MusIC: A multi-scale technology for integrating dynamic cellular function and molecular profiles

MusIC：整合动态细胞功能和分子谱的多尺度技术

• 批准号: 10275702
• 负责人: Navin Varadarajan
• 金额: $ 52.33万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-25 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10275702
• 关键词: Adoptive Cell Transfers; Antibodies; Apoptosis; B lymphoid malignancy; Bar Codes; Biological Assay; Biological Markers; Biology; Biomanufacturing; CD19 gene; Cell Communication; Cell Cycle; Cell Proliferation; Cell Therapy; Cell physiology; Cells; Cellular Assay; Cellular Morphology; Cellular biology; Classification; Clinical; Clinical Data; Computer Vision Systems; Crosslinker; Cytogenetics; Cytometry; DNA; Data; Detection; Development; Disease remission; Engineering; Exhibits; Gene Expression; Genetic Engineering; Goals; Heterogeneity; Human; Image; Image Analysis; Immune; Immune response; Immune system; Immunotherapy; In Vitro; Individual; Infusion procedures; Intelligence; Label; Link; Machine Learning; Malignant Neoplasms; Maps; Measurement; Microscopy; Molecular; Molecular Profiling; Motion; Nuclear Translocation; Oncology; Organelles; Patients; Phase; Phenotype; Population; Property; Protein translocation; Research Personnel; Resolution; Retrieval; Specificity; Subcellular structure; System; Systems Development; T-Cell Receptor; T-Lymphocyte; Technology; Therapeutic; Time; Tissues; Training; United States Food and Drug Administration; Vaccines; Validation; Work; antibody immunotherapy; base; cancer cell; cancer therapy; cell behavior; cell motility; cell type; cellular imaging; chimeric antigen receptor; chimeric antigen receptor T cells; clinically relevant; contrast imaging; convolutional neural network; crosslink; data modeling; deep neural network; engineered T cells; genotyped patients; imaging system; metabolic profile; morphometry; multiscale data; neoplastic cell; patient response; response; sample fixation; single cell technology; single-cell RNA sequencing; software systems; success; technology development; tumor

Our objective is develop and rigorously validate a transformative technology that integrates cellular functions/activities with their deep molecular signatures at single-cell resolution, in high-throughput. Immunotherapy has emerged as a highly effective approach for the treatment of human cancer, and works by harnessing the power of the immune system and its ability to recognize and eliminate cancer cells. Immunotherapy has distinct advantages, including: (i) sustained and durable responses; (ii) defined mechanisms of action; and (iii) higher specificity and fewer-off target effects than traditional approaches. Along with antibody immunotherapy, genetically engineering T cells for redirecting immune responses has recently received Food and Drug Administration (FDA) approval. Adoptive cell therapy (ACT), based on infusing in vitro expanded T cells bearing either T-cell receptors (TCR), or chimeric antigen receptors (CAR), have demonstrated dramatic and durable responses, even in heavily pretreated patients. Despite these initial clinical successes, patient responses vary widely. Recent correlative data indicate that variability in the manufactured T cell products may be the primary determinant of clinical success. Since cellular infusion products are a heterogeneous mixture of cells, mapping the complexity of the population requires the ability to identify the function and molecular profiles of cells at single-cell resolution. There is an essential need for technologies that are able to map this complexity in T-cell functionality and being able to link function to molecular profiles at single-cell resolution. We propose the development and validation of Multiscale Intelligent Convergence (MusIC). MusIC will provide multi-scale data from molecules to subcellular dynamics to cell-cell interaction biology on the same cells across thousands of cells. Given the heterogeneity in the composition of cells being used for ACT, it serves as the ideal system for the development and validation of MusIC. Our team of investigators has expertise in single-cell technology development and immunotherapy, machine learning, and image analysis and data modeling. We anticipate that the successful implementation of this proposal will enable the validation of MusIC as a platform for studying multi-scale cell biology. This in turn, will lead to the more reliable biomanufacturing of T-cell infusion products, and the engineering of more potent immune cells can have a broad impact on immunotherapy.

我们的目标是开发并严格验证集成蜂窝技术的变革性技术 具有单细胞分辨率、高通量的深层分子特征的功能/活动。 免疫疗法已成为治疗人类癌症的一种高效方法，其作用原理是 利用免疫系统的力量及其识别和消除癌细胞的能力。 免疫疗法具有明显的优势，包括：（i）持续且持久的反应； (二) 明确的机制 行动的； (iii) 比传统方法具有更高的特异性和更少的脱靶效应。与抗体一起 免疫疗法，用于重定向免疫反应的基因工程 T 细胞最近已获得食品 和药物管理局 (FDA) 批准。过继细胞疗法 (ACT)，基于输注体外扩增 T 携带 T 细胞受体 (TCR) 或嵌合抗原受体 (CAR) 的细胞已表现出惊人的 和持久的反应，即使是在接受过大量治疗的患者中。尽管取得了这些初步的临床成功，但患者 反应差异很大。最近的相关数据表明，制造的 T 细胞产品的变异性可能 是临床成功的主要决定因素。由于细胞输注产品是异质混合物 细胞，绘制群体的复杂性需要识别功能和分子特征的能力 单细胞分辨率下的细胞数量。迫切需要能够映射这种复杂性的技术 T 细胞功能，并能够以单细胞分辨率将功能与分子谱联系起来。我们建议 多尺度智能融合（MusIC）的开发和验证。 MusicIC 将提供多尺度 从分子到亚细胞动力学，再到数千个相同细胞的细胞间相互作用生物学数据 细胞。考虑到用于 ACT 的细胞组成的异质性，它是理想的系统 MusicIC 的开发和验证。我们的研究团队拥有单细胞技术方面的专业知识 开发和免疫治疗、机器学习、图像分析和数据建模。我们预计 该提案的成功实施将使MusIC作为研究平台得到验证 多尺度细胞生物学。这反过来将导致 T 细胞输注产品的生物制造更加可靠， 更有效的免疫细胞的工程设计可以对免疫疗法产生广泛的影响。