Sign in
ImmunologyNature Biomedical Engineering
Peer-reviewed journal article

Label free metabolic imaging to enhance the efficacy of Chimeric Antigen Receptor T cell therapy

Dan L. PhamiD, Dan CappabiancaiD, Matthew H. ForsbergiD, Cole Weaver, Katherine P. MuelleriD, Anna Maria Tommasi, Jolanta VidugirienėiD, Anthony C. Lauer, Kayla SylvesteriD, Jorgo LikaiD, Madison BugeliD, Jing FaniD, Christian M. CapitiniiD, Krishanu SahaiD, Melissa C. SkalaiD

Abstract

Chimeric antigen receptor (CAR) T cell therapy for solid tumors remains challenging due to the complex manufacturing process and the immunosuppressive tumor microenvironment. The manufacturing condition directly impacts CAR T cell yield, phenotype, and metabolism, which correlate with in vivo potency and persistence. Optical metabolic imaging (OMI) is a non-invasive, label-free method to evaluate single cell metabolism based on autofluorescent metabolic coenzymes NAD(P)H and FAD. Using OMI, we identified the dominating impacts of media composition over the selection of antibody stimulation and/or cytokines on anti-GD2 CAR T cell metabolism, activation strength and kinetics, and phenotype. We demonstrated that OMI parameters were indicative of cell cycle stage and optimal gene transfer conditions for both viral transduction and electroporation-based CRISPR/Cas9. Notably, OMI accurately predicted oxidative metabolic phenotype of virus-free CRISPR-edited anti-GD2 CAR T cells that correlated to higher in vivo potency against neuroblastoma. Our data supports OMIs potential as a robust, sensitive analytical tool that enables dynamic and optimal manufacturing conditions for increased CAR T cell yield and metabolic fitness. One sentence summaryAutofluorescence imaging informs manufacturing conditions that enhance yield and metabolic fitness of CAR T cells for neuroblastoma.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISCappabianca '24Pham '26Jagannathan '25Lakhani '23Carleton '25Dessai '26
67% semantic matchBioengineering
Dan Cappabianca, Dan Pham, Matthew H. Forsberg+10 more·bioRxiv·Jan 31, 2024