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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

基本信息

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
项目类别：
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

项目摘要

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的变革性技术