This technology is a rapid assessment platform that integrates biomaterials, imaging, and machine learning models to evaluate the functionality of T cells for therapeutic applications.
Autologous anti-cancer T cell therapies rely on the health and functionality of patient-derived T cells. Current methods for assessing T cell functionality rely on biomarker-based evaluations, which are limited in their ability to provide comprehensive and actionable insights into T cell health. These approaches often fail to capture critical functional attributes, such as the proliferative capacity and effector phenotype, which are essential for successful therapeutic outcomes. There are currently no platforms that offer rapid, high-level readouts to directly evaluate the functional capacity of T cells before their clinical application.
This technology is a platform that integrates biomaterials, imaging, and deep learning tools to measure T cell function. The platform assesses T cell functionality using phase contrast images of cells cultured on biomaterial-coated substrates. T cell spreading and morphological characteristics are measured over a short period, capturing key functional attributes such as proliferation potential and effector state. A machine learning algorithm processes the images for automated, unbiased classification of T cell health states. The assay can be extended to predict T cell proliferation capacity through a regression model, offering a rapid and actionable alternative to traditional long-term culture methods. By enabling precise selection and optimization of functional T cells, this technology could enhance the effectiveness and consistency of autologous immunotherapies.
This technology has been validated with samples from both healthy donors and patients with chronic lymphocytic leukemia.
Patent Pending
IR CU24313
Licensing Contact: Dovina Qu
