Diseases such as cancer and neurodegenerative disorders often begin with genetic mistakes. But even after scientists identify the genes involved, turning that knowledge into effective treatments has remained extremely difficult. Many of these diseases are linked to hundreds of mutations spread across different biological pathways, making it hard to understand how they collectively drive disease.
A new study published in Nature introduces a potential solution. Researchers created a platform called PerturbFate that can systematically track how disease related genetic changes alter cells and identify where those changes ultimately converge. By observing gene regulation in single cells over time, the team uncovered shared regulatory hubs that many different mutations depend on. Using melanoma drug resistance as a test case, the researchers showed that targeting these common control points could help overcome resistance across multiple genetic causes.
“We focus here on cancer drug resistance, but the paper really starts from a broader question: once you know that a disease is associated with hundreds of genes, how do you design one therapy to target it?” says Junyue Cao, head of the Laboratory of Single-Cell Genomics and Population Dynamics. “We wondered whether all these different genes may be mediated by some shared downstream signaling that we can discover and target instead.”
A Growing Challenge in Genetic Medicine
Advances in genetic sequencing and screening technologies have allowed scientists to identify large numbers of disease linked mutations. However, this progress has created a major new challenge. The genes involved in disease often perform very different jobs inside cells, including controlling gene activity and managing cell signaling pathways. Because of this complexity, designing treatments that address many mutations at once has been difficult.
Cao suspected that these seemingly unrelated mutations might not actually act independently. Instead, they could funnel into shared downstream programs that ultimately determine how cells behave. If that were true, scientists would not need to target every mutation separately. They could focus on common regulatory nodes that control the disease process.
“We wanted to develop a technology to identify these shared regulatory nodes as targets in and of themselves,” says Cao.
To accomplish that, the team needed a system capable of comparing many genetic disruptions at the same time while monitoring how each one reshaped a cell in detail. Existing technologies could only capture part of the picture, often measuring one layer of cellular activity at a time or missing how gene activity changes dynamically over time.
Graduate student Zihan Xu developed PerturbFate to overcome those limitations. The platform enables researchers to observe how different genetic disruptions alter cells in real time by simultaneously tracking DNA accessibility and RNA production. Because these measurements are collected within the same single cell, the system can reveal the gene networks controlling cell behavior and identify where distinct mutations produce the same downstream effects.
“This technology lets us perturb hundreds to thousands of genes in parallel and then measure the detailed molecular changes in each individual cell,” says Cao. “That allows us to link many different genetic perturbations to their downstream effects and identify regulatory nodes.”
Tracking Drug Resistance in Melanoma
To test the platform, the researchers turned to melanoma, where many different mutations can produce resistance to treatment. The team selected 143 genes previously associated with resistance to the melanoma drug Vemurafenib and systematically disabled them in melanoma cells.
PerturbFate then monitored how each disruption changed cellular behavior over time. By labeling newly produced RNA, the researchers could separate fresh gene activity from older molecular signals. Single cell profiling also allowed them to track which genes were active, which regions of DNA became accessible, and how those changes evolved.
This detailed approach gave scientists a cell by cell view of how different mutations influence gene regulation and where those pathways eventually converge.
“We’re capturing not just gene expression, but also RNA dynamics and chromatin state,” says Cao. “That’s critical for identifying the upstream regulators that drive these disease states.”
Xu also created a computational analysis pipeline that combined all these layers of information into detailed gene regulatory networks. The system connected early changes in transcription factor activity with later shifts in DNA accessibility, RNA production, and stable gene expression patterns.
After examining more than 300,000 cells, the researchers found that many different mutations consistently pushed melanoma cells into the same drug resistant condition. When the team targeted the shared regulatory control points driving that state, drug resistance dropped significantly, suggesting a promising strategy for combination therapies.
A Shared Survival Signal
The study also uncovered an important detail involving the Mediator Complex, a cellular structure that helps regulate gene activity. Researchers found that disrupting different parts of this same complex could trigger drug resistance through entirely different biological routes. Despite those differences, the pathways still converged on the same melanoma survival signal known as VEGFC.
When researchers blocked VEGFC, the resistant melanoma cells were no longer able to grow.
The findings suggest that even highly complex genetic diseases may rely on shared vulnerabilities that can be therapeutically targeted. Rather than designing separate treatments for every mutation, scientists may be able to focus on common regulatory pathways that multiple mutations depend on.
Expanding Beyond Cancer
The researchers have made both the laboratory and computational tools behind PerturbFate publicly available. They now plan to expand the approach beyond cultured cells and apply it to living systems.
Cao and his colleagues hope to use the technology to study conditions such as aging and Alzheimer’s disease, both major research areas in his lab. Their goal is to uncover shared weaknesses across complex diseases that could guide the development of more effective therapies.
“This is just a starting point,” says Cao. “Now that we’ve demonstrated the approach in a simple model, we’re working to extend it into living systems to study even more complex diseases.”