| Autor: |
Gabitto, Mariano I, Travaglini, Kyle J, Ariza, Jeanelle, Close, Jennie, Ding, Yi, Long, Brian, Rachleff, Victoria M, Chakrabarty, Rushil, Crane, Paul K, Ferrer, Rebecca, Gatto, Nicole M, Goldy, Jeff, Grabowski, Thomas J, Guilford, Nathan, Guzman, Junitta, Hawrylycz, Michael J, Hodge, Rebecca D, Jayadev, Suman, Kaplan, Eitan S, Keene, C Dirk |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2023 Supplement 12, Vol. 19, p1-2, 2p |
| Abstrakt: |
Background: Alzheimer's disease (AD) is the most common form of dementia. The progression of AD throughout the brain follows a stereotypical pattern that can be described by the quantification of histopathological changes in several brain regions. As part of The Seattle Alzheimer's Disease Cell Atlas (SEA‐AD, https://sea-ad.org), we previously quantified pathological proteins in the middle temporal gyrus (MTG) and used them to describe disease progression, vulnerable cell types, and the MTG molecular changes related to disease Method: In this work, we expanded our results and undertook a comparative approach. We measured a battery of neuropathological proteins (staining NeuN, AT8, IBA1, GFAP, 6e10, pTDP43, and a‐Syn immunoreactivity, and analyzing them with machine learning) in the middle temporal gyrus, the middle frontal gyrus, and medial entorhinal cortex in 84 donors. In addition, we undertook a multimodal single‐cell analysis of such regions, profiling cells using single‐cell RNA‐seq, ATAC‐seq, Multiome, and spatial transcriptomic. Borrowing from our hierarchically resolved cell types in MTG, based on the BRAIN initiative cell type reference, we used machine learning algorithms to create multimodal cell type maps across regions. We used Bayesian statistics to identify differentially accessible regulatory elements, link them to the affected genes and the regulatory transcriptional machinery binding in such elements. Result: We identified regional vulnerable neuronal populations and a core set of transcriptionally similar cell types susceptible to disease across the cortex. We created a cortex‐wide disease progression map combining neuropathological data from our three profiled regions with latent Bayesian statistical algorithms. In each area, we developed an AD pseudo‐progression timescale that describes the burden of disease in the area and, next, combined all areas to create a cortex‐wide holistic pseudo‐progression. We used the mapped cell types and our developed scale to synchronize changes occurring in each patient, describing abundances, gene expression, and chromatin accessibility modifications. Conclusion: The progression scale developed in this study serves as a reference to align changes across brain regions and, in the future, to reconcile differences across cohorts. [ABSTRACT FROM AUTHOR] |
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