A loved one’s diagnosis moved this UCLA Health researcher to take on ALS

Computational geneticist developed a blood test that could detect how fast muscle cells are dying, potentially aiding diagnosis.

After a parent is diagnosed with an incurable disease, it’s natural to feel an urge to do anything possible to help. For some people, that could be advocacy, or household assistance, or tracking down the best available medical care.

For Noah Zaitlen, Phd, associate professor of computational medicine and neurology at UCLA Health, it meant launching a research project to create a better diagnostic test for a debilitating neurological disease.

In 2016, Zaitlen’s father was diagnosed with amyotrophic lateral sclerosis (ALS), a disorder that attacks the brain and spinal cord. The disease causes loss of muscle control and gets progressively worse over time, until patients eventually lose the ability to speak, swallow and breathe.

Five years later, Zaitlen and his colleagues have published a new algorithm they call “CelFiE” that analyzes DNA circulating in the bloodstream. By detecting certain chemical patterns in the DNA, CelFiE estimates what proportion of the circulating DNA came from different cell types. In this way, CelFiE can monitor whether muscle cells are dying off, releasing their DNA into the bloodstream, which could be a sign of ALS.

Dr. Noah Zaitlen, an associate professor of computational medicine and neurology at UCLA Health, began researching a better way to detect ALS after his father was diagnosed with the disease in 2016. (Photo courtesy of Dr. Noah Zaitlen)

The report was published May 11 in the journal Nature Communications.

ALS can be difficult to diagnose. There is no lab test to rule the disease in or out, and doctors rely on the patient’s description of symptoms coupled with physical and neurological exams. Symptoms, however, start out mild and are often ambiguous.

“I wanted to see what I could do, and I started looking around for collaborators and projects,” Zaitlen says. “I ended up hearing about this issue with diagnosis, and it was something I understood, because it took a long time to get my dad diagnosed.”

Zaitlen found out about a technique some researchers use to analyze DNA found in the bloodstream and to identify what type of cell it came from. Most DNA exists inside the nucleus of cells, but when cells die, they can release their DNA into the blood. This DNA is called “cell-free DNA,” because unlike most DNA, it’s floating around loose.

“We were wondering if we could find some sort of signature in the blood for ALS,” said Christa Caggiano, the graduate student in Zaitlen’s lab who led the project. “When you take a blood draw of the cell-free DNA, you don’t really know where it comes from. We wanted to develop an algorithm that used cell-free DNA sequence data to estimate how much was coming from each tissue in the body.”

Simply sequencing the cell-free DNA isn’t informative, because every cell in the body has the same DNA sequence. Where cell types differ is in their gene expression pattern, because cells from different organs — muscle versus bone, say — turn different genes on and off.

Cells deactivate the genes they don’t need by making a small chemical change, called methylation, to the DNA. Each cell type has a distinctive pattern of methylation throughout the genome, but it can be hard to detect. For one thing, the amount of cell-free DNA is tiny, and in addition, some cell types account for a very small fraction of the total.

The team had to work through a lot of math to find the best way to separate the signal from the background.

“There were other kinds of noise in the sequencing that were hard for us to solve,” Zaitlen said. “It took a couple of years for us to arrive at an approach that we were happy with, that seemed to be working very well.”

The system takes in the methylation data from the sample in question and compares that with a panel of known cell types. A key feature of CelFiE is that it can account for “unknown” cell types that turn up in the sample but aren’t in the reference panel. Being able to include unknown cell types helps ensure that the test returns the correct proportions for the known cell types.

Overall, the process has been a new adventure for the group, which has mostly focused on designing computational methods to analyze existing data sets. For this project, the lab dove into uncharted territory, forming collaborations to recruit patients, collect blood samples, and run experiments.

“Our lab had never worked on ALS before or cell-free DNA,” said Caggiano. “It’s unique for the Zaitlen lab in the fact that it was very disease-focused.”

The work relied on key collaborators Catherine Lomen-Hoerth, who runs the ALS clinic at UCSF and collected all the patients, and Barbara Celona, molecular and cell biologist at UCSF School of Medicine, who did all the experimental work. The project was funded by a Weill Award from UCSF and ALS Finding a Cure.

Learn more about the work being done in UCLA Health's Department of Neurology.

Caroline Seydel is the author of this article.


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