A technology developed by Cynthia McMurray, a biochemist senior scientist in the Molecular Biophysics and Integrated Bioimaging (MBIB) Division, and her team shows great promise for diagnosing Alzheimer disease (AD) before symptoms arise. This disease affects millions of people worldwide and is estimated to be the sixth leading cause of death in the United States.
“This is a big deal,” said McMurray, following the publication of the team’s successful proof-of-principle study in the journal Scientific Reports. “Diagnosing Alzheimer disease at early stages is difficult and there is no way to predict who will get the disease, which means there is no successful pathway to develop therapeutics. However, this new technology uses accessible skin cells as surrogates to predict the disease status in the brain. We’re very excited for the possibilities of early prediction, before signs of disease have manifested.”
The team used a technique called spectral phenotyping to analyze cells for signs of disease by measuring how the molecules in cells vibrate upon exposure to infrared light. The vibrational profile of each sample is so distinct and the difference between diseased and healthy cell samples is so visible that McMurray likens the process to “cellular fingerprinting.”
The subtle changes are captured by infrared (IR) spectroscopy and datasets called spectra are produced. Ben Brown, a computational biologist staff scientist in the Environmental Genomics and Systems Biology (EGSB) Division, worked on the team to develop the machine learning algorithms that helped to differentiate between spectra of cells from individuals with disease and those without.
IR spectroscopy has been around since the 1940s, but has largely remained unpopular in this field of study. McMurray and her team combined this analysis with up-to-date computing techniques to yield a new application. In the Scientific Reports study, McMurray, Brown, and colleagues confirmed the diagnostic potential of their approach by showing that an algorithm can easily distinguish IR spectra from mouse brain cells with Huntington disease from spectra of healthy mouse brain cells. Then, they trained an algorithm to do the same with human cells. It worked seamlessly.
The next test was more challenging: Could spectral phenotyping diagnose AD against age-matched controls using easily accessible cells instead of brain cells? They chose fibroblasts, an extremely common cell found in the skin and other connective tissue.
The team is now in the middle of a follow-up study to evaluate their spectral phenotyping approach on a larger set of AD patients and controls. Early results on a handful of samples from presymptomatic patients – who later developed AD – indicate that the technology can spot AD before symptoms develop. If this holds true in future validation trials, spectral phenotyping will, at long last, provide a window of time for patients to try experimental medicines that could delay or even stop disease progression.
Additional Biosciences Area contributors to this work include: postdoctoral scholar Lila Lovergne, research scientist Aris Polyzos, and project scientist Renaud Schuck from MBIB, and graduate student Andrew Chen and student assistant Dhruba Ghosh from EGSB.
Read more in the Berkeley Lab News Center.