Genetically encoded biosensor of apoptosis and necrosis reveals the dynamics and biphasic cytotoxicity by Anti-EGFR CAR-T cells
Description
This study presents the development and validation of a dual genetically encoded fluorescence biosensor system capable of distinguishing apoptosis and necrosis in live cells with high spatial and temporal resolution. The system integrates a nucleus-localized FRET-based caspase-3/7 reporter (RealCas3) with a mitochondria-targeted DsRed marker, enabling simultaneous monitoring of caspase activation and membrane integrity during cell death. Using EGFR-positive tumor cell lines and anti-EGFR CAR T cells, we demonstrate the utility of this platform in quantifying and phenotyping immune-mediated cell death dynamics at single-cell resolution.
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Steps to reproduce
The data in this study were generated using a combination of live-cell imaging, flow cytometry, and functional CAR T-cell cytotoxicity assays. Genetically encoded biosensors were procured from VectorBuilder and stably integrated into MDA-MB-231 cells via transfection. Single-cell clones were subsequently isolated under antibiotic selection to ensure consistent and uniform reporter expression. CAR T cells were produced from human PBMCs following CD3/CD28 stimulation and lentiviral transduction with an EGFR-specific CAR construct. All cell lines and primary immune cells were maintained under sterile conditions using standard mammalian cell culture protocols. Apoptosis and necrosis were induced using chemotherapeutic agents and oxidative stressors. Orthogonal validation assays included Annexin V staining, propidium iodide uptake, caspase inhibition (Z-VAD-FMK), TMRM mitochondrial potential measurements, and probe-efflux analysis. Live-cell fluorescence imaging was performed using a Nikon spinning-disk confocal microscope equipped with CFP/YFP and DsRed filter sets. Flow cytometry was conducted on a BD FACSAria III using 405 nm, 488 nm, and 633 nm lasers with fixed detector settings applied across all experiments. Data processing and statistical analyses were performed using NIS-Elements, BD FACSDiva, and GraphPad Prism.
Institutions
- Rajiv Gandhi Centre for Biotechnology