50,000 Genes, One Research Community

Earlier this year, Dr. Melissa Murray contributed bulk RNAseq hippocampus data to the AD Knowledge Portal through the Community Data Contribution Program. The data are part of the Mayo Clinic Hippocampal Vulnerability Study (MayoHippocampus), which provides bulk RNAseq data from the hippocampus of 40 donors with Alzheimer’s disease and 15 nondemented controls from the Mayo Clinic brain bank. 

MayoHippocampus is one of the largest publicly available human hippocampus datasets, and includes covariates such as race, age of death, Braak, sex, APOE genotype, and a series of neuropathologic variables. Supported by a 2014 New Vision Research Investigator Award, the research provides information on more than 50,000 genes from the memory center of the brain.

Dr. Murray, trained as a translational neuropathologist, carefully selected donors from the Mayo Clinic brain bank, considering the age of the sample, the quality of the tissue, and other diseases donors may have had. “I was able to whittle it down to a very narrow set of Alzheimer’s disease cases without coexisting pathologies,” she said, “so we can feel a lot more comfortable that the results are more Alzheimer’s specific.”

Dr. Melissa Murray

She then dissected the hippocampus, a meticulous process that discourages many researchers from studying it. It’s a region of the brain often impacted by Alzheimer’s, but relatively spared in some individuals. Dr. Murray wanted to leverage this heterogeneity, strategically examining the selective vulnerability of the hippocampus.

To measure this selective vulnerability, she used RNA sequencing, a technique to help quantify gene expression changes. This process may help researchers understand why some people’s memory center is quite small and other people’s memory center has been relatively preserved.

By sharing this sequencing data through the AD Knowledge Portal, Dr. Murray is enabling the entire research community to use it for validation and secondary data research, with the ultimate goal of discovering disease-relevant targets for future functional and translational studies.

“We used a really cool translational neuropathology approach with machine learning applied to prioritize genes of interest,” she explains, referring to the team’s recent publication to identify genes associated with AD neuropathology, “but that’s just one use. We’re contributing the data so the entire research community can implement other unique approaches, too.”