For a study published in Genome Biology, a collaborative team at the Salk Institute analyzed skin cells ranging from the very young to the very old and looked for molecular signatures that can be predictive of age. Developing a better understanding of the biological processes of aging could eventually help to address health conditions that are more common in old age, such as heart disease and dementia.
A human fibroblast cell line was derived from a skin biopsy. To confirm cell identity, staining was performed for a fibroblast marker (SERPINH1, magenta), nuclear pore complexes (Nup153, yellow) and DNA (DAPI, blue).
"This experiment was designed to determine whether there are molecular signatures of aging across the entire range of the human life span," says co-senior author Saket Navlakha, an assistant professor in Salk's Integrative Biology Laboratory. "We want to develop algorithms that can predict healthy aging and non-healthy aging, and try to find the differences."
"The study provides a foundation for quantitatively addressing unresolved questions in human aging, such as the rate of aging during times of stress," says Prof. Martin Hetzer, co-senior author, as well as Salk's vice president and chief science officer.
The researchers focused on a type of skin cell called dermal fibroblasts, which generate connective tissue and help the skin to heal after injury. They chose this type of cell for two reasons: first, the cells are easy to obtain with a simple, non-invasive skin biopsy; second, earlier studies indicated that fibroblasts are likely to contain signatures of aging. This is because, unlike most types of cells that completely turn over every few weeks or months, a subset of these cells stays with us our entire lives.
The investigators analyzed fibroblasts taken from 133 healthy individuals ranging in age from 1 to 94. To get a representative sample, the team studied an average of 13 people for each decade of age. The lab cultured the cells to multiply, then used a method called RNA sequencing (RNA-Seq) to look for biomarkers in the cells that change as people get older. RNA-Seq uses deep-sequencing technologies to determine which genes are turned on in certain cells. Using custom machine-learning algorithms to sort the RNA-Seq data, the team found certain biomarkers indicating aging, and were able to predict a person's age with less than eight years error on average.
"We took a 'kitchen sink' approach with this project," says first author Jason Fleischer, a Salk postdoctoral fellow. "Rather than going into this research with an idea of what we wanted to find, we decided to look at the changes in expression of all the protein-coding genes and let the algorithms sort it out. We used what's called an ensemble machine-learning method to do this."
The analysis from the Salk team was different from earlier approaches taken by other labs to study biological aging. Most previous studies focused on changes at only a few DNA methylation sites, rather than looking at changes of expression on the whole genome. The dataset was also much larger than any research of this type that has ever been done before, because it included so many people representing a range of decades. The researchers have made the data public so that other investigators can use it.
To validate the algorithm, the team also used fibroblasts from 10 patients with progeria, a genetic disease characterized by accelerated aging. Based on analysis of the molecular signatures from these patients, who ranged in age from two to eight, the model predicted them to be about a decade older than their calendar age. "The fact that our system can predict this kind of aging shows that this model is starting to get at the true underpinnings of biological age," Fleischer says.