Mapping Alzheimer's neuropathology signatures to the whole brain transcriptome using machine learning data fusion
Bhattacharya, A.; Savignac, C.; Hodgson, L.; Stanley, J.; Wolf, G.; Krishnaswamy, S.; Bennett, D. A.; Binder, E. B.; Bzdok, D.
Show abstract
In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patterns. Yet, a systematic bias of single-cell genomics toward sampling mostly cortical tissue limits our understanding of the whole-brain transcriptomic vulnerability to AD. Here, we develop a machine learning method to extrapolate local AD neuropathology signatures to the whole brain. By analyzing gene expression profiles of over two million cortical cells from 427 humans spanning the AD-pathology spectrum, we derive transcriptomic estimators of AD neuropathology. After extensive validations on datasets with known ground truth, we apply this framework to three million cells from 108 brain regions in the Siletti whole human brain atlas and derive an anticipated brain map of transcriptomic signatures indexing AD neuropathology. This interrogation of regions spanning the cortical, subcortical, and brainstem structures uncovers transcriptomic signatures associated with hyperphosphorylated tau in the medulla oblongata, dorsal raphe nucleus, and the tuberal and mammillary regions of the hypothalamus. At the cellular level, assessments of these signatures across 31 cell populations identify VGLUT1/2 expressing neurons, astrocytes, and microglia as key neuropathology-resembling populations. Within the hippocampus, pathology signatures surface in the rostral cornu ammonis (CA) subfields, particularly in the CA1 pyramidal neurons and dentate granule cells. {beta}-amyloid-like signatures localize to the neocortex with laminar selectivity--most prominently in upper layer somatostatin+ intratelencephalic neurons (L2-L3), but also in deep layer intratelencephalic and corticothalamic neurons (L5-L6). Neocortical astrocytes and microglia exhibiting disease associated signatures similarly demonstrate a unique laminar preference. Together, this study provides the first whole human brain map of AD pathology-associated transcriptomic signals, and exposes cell type, region, and cortex layer specific vulnerabilities.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveals modulators of memory function 94%
- Reconstructed Cell-Type Specific Rhythms in Human Brain link Alzheimer's Pathology, Circadian Stress, and Ribosomal Disruption 94%
- Glucose hypometabolism and hyperphosphorylated Tau synergistically drive neuronal necroptosis 93%
Similar papers in this journal
Similar papers in this journal
- Amyloid Beta Glycation Induces Neuronal Mitochondrial Dysfunction and Alzheimers Pathogenesis via VDAC1-Dependent mtDNA Efflux 94%
- S-Nitrosylation of CRTC1 in Alzheimer's disease impairs CREB-dependent gene expression induced by neuronal activity 93%
- Protective association of HLA-DRB1 *04 subtypes in neurodegenerative diseases implicates acetylated Tau PHF6 sequences 93%
Similar papers in this journal
- Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity 94%
- Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer's disease 93%
- Applying high-resolution spatial transcriptomics to characterise the amyloid plaque cell niche in Alzheimer's Disease 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.