Self-Evolvr: self-refreshing molecular barcodes using CRISPR-self-guided mutagenesis

ABSTRACT
DNA barcoding involves embedding unique, identifiable sequences of DNA into cells, which can then be
sequenced at much higher depth than is possible across the genome as a whole, allowing tracing of
evolutionary dynamics and lineages of those cells or other genetic elements over time. However, fixed barcode
systems fail after the first clonal expansion or takeover, where the expansion of a population of genetically
identical cells yields identical barcodes, meaning those individuals and their independent lineages are no
longer distinguishable. This project develops Self-EvolvR, a new self-refreshing DNA barcode technology
aimed at addressing the limitations of current evolving barcode systems for high-resolution lineage tracing
across various biological systems. The research will focus on the development, optimization, and validation of
Self-EvolvR to enable scalable, quantitative measurements of cellular lineages over time across a large variety
of organisms and systems. Our approach uses an engineered Cas9 system to continually to introduce
mutations into the barcode over long periods without self-inactivation. This method is designed to track both
rapid and long-term cellular dynamics with a single construct, improving lineage resolution and reducing
toxicity to the system. Aim 1 will construct the Self-EvolvR system to function in living cell lines, and validate
the technology in vivo through lineage tracing in both bacterial and eukaryotic models, demonstrating its
application across different biological systems. Aim 2 is to enhance the mutation rate of Self-EvolvR using
directed evolution to achieve a resolution capable of distinguishing between cells separated by a few division
events. The Self-EvolvR technology is designed to offer a more versatile and less toxic option in systems
where barcodes can be introduced before the population dynamics of interest occur, facilitating new insights
into cellular evolution and differentiation processes. The technology developed in this project will result in a
new experimental tool for high-resolution lineage tracing applicable in a range of research fields including but
not limited to microbiome studies, development, and immunology.

Grant Number: 5R21HG014016-02
NIH Institute/Center: NIH
Principal Investigator: Michael Baym