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细胞在感染性疾病、自身免疫和变态反应中的作用至关重要。现代测序 技术，如单细胞RNA测序（scRNAseq）和空间转录组学， 作为一种强有力的技术，用于研究各种不同类型的单个B细胞的转录状态， 生物学背景。这些技术产生了大量复杂的数据， 需要使用强大的、复杂的计算方法。对这些数据的分析是 受到数据中嵌入的许多技术和生物偏见的阻碍。在scRNAseq中，对于 例如，沿着沿着某些发育轨迹的细胞的非均匀捕获，以及 多个并发转录程序的表达，对目前的单细胞 聚类和轨迹推断方法。当研究B细胞时， 具有复杂动力学的区室，例如在淋巴组织中发现的区室。解决这些 问题，我们提出了一个新的工具箱的算法建模B细胞活动，结合先验， 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， 阐明时间B细胞发育过程。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物， 在IBD中观察到异质性。总的来说，我们的研究将创造急需的计算工具， 分析scRNAseq和空间转录组学数据中的免疫信号，以及显示， 结合先验知识极大地提高了计算算法的能力， 免疫系统的全方位变化发生在疫苗接种，感染， 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。

• DOI: 10.1101/2023.11.27.568874
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Weber,LeahL;Reiman,Derek;Roddur,MrinmoyS;Qi,Yuanyuan;El-Kebir,Mohammed;Khan,AlyA
• 通讯作者: Khan,AlyA