High‐Throughput Image Cytometry Detection Method for CAR‐T Transduction, Cell Proliferation, and Cytotoxicity Assays

Abstract

Chimeric antigen receptor (CAR)‐T cell therapy has drawn much attention due to its recent clinical success in B‐cell malignancies. In general, the CAR‐T cell discovery process consists of CAR identification, T‐cell activation, transduction, and expansion, as well as assessment of CAR‐T cytotoxicity. The current evaluation methods for the CAR‐T discovery process can be time‐consuming, low‐throughput and requires the preparation of multiple sacrificial samples in order to produce kinetic data. In this study, we employed the use of a plate‐based image cytometer to monitor anti‐CAIX (carbonic anhydrase IX) G36 CAR‐T generation and assess its cytotoxic potency of direct and selective killing against CAIX+ SKRC‐59 human renal cell carcinoma cells. The transduction efficiency and cytotoxicity results were analyzed using image cytometry and compared directly to flow cytometry and Chromium 51 (51Cr) release assays, showing that image cytometry was comparable against these conventional methods. Image cytometry method streamlines the assays required during the CAR‐T cell discovery process by analyzing a plate of T cells from CAR‐T generation to in vitro functional assays with minimum disruption. The proposed method can reduce assay time and uses less cell samples by imaging and analyze the same plate over time without the need to sacrifice any cells. The ability to monitor kinetic data can allow additional insights into the behavior and interaction between CAR‐T and target tumor cells. © 2020 International Society for Advancement of Cytometry

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 28, 2020
Source ID
10.1002/cyto.a.24267

Entities

People

  • Atef Fayed
  • Leo Li‐ying Chan
  • Marion Grimaud
  • Quan Zhu
  • Wayne A Marasco
  • Yufei Wang

Organizations

  • Dana–Farber Cancer Institute
  • Harvard Medical School
  • National Foundation for Cancer Research
  • United States Department of Defense

Tags

Fields of Study

  • Biology

Readers

  • Immunology
  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.