ARCHIVE: Machine-Guided Design of an Efficient Open-Ended DNA Recording Device to Increase Resolution of Multiplexed Cell History Tracking

Abstract

Engineering cell-based devices to record events into DNA has potential both as a non-ablative research tool and in the clinic for enacting gene-circuit-based logic of cell therapies conditional on cell history. Whether as a means of understanding interactions on the single-cell level, or reconstructing histories of cellular events, a cellular DNA recording device has widespread utility, with prime editing-based methods at the forefront of this endeavor - notably peCHYRON. Yet, the resolution of such open-ended recording tools are inherently constrained by edit insertion efficiency and cannot yet capture RNA-polymerase II-transcribed signals, which represent a large segment of functionally-defined endogenous gene-regulatory architectures. Here we present ARCHIVE (Amplified Recording of Cellular Histories into Information-dense Vectors of Events), capable of integrating RNA-encoded signals into a predefined genomic recording locus with high efficiency. By utilizing deep-learning assisted prediction of prime-editing efficiency as a surrogate fitness model for generative in silico pegRNA evolution, we developed a recording device with an order-of-magnitude improvement in temporal resolution (efficiency of iterative message integration steps) compared to the state of the art - a capability we establish here at the level of constitutive promoter tracking. We expect ARCHIVE to serve as a launching point for more advanced mammalian synthetic-biology recording devices for both functional genomics and therapeutics research.