Back

Systematic discovery of receptor-ligand biology by engineered cell entry and single-cell genomics

Yu, B.; Shi, Q.; Belk, J. A.; Yost, K. E.; Parker, K. R.; Huang, H.; Lingwood, D.; Davis, M. M.; Satpathy, A. T.; Chang, H. Y.

2021-12-14 genomics
10.1101/2021.12.13.472464 bioRxiv
Show abstract

Cells communicate with each other via receptor-ligand interactions on the cell surface. Here we describe a technology for lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to decode receptor specificity. Engineered lentiviral particles displaying specific ligands deliver fluorescent proteins into target cells upon cognate receptor-ligand interaction, without genome integration or transgene transcription. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. We develop an effective presentation strategy to capture interactions between B cell receptor (BCR) and intracellular antigen epitopes. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of TCR-antigen specificities, clonality, cell type, and cell states of individual T cells. ENTER-seq of patient blood samples after CMV infection reveals the viral epitopes that drive human effector memory T cell differentiation and inter-clonal phenotypic diversity that targets the same epitope. ENTER enables systematic discovery of receptor specificity, linkage to cell fates, and cell-specific delivery of gene or protein payloads. HIGHLIGHTSO_LIENTER displays ligands, deliver cargos, and records receptor specificity. C_LIO_LIENTER deorphanizes antigen recognition of TCR and BCR. C_LIO_LIENTER-seq maps TCR specificity, clonality and cell state in single cells. C_LIO_LIENTER-seq of patient sample decodes antiviral T cell memory. C_LI

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.