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New method allows scientists to follow gene activity over time in the same cells

In the new method, cells package and export their RNA, enabling researchers to sequence and analyze the RNA without killing the cells.

New method allows scientists to follow gene activity over time in the same cells

The Broad Institute of MIT and Harvard, in collaboration with researchers at MIT, has developed an innovative method to observe gene activity in living cells over time. This new approach, called "cellular self-reporting," allows scientists to analyze a cell's transcriptome, or all the RNA produced by a cell, without the need to kill the cells.

Previously, methods for studying cells relied on killing them to access the RNA, providing only a single snapshot of the cell's activity. The new cellular self-reporting method, however, utilizes virus-like particles produced by the cells, which are used to package and deliver RNA to the surrounding culture medium. Scientists can simply sample this medium to isolate the RNA, and repeat the process as needed to monitor how gene activity in the same cell population changes over time.

The researchers tested the method in various cellular model systems, including immortalized human cells, cancer cell lines, stem cells, neuronal cells, and primary cells from human donors. They also demonstrated the method's potential to study complex systems, such as endothelial cells within organ-on-a-chip devices, helping to reveal changes in genes related to tissue formation and vascular networks.

Paul Blainey, senior author and professor of biological engineering at MIT, expressed his excitement about the practical impact of this research, stating that the method could enable a more widespread use of RNA sequencing in life science and biomedical labs. Co-first author Mohamad Najia and Anna Le led the work in developing the method, which they believe could significantly advance our understanding of how cells and tissues change over time in various biological contexts.

Written by urgent.news from MIT News Research's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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