Provide a web portal for convenient retrieval of omic profiles.
We seek to create tools to increase accessibility and reusability of multi-omic data collected by the lab and to facilitate easier and more reliable access to data via decentralized storage. Our current browsers and data repositories are available on our Resources Page.
Single-cell, transcriptomic, metabolomic, and proteomic browsers and web portals are currently in the works.
Develop a cellular atlas for AD by characterizing gene expression and epigenome at single-cell resolution.
We are currently working on developing a cellular atlas for AD by working with data at the single-nucleus level. We are also working to identify glial and neuronal pathways that are altered in AD and the changes in cross talk between those cells.
snRNAseq and snATACseq data is being used to identify transcriptional states that are either enriched or depleted in carriers of AD genetic factors, such as variations in APP, PSEN1, TREM2 and MS4A.
Multi-modal molecular characterization of AD.
We use machine learning to analyze cross-omics data, and to create a complex high-dimensional profile of changes in AD cellular environments. We can then apply the data gained from cross-omics analysis to categorize different AD subtypes.
This multi-modal characterization of AD will allow us to work to determine downstream effects of AD genetic factors.