Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence
Journal article, 2025

The anti-tumor function of engineered T cells expressing chimeric antigen receptors (CARs) is dependent on signals transduced through intracellular signaling domains (ICDs). Different ICDs are known to drive distinct phenotypes, but systematic investigations into how ICD architectures direct T cell function—particularly at the molecular level—are lacking. Here, we use single-cell sequencing to map diverse signaling inputs to transcriptional outputs, focusing on a defined library of clinically relevant ICD architectures. Informed by these observations, we functionally characterize transcriptionally distinct ICD variants across various contexts to build comprehensive maps from ICD composition to phenotypic output. We identify a unique tonic signaling signature associated with a subset of ICD architectures that drives durable in vivo persistence and efficacy in liquid, but not solid, tumors. Our findings work toward decoding CAR signaling design principles, with implications for the rational design of next-generation ICD architectures optimized for in vivo function.

intracellular signaling domains

single-cell RNA sequencing

chimeric antigen receptors

CAR T cells

tonic signaling

persistence

T cell signaling

pooled screens

immunotherapy

T cells

Author

Caleb R. Perez

Massachusetts Institute of Technology (MIT)

Singapore-MIT Alliance for Research and Technology

Andrea Garmilla

Massachusetts Institute of Technology (MIT)

Massachusetts General Hospital

Singapore-MIT Alliance for Research and Technology

Harvard University

Avlant Nilsson

Massachusetts Institute of Technology (MIT)

Chalmers, Life Sciences, Systems and Synthetic Biology

Hratch M. Baghdassarian

Massachusetts Institute of Technology (MIT)

Khloe S. Gordon

Singapore-MIT Alliance for Research and Technology

Massachusetts Institute of Technology (MIT)

Louise G. Lima

Massachusetts Institute of Technology (MIT)

Blake E. Smith

Massachusetts Institute of Technology (MIT)

Harvard University

Marcela V. Maus

Massachusetts General Hospital

Harvard University

Douglas A. Lauffenburger

Massachusetts Institute of Technology (MIT)

Michael E. Birnbaum

Singapore-MIT Alliance for Research and Technology

Massachusetts Institute of Technology (MIT)

Cell Systems

24054712 (ISSN) 24054720 (eISSN)

Vol. 16 101260

Deep Learning the Immune System

Swedish Research Council (VR) (2019-06349), 2020-01-01 -- 2023-12-31.

Subject Categories (SSIF 2025)

Cell and Molecular Biology

DOI

10.1016/j.cels.2025.101260

PubMed

40215972

More information

Latest update

7/31/2026