Unraveling the functional diversity of B cells in health and disease

揭示 B 细胞在健康和疾病中的功能多样性

• 批准号: 10726375
• 负责人: Aly Azeem Khan
• 金额: $ 45.04万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-02 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726375
• 关键词: Achievement; Address; Affinity; Algorithm Design; Algorithms; Antibodies; Antibody Affinity; Architecture; Autoimmunity; B-Cell Antigen Receptor; B-Lymphocytes; Biological; Cell Compartmentation; Cell model; Cells; Clinical; Collaborations; Communicable Diseases; Complex; Computational Technique; Computational algorithm; Computing Methodologies; Data; Data Set; Development; Developmental Process; Dimensions; Disease; Functional disorder; Gene Expression; Genetic Transcription; Gut associated lymphoid tissue; Health; Heterogeneity; Histologic; Histology; Hypersensitivity; Image; Immune; Immune response; Immune signaling; Immune system; Immunoglobulin Class Switching; Immunoglobulin Constant Region; Immunoglobulin Switch Recombination; Immunology; Individual; Infection; Knowledge; Location; Lymphoid Tissue; Maps; Measures; Mediating; Methods; Modeling; Modernization; Molecular; Output; Pathway interactions; Phenotype; RNA; Research; Research Personnel; Role; Sampling; Slice; Stains; Systems Biology; Techniques; Technology; Time; Tissues; Vaccination; Vaccines; Work; biological systems; biomarker identification; complex data; computerized tools; improved; innovation; insight; novel; predictive marker; programs; response; single-cell RNA sequencing; therapeutic target; tool; transcriptomics; vaccine response

The role of B cells in infectious disease, autoimmunity, and allergy is critical. Modern sequencing technologies, such as single-cell RNA sequencing (scRNAseq) and spatial transcriptomics, have emerged as powerful techniques for studying the transcriptional states of individual B cells in a variety of biological contexts. These technologies generate massive amounts of complex data that necessitate use of powerful, sophisticated computational methods. The analysis of such data is hampered by numerous technical and biological biases embedded in the data. In scRNAseq, for example, the non-uniform capture of cells along some developmental trajectory, as well as the expression of multiple concurrent transcriptional programs, pose a challenge to current single cell clustering and trajectory inference methods. These biases are exacerbated when studying B cell compartments with complex dynamics, such as those found in lymphoid tissues. To address these issues, we propose a novel toolbox of algorithms for modeling B cell activity that combines prior, validated biological knowledge with computational algorithm design. In Aim 1, we develop tools to elucidate temporal B cell developmental processes. And in Aim 2, we develop tools to elucidate B cell spatial transcriptional programs. In Aim 3, apply our tools to a variety of important clinical scenarios, such as mapping the immune correlates of higher affinity antibodies and characterizing the heterogeneity observed in IBD. Overall, our research will create much-needed computational tools for analyzing immune signals in scRNAseq and spatial transcriptomics data, as well as show that incorporating prior knowledge greatly improves the ability of computational algorithms to reveal the full spectrum of immune system changes that occur in response to vaccination, infection, and immune-mediated diseases.

B 细胞在传染病、自身免疫和过敏中的作用至关重要。现代测序 单细胞 RNA 测序 (scRNAseq) 和空间转录组学等技术 成为研究各种个体 B 细胞转录状态的强大技术。 的生物学背景。这些技术产生大量复杂的数据 需要使用强大、复杂的计算方法。对此类数据的分析是 数据中嵌入的许多技术和生物学偏见阻碍了这一过程。在 scRNAseq 中，对于 例如，沿着某些发育轨迹对细胞的不均匀捕获，以及 多个并发转录程序的表达，对当前的单细胞提出了挑战 聚类和轨迹推断方法。研究 B 细胞时这些偏差会加剧 具有复杂动力学的隔室，例如在淋巴组织中发现的隔室。为了解决这些 问题，我们提出了一种新的算法工具箱，用于建模 B 细胞活动，该工具箱结合了先前的、 通过计算算法设计验证生物学知识。在目标 1 中，我们开发工具来 阐明颞 B 细胞的发育过程。在目标 2 中，我们开发工具来阐明 B 细胞 空间转录程序。在目标 3 中，将我们的工具应用于各种重要的临床场景， 例如绘制更高亲和力抗体的免疫相关性并表征 IBD 中观察到的异质性。总的来说，我们的研究将为以下领域创造急需的计算工具： 分析 scRNAseq 和空间转录组数据中的免疫信号，并表明 结合先验知识极大地提高了计算算法揭示问题的能力 因疫苗接种、感染和感染而发生的全方位免疫系统变化 免疫介导的疾病。

项目成果

TRIBAL: Tree Inference of B cell Clonal Lineages.

TRIBAL：B 细胞克隆谱系的树推断。

• 发表时间: 2023-11-27
• 作者: Weber, Leah L;Reiman, Derek;Roddur, Mrinmoy S;Qi, Yuanyuan;El;Khan, Aly A
• 通讯作者: Khan, Aly A