Plate Nº 71 · recorded October 10, 2026
Biology & EvolutionReported finding
MIT team builds method that lets living cells mail out their own RNA
A Broad Institute and MIT team engineered living cells to release their RNA through virus-like particles, enabling repeated gene-activity tracking in the same cells without killing them.
By James Calloway3 min read590 words
In brief
- A Sept. 1 Broad Institute press release announced the work, published in Cell.
- Senior author Paul Blainey led the project with co-first authors Jacob Borrajo, Mohamad Najia, and Anna Le.
- The method uses a retroviral structural protein to package cellular RNA into virus-like particles that bud into culture medium.
- Researchers tested the approach in immortalized cells, cancer lines, stem cells, neurons, primary donor cells, spheroids, and organ-on-a-chip devices.
- The project began more than a decade ago in Blainey's lab.
A team at the Broad Institute of MIT and Harvard has engineered living cells to ship their own RNA into surrounding culture medium, letting researchers track gene activity in the same cell population over time without killing any cells. A Sept. 1 Broad Institute press release announced the work, which appears in Cell.
Current transcriptome profiling—reading all the RNA a cell produces—requires breaking cells open. Each measurement captures one moment. Tracking change means harvesting fresh cells at each time point.
How does the cellular self-reporting method work?
The team drew on retroviruses, which over millions of years evolved protein shells that package RNA and bud off from infected cells. They engineered mammalian cells to express a retroviral structural protein that captures not only viral RNA but also the cell's own RNA.
The protein forms a virus-like particle around the RNA, exits the cell membrane, and floats into the culture medium. Researchers then sample the medium, isolate the RNA, and sequence it—all without disturbing the cells.
Co-first author Mohamad Najia, a research fellow in the Blainey lab and in George Daley's lab at Boston Children's Hospital, said: "Compared to methods using robotics or mechanical biopsies of cells, our molecularly encoded solution could be much more enabling for the average life science or biomedical lab, particularly the time dynamic questions that we hope to elucidate with this technology."
What cell types did the team test?
The researchers applied the method to immortalized human cells, cancer cell lines, stem cells, neurons derived from stem cells, and primary cells from human donors. They also grew two human cell types together and tagged the virus-like particles so each population's signal could be sorted later.
They then turned to three-dimensional systems that are hard to sample mechanically. On spheroids—small ball-shaped clusters—of human endothelial cells, they tracked transcriptional responses to a biochemical stimulus over short time windows.
With Linda Griffith, a professor of biological and mechanical engineering at MIT, the team tested organ-on-a-chip devices, miniature models that mimic organ physiology. In the chips, the method revealed that endothelial cells changed expression of genes tied to blood-vessel formation depending on whether supporting fibroblasts came from uterus or lung.
What might this enable?
Senior author Paul Blainey, a Broad core member and professor of biological engineering at MIT, framed the work in practical terms. "Our lab focuses our time and resources on developing tools that will actually get used and make real impact on the broader field," he said. "It's so gratifying to see a real coming to fruition of this concept, which was complete science fiction when we started."
He added: "The existing methods were a bit medieval and involved stabbing cells or cutting pieces off of them."
The project began more than a decade ago in Blainey's lab, when he and co-first author Jacob Borrajo committed to a molecular approach they knew would be slow but scalable. Co-first author Anna Le, a postdoc in the Blainey lab, helped lead the work alongside Borrajo and Najia.
For now, the method reports RNA from cell populations rather than single cells, and the team is working toward single-cell resolution. They invite other labs to try the system on questions about how cells and tissues change over time.
Preliminary tests suggest broad applicability across cell types, but broader validation in more tissues and longer time courses will be needed before the approach can settle into routine use.
via cell.com (Original)
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Staff writer covering marketplaces and e-commerce at SciBeat.
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