Diseases such as cancer and neurodegenerative diseases often begin with genetic mistakes. But even after scientists identify the genes involved, it remains extremely difficult to turn that knowledge into effective treatments. Many of these diseases are associated with hundreds of mutations spread across different biological pathways, making it difficult to understand how they collectively cause the disease.
New research published in nature Here are some potential solutions. Researchers have created a platform called PerturbFate that can systematically track how disease-related genetic changes change cells and identify where those changes ultimately converge. By observing gene regulation in single cells over time, the researchers uncovered a common regulatory hub on which many different mutations depend. Using drug resistance in melanoma as a test case, researchers showed that targeting these common control points could help overcome resistance across multiple genetic causes.
“While we’re focused here on cancer drug resistance, this paper actually starts with a broader question: Once we know that a disease is associated with hundreds of genes, how do we design treatments to target it?” said Junyue Cao, director of the Single Cell Genomics and Population Dynamics Laboratory. “We wondered if all these different genes were mediated by shared downstream signaling that could be discovered and targeted instead.”
Growing challenges in genetic medicine
Advances in gene sequencing and screening technology have enabled scientists to identify numerous mutations associated with the disease. However, this progress has created major new challenges. Genes involved in diseases often have very different functions within cells, such as controlling gene activity and managing cell signaling pathways. This complexity has made it difficult to design treatments that address many mutations at once.
Cao suspected that these seemingly unrelated mutations did not actually function independently. Instead, they can focus on the shared downstream programs that ultimately determine how the cell behaves. If that were true, scientists wouldn’t have to target every mutation individually. They may be able to focus on common regulatory nodes that control disease processes.
“We wanted to develop technology that would identify these shared regulatory nodes as targets in their own right,” Cao says.
To accomplish that, the team needed a system that could compare many gene disruptions simultaneously, closely monitoring how each one reshapes cells. Existing techniques can 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 these limitations. Using this platform, researchers can observe in real time how different gene disruptions change cells by simultaneously tracking DNA access and RNA production. Because these measurements are collected within the same single cell, the system can reveal the genetic networks that control cell behavior and pinpoint where different mutations produce the same downstream effects.
“This technique allows us to perturb hundreds to thousands of genes in parallel and measure detailed molecular changes in individual cells,” Cao says. “This allows us to connect many different genetic perturbations with their downstream effects and identify control nodes.”
Tracking drug resistance in melanoma
To test the platform, the researchers focused on melanoma. In melanoma, various mutations can create resistance to treatment. The research 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 perturbation changed the cell’s behavior over time. By labeling newly produced RNA, the researchers were able to separate new gene activity from old molecular signals. Single-cell profiling also allowed them to track which genes were active, which regions of DNA were made accessible, and how those changes evolved.
This detailed approach has allowed scientists to understand, cell by cell, how different mutations affect gene regulation and where those pathways ultimately converge.
“We’re not just capturing gene expression, but also RNA dynamics and chromatin state,” Cao says. “This is important for identifying the upstream regulators that drive these pathologies.”
Xu also created a computational analysis pipeline that combines all these layers of information to build detailed gene regulatory networks. This system correlated early changes in transcription factor activity with later changes 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 drive melanoma cells to the same drug-resistant state. When the research team targeted common regulatory control points that drive the condition, drug resistance was significantly reduced, suggesting a promising strategy for combination therapy.
shared survival signals
The study also revealed important details about the Mediator complex, a cellular structure that helps regulate gene activity. Researchers have found that disrupting different parts of this same complex can cause drug resistance through completely different biological pathways. Despite these differences, the pathways still converged on the same melanoma survival signal known as VEGFC.
When researchers blocked VEGFC, resistant melanoma cells were unable to proliferate.
The findings suggest that even the most complex genetic diseases may rely on common vulnerabilities that can be targeted for treatment. Rather than designing individual treatments for every mutation, scientists may be able to focus on common regulatory pathways that multiple mutations rely on.
Expanding beyond cancer
The researchers published both the experimental and computational tools behind PerturbFate. They now plan to extend this approach beyond cultured cells and apply it to biological systems.
Cao and his colleagues hope to use this technology to study aging and conditions such as Alzheimer’s disease, which are the lab’s main research areas. Their goal is to uncover common weaknesses in complex diseases that could lead to the development of more effective treatments.
“This is just a starting point,” Cao said. “Having demonstrated this approach in a simple model, we are now working to extend it to biological systems to study more complex diseases.”
