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| Funder | NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES |
|---|---|
| Recipient Organization | University of Houston |
| Country | United States |
| Start Date | Sep 25, 2021 |
| End Date | Aug 31, 2025 |
| Duration | 1,436 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10491797 |
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.
University of Houston
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