Virus-Like Particles Enable Tracking Gene Activity Over Time in Living Cells
- Researchers at the Broad Institute and MIT have developed a live-cell transcriptomic method that uses virus-like particles to track gene activity over time in the same cells without...
- Traditional methods for measuring a cell's transcriptome—the complete set of RNA transcripts produced by genetic code—require killing the cell to access internal molecules.
- The research team validated the method across a diverse array of cellular model systems to prove its broad applicability.
Researchers at the Broad Institute and MIT have developed a live-cell transcriptomic method that uses virus-like particles to track gene activity over time in the same cells without destroying them. Described in the journal Cell, the cellular self-reporting approach allows living cells to package and deliver their own RNA to surrounding culture media for repeated sampling and sequencing.
Developing the Cellular Self-Reporting Method
Traditional methods for measuring a cell’s transcriptome—the complete set of RNA transcripts produced by genetic code—require killing the cell to access internal molecules. According to study senior author Paul Blainey, existing techniques were medieval and involved stabbing cells or cutting pieces off them. Blainey is a core member of the Broad and a professor of biological engineering at MIT.
To replace these destructive practices, Blainey and study first author Jacob Borrajo sought inspiration from retroviruses. Over millions of years, retroviruses evolved protein shells to package and spread RNA genomes between cells. The MIT and Broad Institute team engineered mammalian cells to express a retroviral structural protein that recruits cellular RNA, forms a shell around it, and buds off the cell membrane as a virus-like particle.
Scientists can collect a sample of the surrounding liquid culture medium, isolate the RNA inside the virus-like particles, and sequence it. This reveals the transcriptome of a cell population repeatedly over time. Our lab focuses our time and resources on developing tools that will actually get used and make real impact on the broader field,
Blainey said, according to reports from the Broad Institute.
Testing Across Multiple Model Systems
The research team validated the method across a diverse array of cellular model systems to prove its broad applicability. The technique successfully functioned in immortalized human cells, cancer cell lines, stem cells, neuronal cells made from them, and primary cells from human donors. Researchers also tested cultures containing two human cell types growing together. By applying tags to the virus-like particles, the team successfully distinguished signals from both cell types during analysis.
Implications for Biomedical Research and Drug Discovery
The newly unveiled technology aims to help scientists understand how cells change as they mature or respond to external perturbations. Researchers can apply the tracking method to observe how cells go awry over time in disease and how drugs impact cell populations.
Compared to methods using robotics or mechanical biopsies of cells, our molecularly encoded solution could be much more broadly enabling for the average life science or biomedical lab, particularly for the time dynamic questions that we hope to elucidate with this technology,
stated co-first author Mohamad Najia, a research fellow in the Blainey lab and the lab of George Daley at Boston Children’s Hospital.
The project was led by co-first authors Jacob Borrajo and Mohamad Najia alongside co-first author Anna Le, a postdoc in the Blainey lab. The development marks the culmination of more than a decade of research aimed at making RNA sequencing non-destructive, scalable, and accessible for general life science laboratories.
