量抗原和 B 细胞进行实验。 通过测序获得抗原特异性的受体），涉及将 B 细胞样品与 （理论上无限）DNA 条形码抗原库，从而将 B 细胞-抗原结合转化为 本质上，LIBRA-seq 提供以下所有功能：（a）表征 (b) 筛选大量 (c) 对于每个 B 细胞，确定配对的重轻链 BCR 序列； (d) 对于每个 B 细胞，我们验证了 LIBRA-seq 的高分辨率初始抗原特异性图。 HIV-1 和随后的冠状病毒感染背景下的概念验证研究。 为推广LIBRA-seq技术应用于不同抗原靶标奠定基础， 并突出技术优化领域，这将是本次技术开发的重点 特别是，在这里我们建议优化 LIBRA-seq 以实现更广泛的通用应用。 抗原靶标的多样性。 最终，LIBRA-seq 技术将对基础和应用产生长期影响。 免疫学，帮助彻底改变我们对抗体-抗原相互作用的理解，并导致 发现针对多种具有生物医学意义的疾病领域的新型抗体疗法。