Cytometric analysis of T cell phenotype using cytokine profiling for improved manufacturing of an EBV-specific T cell therapy

利用细胞因子谱分析进行T细胞表型细胞计量学分析,以改进EBV特异性T细胞疗法的生产。

阅读:1

Abstract

Adoptive immunotherapy using Epstein-Barr Virus (EBV)-specific T cells is a potentially curative treatment for patients with EBV-related malignancies where other clinical options have proved ineffective. We describe improved good manufacturing practice (GMP)-compliant culture and analysis processes for conventional lymphoblastoid cell line (LCL)-driven EBV-specific T cell manufacture, and describe an improved phenotyping approach for analysing T cell products. We optimized the current LCL-mediated clinical manufacture of EBV-specific T cells to establish an improved process using xenoprotein-free GMP-compliant reagents throughout, and compared resulting products with our previous banked T cell clinical therapy. We assessed effects of changes to LCL:T cell ratio in T cell expansion, and developed a robust flow cytometric marker panel covering T cell memory, activation, differentiation and intracellular cytokine release to characterize T cells more effectively. These data were analysed using a t-stochastic neighbour embedding (t-SNE) algorithm. The optimized GMP-compliant process resulted in reduced cell processing time and improved retention and expansion of central memory T cells. Multi-parameter flow cytometry determined the optimal protocol for LCL stimulation and expansion of T cells and demonstrated that cytokine profiling using interleukin (IL)-2, tumour necrosis factor (TNF)-α and interferon (IFN)-γ was able to determine the differentiation status of T cells throughout culture and in the final product. We show that fully GMP-compliant closed-process culture of LCL-mediated EBV-specific T cells is feasible, and profiling of T cells through cytokine expression gives improved characterization of start material, in-process culture conditions and final product. Visualization of the complex multi-parameter flow cytometric data can be simplified using t-SNE analysis.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。