Details

Autor(en) / Beteiligte

• Travaglini, Kyle J
• Gabitto, Mariano I
• Ariza, Jeanelle
• Ding, Yi
• Mahoney, Joseph T
• Casper, Tamara
• Chakrabarty, Rushil
• Clark, Michael
• Crane, Paul K
• Dee, Nick
• Ferrer, Rebecca
• Gatto, Nicole M
• Gloe, Jessica
• Goldy, Jeff
• Grabowski, Thomas J
• Guilford, Nathan
• Guzman, Junitta
• Hawrylycz, Michael J
• Hodge, Rebecca D
• Jayadev, Suman
• Kaplan, Eitan S
• Keene, C Dirk
• Larson, Eric B
• Latimer, Caitlin S
• Levi, Boaz P
• Melief, Erica J
• Meyerdierks, Emma
• Mukherjee, Shubhabrata
• Pham, Thanh
• Rachleff, Victoria M
• Smith, Kimberly
• Torkelson, Amy
• Lein, Ed S
• Miller, Jeremy A

Titel

Molecular hallmarks of Alzheimer’s disease with a multimodal single cell atlas of the cortex

Ist Teil von

• Alzheimer's & dementia, 2023-12, Vol.19 (S12), p.n/a

Erscheinungsjahr

2023

Quelle

Wiley Online Library All Journals

Beschreibungen/Notizen

• Background The Seattle Alzheimer’s Disease Cell Atlas (SEA‐AD, https://sea-ad.org) is a large‐scale effort to identify, at single cell resolution, the cellular and molecular mechanisms that cause AD and facilitate its progression. SEA‐AD brings together quantitative neuropathology (QNP) derived from classic histopathological and cell type stains with single nucleus transcriptomics, epigenetics, and spatial transcriptomics on a cohort of 84 aged donors that span the pathological and cognitive disease spectrum, including unaffected controls. Method We ordered SEA‐AD donors based on QNP using a Bayesian latent space model to construct a model‐estimated “pseudo‐progression” (PS). We then used a general linear mixed effects model to test for gene expression changes along the PS in each of the BRAIN initiative’s 139 cell types present in SEA‐AD’s single nucleus RNA sequencing dataset (∼1.2 million nuclei) from the middle temporal gyrus. By separately fitting models to donors with lower and higher pathology, we obtained two beta coefficients for each gene that enabled distinction between early, late, and consistent up‐ and down‐regulation along PS. We then tested whether the betas for each gene were different across all cell types or in only certain ones to measure specificity. We curated gene modules from biological processes potentially relevant to AD, identified in gene‐set enrichment analyses and prior studies, and categorized each gene within them based on their dynamics and specificity. Result Roughly one‐third of genes had differential expression across neuronal cell types (mean betas > 0.2), split evenly between up‐ and down‐regulation, while a smaller subset were changed in specific cell types. Modules enriched in broadly changed genes that had increased expression early in PS included those involved with the microtubule cytoskeleton, vesicle adaptors, and neurotransmitter receptors, while those with decreased expression included those involved with cholesterol synthesis, glycolysis, mitochondrial function, and neurofilaments. Among the genes changed only in specific cell types were NOTCH2 in Somatostatin and Parvalbumin interneurons selectively vulnerable to pathology, PTPRG in disease associated microglia, and COL21A1 in disease associated astrocytes. Conclusion Broadly affected modules identified by SEA‐AD suggest most neurons are affected by disease pathology and cell type‐specific differences may provide clues to selective vulnerability.