New method allows tracking genetic activity of living cells without destroying them

Researchers from the Broad Institute and MIT have developed a method to repeatedly analyze the genetic activity of living cells using virus-like particles that export RNA, without destroying the samples.

The method allows for successive sampling of RNA from the same cell population and observing how it changes over time. The technique worked on human cells, cancerous cells, stem cells, neuronal cells, primary cells, and three-dimensional models.

Researchers from the Broad Institute of MIT and Harvard and the Massachusetts Institute of Technology developed a method that allows tracking the genetic activity of living cells over successive periods without having to destroy them to access their RNA. This advancement seeks to address a central limitation of conventional transcriptomics: each analysis typically provides an isolated snapshot because the cell must be broken during sample preparation.

The technique, described in the journal Cell, turns the cells themselves into a kind of molecular self-reporting system. Instead of physically extracting the genetic material, researchers design them to package part of their RNA into virus-like particles and release it into the culture medium, where it can be collected and sequenced.

This possibility is particularly relevant for studying processes that change over time, such as cell maturation, response to a disturbance, or the effect of a drug. A sample of the medium can be taken repeatedly, allowing scientists to compare different moments of the same population and reconstruct how its genetic activity evolves.

The work was led by Mohamad Najia and Jacob Borrajo, along with lead co-author Anna Le, a postdoctoral researcher in Paul Blainey's lab. Blainey, a senior member of the Broad and a professor of biological engineering at MIT, participated in the development of the project, which aimed to create useful tools for other research groups. The team considered that the realization of this idea represented the outcome of a high-risk project initiated more than a decade ago.

The foundation of the system is inspired by retroviruses, which evolved the ability to wrap their RNA genomes in protein coats to move them between cells. The team designed mammalian cells to express a retroviral structural protein capable of recruiting cellular RNA, forming a coat around that material, and shedding from the membrane into the fluid surrounding the cell.

Once the particles are released, scientists only need to take a sample of the culture medium, isolate the RNA, and sequence it. The result provides a representation of the transcriptome of the cell population without resorting to procedures that destroy it or significantly alter its immediate environment.

The proposal aims to overcome the limitations of alternatives based on robotics, mechanical biopsies, or individual cell manipulation, which can be difficult to scale in common laboratories. Mohamad Najia explained that a molecularly encoded solution could facilitate the study of dynamic and temporal questions in life sciences and biomedical research laboratories.

The system still does not equate to a perfect reading of each individual cell, as the signals obtained correspond mainly to the analyzed population. However, the Broad team is working to adapt the approach to the study of individual cells, an evolution that could increase the resolution of the method and help distinguish subpopulations with different behaviors.

To evaluate the breadth of the technique, researchers tested it on immortalized human cells, cancer cell lines, stem cells, and neuronal cells derived from them. They also used primary cells obtained from human donors, which allowed them to verify that the system was not limited to laboratory cell models with very specific characteristics.

The team also cultured two types of human cells simultaneously and used virus-like particle tags to distinguish the signals from each population during analysis. This capability could help study interactions between cells, as long as researchers can clearly identify the origin of the RNAs that reach the medium.

Another demonstration was conducted on human endothelial cell spheroids, three-dimensional structures where spatial architecture can be important for interpreting genetic activity. After biochemical stimulation, the method allowed capturing short-term transcriptional changes without dismantling the model to extract its cells.

The researchers also collaborated with Linda Griffith, a professor of biological and mechanical engineering at MIT, to test the system in organ-on-a-chip devices. In these models, cellular self-reporting allowed monitoring over time the expression of genes related to vascular network formation, whose changes depended on whether the supporting fibroblasts came from the uterus or the lung.

Organ-on-a-chip devices aim to mimic aspects of human physiology and can help reduce the need for preclinical testing or experiments with animal models. However, their complexity makes it difficult to extract cells without disturbing the structure of the device, so a tool that collects signals from the medium could provide information without dismantling the system.

The same principle could be used to observe how a cell population responds to drugs, biological stress, or maturation signals. Instead of comparing destroyed samples from different groups, researchers could track successive changes in a population and relate them to the exact moment of an intervention.

The technique also offers a way to investigate diseases where temporal evolution is crucial, although the statement does not yet present a clinical application or claim that the method is ready for use in patients. Its results correspond to experimental demonstrations in cellular systems and laboratory models, so it will be necessary to study its accuracy, stability, and behavior in more complex contexts.

Blainey's lab continues to seek new biological applications and works to make the study of individual cells feasible. For now, the advancement proposes a different way to understand transcriptomics: instead of forcing the cell to reveal its content through destruction, it allows the cell to periodically send a molecular signal about what is happening inside it.

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