A data-driven bioinformatics platform for the design and analysis of multiplexed antibody-based cytometry experiments in cancer research

数据驱动的生物信息学平台，用于设计和分析癌症研究中基于多重抗体的细胞计数实验

• 批准号: 10528837
• 负责人: Pablo Gonzalez Camara
• 金额: $ 35.94万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10528837
• 关键词: Algorithms; Antibodies; Antigens; Atlases; Automated Annotation; Base Composition; Bioinformatics; Cell Separation; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Characteristics; Cloud Computing; Collaborations; Collection; Communities; Computer software; Computing Methodologies; Cost efficiency; Cytometry; Data; Data Set; Ecosystem; Educational workshop; Ensure; Flow Cytometry; Gene Expression Profile; Genes; Goals; Hematopoietic Neoplasms; Human; Immunohistochemistry; Informatics; Information Theory; Learning; Leukocytes; Malignant neoplasm of pancreas; Manuals; Measurement; Methods; Neoplasm Metastasis; Ontology; Pathogenesis; Performance; Phenotype; Population; Process; Proteins; Publishing; Reproducibility; Research; Research Personnel; Resolution; Standardization; Statistical Models; Technology; Testing; Tissues; Tumor Tissue; Tumor-infiltrating immune cells; Work; anticancer research; base; cancer therapy; cell type; design; experimental study; fluorophore; learning algorithm; leukemia; neoplastic cell; open source; outreach; public repository; relational database; repository; single-cell RNA sequencing; therapy resistant; transcriptome; transcriptomics; tumor; tumor progression; web portal

ABSTRACT Tumor progression, resistance to therapy, and metastasis are closely related to the characteristics of the tumor cell ecosystem. Multiplexed antibody-based cytometry is the standard method for phenotypic characterization of tissue composition, pathogenesis, and immune infiltration with single-cell (and sometimes spatial) resolution. The identification of cell populations in these data is facilitated by algorithms that cluster cells according to their antigenic profile, as well as by predefined sets of markers that have historically evolved by trial and error. However, the annotation of these data is a manual, subjective, and laborious process that hinders the reproducibility and accuracy of the results. The design of antibody panels that include specific markers for all cell types and states present in a tissue is usually unfeasible, and the efficiency of commonly used markers is unknown. Consequently, cell clusters can differ little in their antigenic profile or contain a mixture of cell types. To overcome these limitations, this project will develop informatics technologies that leverage existing single-cell transcriptomic atlases to assist and automate the design and analyses of multiplexed antibody-based cytometry experiments. Our working hypothesis is that the vast amount of available single-cell transcriptomic data of tissues can inform the design, annotation, and analysis of cytometry experiments. We will develop and evaluate informatics technologies for establishing reference antigenic profiles and optimal antibody panels based on single-cell proteotranscriptomic data (Aim 1 ), and for automating the identification, annotation, and gating of cell populations in multiplexed antibody-based cytometry experiments (Aim 2). These new computational methods will enable any researcher to 1) automatically identify and annotate cell populations in a cytometry dataset based on reference single-cell data hosted in a repository, 2) define optimal gates for sorting cell populations, 3) transfer gates across experiments, 4) design optimal antibody panels for a given tissue or set of cell populations, and 5) infer the gene expression profile of cells. We will implement these methods in an open-source software and online portal for the transcriptome-guided annotation and analysis of cytometry data of tumors, and will closely work with end-users through several planned workshops and tutorials to maximize the utility and outreach of this platform (Aim 3). We will test our platform on leukemic and pancreatic cancer tissues profiled with spectral flow cytometry and multiplexed quantitative immunohistochemistry. The informatics technologies developed in this project will transform cancer research by boosting the phenotypic resolution, accuracy, and reproducibility of multiplexed antibody-based cytometry analyses of tumor tissues.

抽象的 肿瘤进展，对治疗的抵抗力和转移与肿瘤细胞生态系统的特征密切相关。基于多重抗体的细胞仪是通过单细胞（有时是空间）分辨率的组织组成，发病机理和免疫浸润的表型表征的标准方法。这些数据中细胞群的鉴定是由细胞根据其抗原谱的聚集算法以及通过试验和误差发展而来的预定义标记集的算法促进的。但是，这些数据的注释是一个手动，主观和费力的过程，它阻碍了结果的可重复性和准确性。通常不可行的抗体面板的设计，这些抗体面板包括针对组织中所有细胞类型和状态的特定标记，并且常用标记的效率尚不清楚。因此，细胞簇在其抗原剖面上可能有很小的不同或包含细胞类型的混合物。为了克服这些局限性，该项目将开发信息学技术，以利用现有的单细胞转录组图形图，以协助和自动化基于多重抗体的细胞术实验的设计和分析。我们的工作假设是，组织的大量可用单细胞转录数据可以为细胞术实验的设计，注释和分析提供信息。我们将开发和评估基于单细胞蛋白质转录组数据（AIM 1）的参考抗原谱和最佳抗体面板的信息技术（AIM 1），并自动化基于多路复用抗体的细胞术实验（AIM 2）中细胞种群的鉴定，注释和门控。这些新的计算方法将使任何研究人员能够从1）自动识别和注释细胞术数据集中的细胞群，该数据集基于托管在存储库中的参考单细胞数据，2）定义分类细胞种群的最佳门，3）在实验中转移门，4）4）针对细胞群体的最佳抗体范围或5）cembolations and Setter and net cember and 5）。我们将在开源软件和在线门户网站中实现这些方法，以通过几个计划的研讨会和教程与最终用户密切合作，以最大程度地与最终用户合作，以​​最大程度地利用该平台的实用性和外观（AIM 3）。我们将测试我们对白血病和胰腺癌组织的平台，这些平台用光流式细胞术和多重定量免疫组织化学进行了测试。该项目中开发的信息学技术将通过促进基于多重抗体的肿瘤组织细胞术分析的表型分辨率，准确性和可重复性来改变癌症研究。