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: 10710176
• 负责人: Navin Varadarajan
• 金额: $ 41.17万
• 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/10710176
• 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; Permeability; Phase; Phenotype; Population; Proliferating; Property; Protein translocation; Research Personnel; Resolution; Retrieval; Specificity; Subcellular structure; System; Systems Development; T-Cell Receptor; T-Lymphocyte; Technology; Therapeutic; Tissues; Training; United States Food and Drug Administration; Vaccines; Validation; Work; antibody immunotherapy; 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; manufacture; metabolic profile; migration; morphometry; multiscale data; nano; neoplastic cell; patient response; response; sample fixation; single cell technology; single-cell RNA sequencing; software systems; success; technology development; technology validation; 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.

我们的目标是开发和严格验证一项整合细胞的变革性技术 高通量的单细胞分辨率的功能/活动及其深层分子特征。 免疫疗法已成为治疗人类癌症的一种高效方法，并通过 利用免疫系统的能力及其识别和消除癌细胞的能力。 免疫疗法具有不同的优势，包括：（i）持续耐用的反应； （ii）定义的机制 行动； （iii）比传统方法更高的特异性和更少的目标效应。以及抗体 免疫疗法，用于重定向免疫反应的基因工程T细胞最近收到了食物 和药物管理局（FDA）批准。基于在体外扩张的t中注入t 带有T细胞受体（TCR）或嵌合抗原受体（CAR）的细胞表现出戏剧性的 和耐用的反应，即使在经过大量预处理的患者中也是如此。尽管有这些最初的临床成功，但患者 响应差异很大。最近的相关数据表明，制成的T细胞产品的可变性可能 成为临床成功的主要决定因素。由于细胞输注产物是一种异质混合物 细胞，映射种群的复杂性需要识别功能和分子曲线的能力 单细胞分辨率的细胞。对于能够映射这种复杂性的技术的必要性 在T细胞功能和能够在单细胞分辨率下链接到分子曲线的功能。我们建议 多尺度智能收敛（音乐）的开发和验证。音乐将提供多尺度 从分子到亚细胞动力学再到相同细胞上细胞 - 细胞相互作用生物学的数据 细胞。鉴于细胞用于ACT的组成的异质性，它是理想的系统 音乐的发展和验证。我们的调查人员团队在单细胞技术方面拥有专业知识 开发和免疫疗法，机器学习以及图像分析和数据建模。我们预料到这一点 该提案的成功实施将使音乐作为学习平台的验证 多尺度细胞生物学。反过来，这将导致T细胞输注产品的更可靠的生物制造， 而且，更有效的免疫细胞的工程对免疫疗法有广泛的影响。

项目成果

Decoding the mechanisms of chimeric antigen receptor (CAR) T cell-mediated killing of tumors: insights from granzyme and Fas inhibition.

• DOI: 10.1038/s41419-024-06461-8
• 发表时间: 2024-02-02
• 期刊: CELL DEATH & DISEASE
• 影响因子: 9
• 作者: Montalvo, Melisa J.;Bandey, Irfan N.;Rezvan, Ali;Wu, Kwan-Ling;Saeedi, Arash;Kulkarni, Rohan;Li, Yongshuai;An, Xingyue;Sefat, K. M. Samiur Rahman;Varadarajan, Navin
• 通讯作者: Varadarajan, Navin

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

• DOI: 10.1093/bioinformatics/btad584
• 发表时间: 2023-10-03
• 期刊: Bioinformatics (Oxford, England)