Back

High-throughput screening of functional neo-antigens and their specific TCRs via the Jurkat reporter system combine with droplet microfluidics

Li, Y.; Qi, J.; Liu, Y.; Zheng, Y.; Zhu, H.; Zang, Y.; Guan, X.; Xie, S.; Zhao, H.; Fu, Y.; Xiang, H.; Zhang, W.; Chen, H.; Liu, H.; Zhao, Y.; Feng, Y.; Bu, F.; Liang, Y.; Li, Y.; Xu, Q.; He, Y.; Sun, L.; Liu, L.; Gu, Y.; Xu, X.; HOU, Y.; Dong, X.; Liu, Y.

2023-02-21 bioengineering
10.1101/2023.02.20.529171 bioRxiv
Show abstract

T-cell receptor (TCR)-engineered T cells can precisely recognize a broad repertoire of targets derived from both intracellular and surface proteins of tumor cells. TCR-T adoptive cell therapy has shown safety and promising efficacy in solid tumor immunotherapy. However, antigen-specific functional TCR screening is time-consuming and expensive, which limits its application clinically. Here, we developed a novel integrated antigen-TCR screening platform based on droplet microfluidics technology, enabling high-throughput peptide-major histocompatibility complex (pMHC) library-to-TCR library screening with high sensitivity and low background signal. We introduced DNA barcoding technology to label peptide antigen candidate-loaded antigen-presenting cells (APCs) and Jurkat reporter cells to check the specificity of pMHC-TCR candidates. Coupled with the next-generation sequencing pipeline, interpretation of the DNA barcodes and the gene expression level of the Jurkat T-cell activation pathway provided a clear peptide-MHC-TCR recognition relationship. Our proof-of-principle study demonstrates that the platform could achieve unbiased pMHC-TCR library-on-library screening, which is expected to be used in the cross-reactivity and off-target testing of candidate pMHC-TCR libraries in clinical applications.

Matching journals

The top 4 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.