Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions
- Department of Neurology and Neurosurgery, McGill University, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Canada
- Department of Biomedical Engineering, McGill University, Canada
- School of Computer Science, McGill University, Canada
- Mila – Quebec Artificial Intelligence Institute, Canada
- The Douglas Research Center, Canada
- McGill Centre for Studies in Aging, Canada
Figures
Figure 1
Schematic approach for whole-brain cell type proportions vulnerability analysis in neurodegeneration.
(A) Microarray bulk gene expression levels in the Allen Human Brain Atlas (AHBA) were derived from 3072 distinct tissue samples of six postmortem healthy human brains. Missing gene expression data were then inferred for each unsampled gray matter voxel using Gaussian process regression. When combined with original AHBA data, they were mapped into volumetric Montreal Neurological Institute (MNI) space, resulting in the whole-brain transcriptional atlas. Deconvolution algorithm for bulk RNA expression levels was applied to the transcriptional atlas by using well-known cell type-specific gene markers to estimate cell type proportions. Comprehensive volumetric maps showing reconstructed distributions of six canonical cell types across all gray matter voxels in the brain were created (see ‘Cell type proportion estimation’). (B) Voxel-wise surface visualization (lateral, dorsal, and ventral views) of cell abundance maps for neurons, astrocytes, microglia, endothelial cells, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). At each voxel, red and blue colors indicate high and low proportion densities, respectively. (C) Associations between cell type proportions from each density map and atrophy values in 13 neurodegenerative conditions were analyzed in 118 gray matter regions predefined by the automated anatomical labeling (AAL) atlas.
© 2024, BioRender Inc. Figure 1 was created with BioRender, and is published under a CC B-NC-ND license with permission. Further reproductions must adhere to the terms of this license.
Figure 2
Spatial associations between tissue integrity and cell type proportions for 13 neurodegenerative conditions illustrated in the scatterplots and surface maps (left hemisphere; lateral view) of regional measures.
(A–M) Strongest Spearman’s correlations for early-onset Alzheimer’s disease (EOAD), late-onset Alzheimer’s disease (LOAD), dementia with Lewy bodies (DLB), presenilin-1 (PS1), FTLD-3Rtau, FTLD-4Rtau, FTLD-TDP43A, FTLD-TDP43C, Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), behavioral variant of frontotemporal dementia (bvFTD), non-fluent variant of primary progressive aphasia (nfvPPA), and semantic variant of primary progressive aphasia (svPPA), respectively. Atrophy and cell type density measures were averaged across 118 gray matter (GM) regions and projected to the cortical surface of the fsaverage template. Each dot in the scatterplots represents a GM region from the automated anatomical labeling (AAL) atlas (Supplementary file 1). Lower tissue integrity score in the scatterplots’ x-axis indicates greater GM loss/atrophy. For a better visual comparison of patterns in atrophy and cell abundance, the atrophy scale was reversed, with higher t-statistic values indicating greater atrophy in the surface plots. Thus, the first color bar ranging from 0 is universal for all cell maps and pathologically confirmed dementia conditions (A–H). The second color bar captures the tissue enlargement in PD, ALS, and variants of FTD (I–M). Notice how astrocyte density significantly correlates with increase in tissue loss in EOAD, DLB, PS1, FTLD-TDP43C, and nfvPPA (A, C, D, H, L; p<0.001). Tissue loss was also associated with increase in microglial proportion in LOAD, FTLD-3Rtau, FTLD-4Rtau, FTLD-TDP43A, bvFTD, and svPPA (B, E, F, G, K, M; p<0.001). Increased oligodendrocytes associated with PD (I; p<0.001). Increase in neuronal proportion showed association with decrease in atrophy and tissue enrichment in ALS (J; p<0.001). All p-values were false discovery rate (FDR)-adjusted with the Benjamini–Hochberg procedure (p<0.05).
Figure 3 with 1 supplement
Cell and disorder similarities based on shared distributions.
(A) Dendrogram and unsupervised hierarchical clustering heatmap of Spearman’s correlations between cell type proportions and atrophy patterns across the 13 neurodegenerative conditions. (B) Cell–cell associations based on regional vulnerabilities to tissue loss across neurodegenerative conditions. (C) Disorder–disorder similarities across cell types. In (A), red color corresponds to strong positive correlations between cells and disorders, white to no correlation, and dark blue to strong negative correlations.
Figure 3—figure supplement 1
Spatial associations between tissue integrity and cell type proportions for 13 neurodegenerative conditions in the gray matter (GM) regions defined by the Desikan–Killiany–Tourville (DKT) parcellation.
Additional files
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Supplementary file 1
Cortical and subcortical regions from the AAL atlas.
- https://cdn.elifesciences.org/articles/89368/elife-89368-supp1-v1.docx
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Supplementary file 2
Origin of each disorder-associated t-statistic map.
- https://cdn.elifesciences.org/articles/89368/elife-89368-supp2-v1.docx
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Supplementary file 3
Eighty cell type-related gene markers provided by the BRETIGEA R package.
- https://cdn.elifesciences.org/articles/89368/elife-89368-supp3-v1.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/89368/elife-89368-mdarchecklist1-v1.docx
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Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions
eLife 12:RP89368.
https://doi.org/10.7554/eLife.89368.3
