Cells can no longer tell their own story in the lab. To study cellular behavior, researchers have used static snapshots and algorithms to fill in the gaps. However, a new generation of DNA-recording tools promises to change that.
These cutting-edge systems, which record signals received by cells, paths traveled, and decisions made, are now logging events indelibly in genomic ink. Early versions could track branching lineages through cell division cycles but were limited. With advances in gene editing and single-cell sequencing technologies, these tools can now capture more complex data.
Researchers have used these tools to analyze gene expression in animal brains, developmental decisions in mouse embryos, and even the gut of engineered bacteria. The field is still developing its technical capabilities, but momentum is building. New investments are accelerating innovation, with proof-of-concept studies pointing towards real-world potential.
Optimized and adopted, these DNA-based recording tools could transform how scientists study development, disease, and cellular decision-making in real time. By engineering cells to record predictable and programmable data, researchers can unlock new frontiers. This technology dates back to 2003, but it wasn’t until the advent of next-generation sequencing that molecular recorders capable of capturing large amounts of biological information could be built.
Synthetic biologist Timothy Lu led the charge in 2014, showing how engineered bacteria converted fleeting transcriptional signals into changes in DNA. Recent advances in CRISPR-Cas9 gene editing have enabled more refined recording strategies, such as base editors and prime editors, which can log gene expression and signalling dynamics with precision.
These next-generation editors are pushing the field beyond lineage recording, allowing researchers to record not just that an event occurred but also what preceded and followed it. This kind of genomic bookkeeping can be parallelized to respond to diverse cellular signals, creating simultaneous, chronological recordings of molecular activity in a type of multitrack data ledger.
While DNA-based recording platforms are powerful tools, they’re not the only solution. Bioengineer Changyang Linghu favors a system where protein monomers are added to growing assemblies in response to transcriptional events. This approach offers significant advantages in non-dividing cells like neurons, but it’s less adaptable for labs than single-cell sequencing.
The field is still evolving, with different approaches being explored. As researchers continue to refine and expand these tools, the possibilities for studying cellular behavior and making new discoveries are vast.
Source: https://www.nature.com/articles/d41586-025-03035-2