Back

Screening of a pooled library of chimeric antigen receptor T cells based on secretory function

Soemardy, C.; Mei, A.; Castellanos-Rueda, R.; Espinoza, N. G.; Kizerwetter, M.; Spangler, J. B.; Reddy, S. T.; Di Carlo, D.

2025-07-05 bioengineering
10.1101/2025.07.02.662376 bioRxiv
Show abstract

Chimeric antigen receptor (CAR) T cell therapies have shown promise in treating hematologic malignancies, but challenges remain due to immune suppression, antigen heterogeneity, and insufficient functional screening platforms. Here, we present a modular nanovial-based platform for high-throughput, single-cell functional screening of pooled CAR T cell libraries. Nanovials, hydrogel microparticles with nanoliter-scale cavities, were functionalized with recombinant HER2 antigen and cytokine-capture antibodies to simulate antigen-presenting cells and capture secreted interferon-{gamma} (IFN{gamma}). This system enabled the selective capture, activation, and functional profiling of CAR T cells based on antigen engagement and cytokine secretion. We screened a 32-variant CAR library with diverse intracellular signaling domains, using nanovials to isolate IFN{gamma}-secreting cells after 3- and 12-hour CAR-specific stimulation. IL15RA-containing CARs, particularly IL15RA-CD28, were preferentially enriched in the sorted T cells after 3 hours of stimulation, consistent with early effector activation profiles. By 12 hours, IL15RA-containing constructs remained enriched while other CD40-containing domains showed delayed but substantial enrichment, suggesting prolonged signaling dynamics. The platforms high-throughput capability (>2 million cells screened), compatibility with downstream sequencing, and tunable antigen presentation make it ideal for identifying CAR constructs associated with various time-dependent secretion phenotypes.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.