I-Corps: A label-free, autofluorescence lifetime flow cytometer for in-line monitoring of T cell function

该I-Corps项目更广泛的影响/商业潜力是开发一种无标记方法，用于临床使用前的细胞治疗生产工艺优化和产品表征。基于细胞的疗法有可能治疗甚至治愈无数疾病。然而，这些复杂的生物制品在严格监管的行业中显示出固有的可变性，并且标准质量评估需要标记来表征细胞的功能子集。目前的评估方法费力、耗时、易受试剂质量变化的影响，并且可能改变细胞功能。该技术可能为细胞制造提供解决方案，这对基于细胞的疗法的持续发展至关重要。I-Corps项目基于开发一种无标记、非破坏性的光学检测技术，具有单细胞分辨率，可量化细胞功能、活力和代谢，包括预测后期细胞质量的机器学习方法。所提出的硬件、光学器件和分析算法可以容易地集成到各种几何形状中，例如流式细胞术和显微镜，从而能够进行非破坏性评估，以进行快速、无标记的诊断和生物制造过程优化。所提出的技术的固有灵活性使非破坏性评估能够快速，无标签的生物制造过程优化。此外，还设计了一个原型，与商业仪器相比，该原型旨在以更低的成本和占地面积与生物制造工作流程集成。 还开发了软件来检测细胞水平上的功能性生物标志物变化，沿着的还有预测后期细胞功能的机器学习模型，这两种模型都没有商业化。所提出的技术在固有的通用蜂窝信号上运行，从而能够在所有细胞类型中使用。 先前的结果表明，该技术对T细胞的功能反应敏感，可在几分钟内预测活化，在活化表面标记物表达前1小时。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a label-free method for cell therapy manufacturing process optimization and product characterization prior to clinical use. Cell-based therapies have the potential to treat, or even cure, a myriad of diseases. However, these complex biological products display intrinsic variability within a tightly regulated industry and standard quality assessments require labels to characterize functional subsets of cells. Current methods of assessment are laborious, time-consuming, susceptible to reagent quality variability, and may alter cell function. The proposed technology may provide a solution for cell manufacturing that is critical for the continued development of cell-based therapies.This I-Corps project is based on the development of a label-free, non-destructive optical detection technology with single-cell resolution to quantify cell function, viability, and metabolism including machine learning methods that predict later cell quality. The proposed hardware, optics, and analytical algorithms may be readily integrated into a variety of geometries, such as flow cytometry and microscopy, enabling non-destructive assessment for rapid, label-free diagnostics and biomanufacturing process optimization. The inherent flexibility of the proposed technology enables non-destructive assessment for rapid, label-free biomanufacturing process optimization. In addition, a prototype has been engineered that was purpose-built to integrate with biomanufacturing workflows at reduced cost and footprint compared to commercial instruments. Software also has been developed to detect functional biomarker changes at the cell level along with machine learning models that predict later cell function, neither of which are commercially available. The proposed technology operates on intrinsic universal cellular signals, enabling utility across all cell types. Previous results demonstrated that the technology is sensitive to functional response from T cells that predict activation within minutes, preceding expression of activation surface marker at 1 hour.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.