A new breakthrough from researchers at Rice University could unlock the genetic components that determine several human diseases such as Parkinson's and Alzheimer's.

Alzheimer's disease affected 57 million people worldwide in 2021, and cases in the United States are expected to double in the next couple of decades. Despite its prevalence and widespread attention of the condition, the full mechanisms are still poorly understood. One hurdle has been identifying which brain cells are linked to the disease.

For years, it was thought that the cells most linked with Alzheimer's pathology via DNA evidence were microglia, infection-fighting cells in the brain. However, this did not match with actual studies of Alzheimer's patients' brains. It's the memory-making cells in the human brain that are implicated in the pathology.

To prove this link, researchers at Rice alongside Boston University developed a computational algorithm called “Single-cell Expression Integration System for Mapping genetically implicated Cell types," or SEISMIC. It allows researchers to zero in on specific neurons linked to Alzheimer's, the first of its kind. Qiliang Lai, a Rice doctoral student and the lead author of a paper on the discovery published in Nature Communications, believes that this is an important step in the fight against Alzheimer's.

“As we age, some brain cells naturally slow down, but in dementia ⎯ a memory-loss disease ⎯ specific brain cells actually die and can’t be replaced,” said Lai. “The fact that it is memory-making brain cells dying and not infection-fighting brain cells raises this confusing puzzle where DNA evidence and brain evidence don’t match up.”

Studying Alzheimer's has been hampered by the limitations of computational analysis. Genome-wide association studies (GWAS) and single-cell RNA sequencing (scRNA-seq) map small differences in the DNA of Alzheimer's patients. The genetic signal in these studies would often over-emphasize the presence of infection fighting cells, essentially making the activity of those cells too "loud" statistically to identify other factors. Combined with greater specificity in brain regional activity, SEISMIC reduces the data chatter to grant a clearer picture of the genetic component of Alzheimer's.

“We built our seismic algorithm to analyze genetic information and match it precisely to specific types of brain cells,” Lai said. “This enables us to create a more detailed picture of which cell types are affected by which genetic programs.”

Though the algorithm is not in and of itself likely to lead to a cure or treatment for Alzheimer's any time soon, the researchers say that SEISMIC is already performing significantly better than existing tools at identifying important disease-relevant cellular signals more clearly.

“We think this work could help reconcile some contradicting patterns in the data pertaining to Alzheimer’s research,” said Vicky Yao, assistant professor of computer science and a member of the Ken Kennedy Institute at Rice. “Beyond that, the method will likely be broadly valuable to help us better understand which cell types are relevant in different complex diseases.”