Back

CaRPOOL: A Pooled Calcium-Recording CRISPR Screening Platform Identifies CCR7 as a Modulator of Cellular Osmomechanosensing

Ouyang, M.; Wang, J.; Tian, R.

2025-11-28 cell biology
10.1101/2025.11.27.690909 bioRxiv
Show abstract

Cells must continuously sense and respond to environmental changes by translating physical and chemical cues into intracellular signals. However, systematic discovery of genes governing these sensory processes has been limited by the transient nature of signaling events and the low throughput of measurement assays. Here, we present CaRPOOL, a pooled, high-throughput genetic screening platform that integrates the calcium-activity recorder CaMPARI2 with CRISPR interference(CRISPRi), enabling stable capture of transient calcium signals for genome-scale functional screening. Using osmomechanical stimulation as a model, we demonstrate that CaMPARI2 photoconversion faithfully reports stimulus-dependent calcium responses and supports pooled fluorescence-activated cell sorting (FACS)-based screening. A CRISPRi library targeting membrane-associated genes identified both known and previously uncharacterized regulators of mechanotransduction, including the chemokine receptorCCR7. Mechanistic analyses revealed thatCCR7 promotes osmomechanical calcium signaling through a PIEZO1-dependent Gs-cAMP-PKA pathway, establishing it as a mechanosensitive GPCR. Notably, osmotic stress upregulatedCCR7 expression in immune cells and enhanced mechanical responsiveness, suggesting a role in immunomechanical adaptation. Together, these findings introduce a broadly applicable platform for high-throughput discovery of genes controlling dynamic signaling responses and reveals a GPCR-ion channel crosstalk mechanism in mechanotransduction with potential implications for immune cell mechanoadaptation.

Published in Cell Communication and Signaling (predicted rank #21) · training set

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.