ReviewNature protocols2026
Sensing and perturbing mammalian cell states with reprogrammable ADAR sensors (RADARS).
Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
Reprogrammable Adenosine Deaminase Acting on RNA (ADAR) Sensors (RADARS) control RNA translation in mammalian cells, allowing for noninvasive sensing or perturbation of specific cell types based on transcriptional signatures. Upon base-pairing between a target RNA and a sensor RNA, RADARS leverages ADAR to edit a premature stop codon upstream of a gene of interest, thereby releasing translation of the desired cargo. These design principles enable sequence programmability, allowing RADARS to adapt more easily to new contexts than existing tools for targeting cell types. We describe a detailed protocol for performing experiments with RADARS, including designing, cloning and validating RADARS constructs targeting a transcript of interest. RADARS guide sequences can be designed with an intuitive web interface and cloned into existing constructs for downstream applications including imaging, sorting and sequencing. We outline recommendations for cargo choice, sensor design and ADAR system selection, enabling users to choose the best workflow depending on the desired application. Beginning with sensor design, the selection of top-performing RADARS guides can be completed in ~2 weeks, followed by a desired use case. Convenient engineering and application of RADARS for various applications enable the design and execution of various cell-targeting experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.