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。人类免疫系统参与了与几乎所有其他免疫系统的复杂相互作用。 体内的系统。特别地，适应性免疫应答的B细胞组分在以下方面起作用： 各种疾病环境，包括传染病，癌症，自身免疫，心血管，血液， 神经系统疾病和其他疾病。此外，抗体（B细胞的产物）被有效地用于 诊断治疗和预防然而，尽管几十年的抗体发现努力，仍然有很大的 将人抗体序列与抗原特异性联系起来的数据有限（靶的优先识别 通过给定抗体的抗原）。数据如此有限的主要原因之一是，即使是高- 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的过程分离。 因此，即使在单个NGS内通常存在数千至数百万个抗体序列， 数据集，仅获得针对不超过2-3个靶标的少数抗体的功能信息 一次抗原。为了解决当前B细胞表征技术的这些重大挑战， 和抗体发现，我们的小组一直专注于单细胞技术的发展， 给定的样品，使得能够从单个高通量的抗体序列映射到抗原特异性， 一次试验大量抗原和B细胞。该技术，LIBRA-seq（链接B细胞 受体对抗原的特异性），包括将B细胞样品与 DNA-条形码化抗原的（理论上无限的）库，从而将B细胞-抗原结合转化为DNA-条形码化抗原的（理论上无限的）库。 “可排序事件”。本质上，LIBRA-seq提供所有以下特征： （B）在单细胞水平上，一次筛选数千至数万个B细胞; （c）对于每个B细胞，测定配对的重链-轻链BCR序列; (d)对于每个B细胞，生成高分辨率抗原特异性图谱。我们最初验证了LIBRA-seq， 在HIV-1和随后的冠状病毒感染背景下的概念验证研究。这些初步研究 为推广LIBRA-seq技术应用于多种抗原靶点奠定基础， 突出技术优化领域，这将是技术发展的重点 提议特别是，在这里，我们建议优化LIBRA-seq的广义应用， 抗原靶点的多样性。 最终，LIBRA-seq技术将对基础和应用产生长期影响。 免疫学，有助于彻底改变我们对抗体-抗原相互作用的理解， 发现靶向具有生物医学意义的多种疾病领域的新型抗体治疗剂。