Researchers at the Whitehead Institute and MIT have introduced PEtracer, a lineage-tracing platform that leverages prime editing alongside single-cell sequencing and imaging to accurately chart cell fate. By continuously writing permanent genetic marks into each cell’s genome, the system creates an evolving record of ancestry that can be read out either by sequencing or high-resolution imaging.
In proof-of-concept studies, the team coupled PEtracer with MERFISH spatial transcriptomics in a mouse model of tumor metastasis, reconstructing the growth of individual tumors in vivo. Their analysis revealed distinct sets of cell-intrinsic and cell-extrinsic factors that coordinate tumor development, illustrating the power of the approach to capture both lineage and microenvironmental context.
By enabling systematic characterization of cell fate with high spatial and temporal resolution, PEtracer opens new possibilities for studying development, tissue maintenance, and disease progression. We spoke with Luke Koblan, first author of the study, to learn more about the technology’s design, challenges, and potential applications.
Could you explain, in a nutshell, how PEtracer works?
PEtracer is a new technology that allows us to describe cellular identities, spatial organization, and lineage information with high spatial and temporal resolution. It works by using prime editing to continuously write permanent marks into scratchpads introduced into each cell’s genome. These marks accumulate over time, creating a record of each cell’s ancestry. We can capture this lineage information using both single-cell sequencing and advanced high-resolution imaging approaches.
In contrast to existing methods, PEtracer provides an evolving record of cell lineage information with both high spatial and temporal resolution. Using imaging readouts, PEtracer affordably scales to reconstruct rich, spatially-resolved phylogenies, while also capturing each cell’s gene expression profile and microenvironment from intact tissues.
What was the initial motivation from your team to combine prime editing with single-cell sequencing and spatial imaging in this way?
Our initial goal to develop an approach to assay cell state and lineage with high spatial and temporal resolution led us to form a “technological wish list” of sorts. To mark cell lineages continuously over time with tunable labeling kinetics, we knew we had to use Cas9-based genome editing tools. Additionally, we hoped to take advantage of the complementary strengths of single-cell sequencing to profile the complete transcriptome and imaging technologies to visualize the native tissue context. More specifically, to assay lineage information with cutting-edge hybridization-based imaging readouts, we knew that we needed a lexicon of pre-defined lineage marks that could be readout with hybridization probes. Following some in silico simulation work, we determined that prime editing was a DNA editing tool uniquely suited to satisfy these criteria.
Was there a key breakthrough during development?
This project came together as a series of incredibly exciting moments, as opposed to one major hurdle we had to overcome. We successively unlocked new levels to the project, leading to many stages of new and exciting problems to solve across almost every dimension in the system. We first figured out how to build and implement the gene editing strategies that serve as the backbone of the PEtracer system. Next, we needed to determine how our single-cell sequencing and imaging readouts might work. Once we had our readouts working, we then moved on to in vivo experiments and deducing the most effective ways to process these novel datasets to understand tumor evolutionary dynamics more deeply.
What was the biggest technical or analytical challenge during development – and how did you overcome it?
This project involved diverse expertise across fields of genome editing, cancer biology, advanced imaging technologies, and computation – and our incredibly talented team had to solve complex problems to develop this technology and generate the beautiful data in the manuscript. These efforts ranged from developing new ways to optimize lineage mark insertion balance and kinetics, to designing novel strategies to preserve RNA integrity from tumor samples. By focusing on the simple experiments and methodically testing the most pertinent variables in each case, we overcame these challenges and provided technically useful insights across these diverse areas – to ultimately develop a tool enabling new biological discoveries.
Could you tell me about the potential impact PEtracer could have – for example, on how we study development, disease progression, or tumor biology?
We are extremely excited about the potential for PEtracer to help us more deeply understand all sorts of biological questions: ranging from how a fertilized egg develops into a complete organism, to how stem cells build and maintain complex tissues, to how tumors grow, evolve, metastasize, and respond to therapies. By engineering the PEtracer components into diverse model systems, we will probe these important questions by charting how the histories of individual cells give rise to phenotypes in health and disease.
What are the next steps for your team?
Our work will push forward in two broad areas: first, we hope to apply the tools we have built across diverse model systems to help answer important problems in biology. Second, we hope to further improve and add capabilities to the technology that enables us to model and understand these diverse problems more precisely.
It’s certainly an exciting time for our group, and we are looking forward to working with experts across domains of biology to tackle some of life’s most interesting and important problems.
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