Detectrons convert transient RNA sequences into stable DNA barcodes for high-throughput analysis of RNA-dependent processes.

Programmable RNA sensors such as toehold switches are used to detect specific RNA sequences. However, their reliance on protein-based or RNA-based outputs limits their use in multiplexed and sequencing-based applications. Here we introduce Detectrons, modular biosensors that couple programmable toehold switches with retron-mediated reverse transcription to transduce RNA inputs into unique DNA barcodes, converting dynamic RNA signals into durable DNA records within living cells. The framework enables alternative modes of transcript-based sensing with applications including viral infection detection. Through the construction of a synthetic toehold retron library and application of machine learning, we uncover key design principles that improve signal strength and specificity. We apply Detectrons to the multiplexed live-cell detection of specific phage infections, enabling transcript-triggered barcode synthesis and quantitative host susceptibility profiling in pooled bacterial populations. Detectrons provide a scalable and generalizable strategy for phage screening and for recording transcriptional events in complex bacterial communities.