Spatial transcriptomics of cerebral amyloid angiopathy and amyloid‐β related angiitis
Bibliographic record
Abstract
BACKGROUND: The removal of amyloid-β (Aβ) by antibodies has revolutionized the treatment landscape of Alzheimer's disease (AD). Vascular Aβ has been implicated in the emergence of amyloid-related imaging abnormalities (ARIA). A better understanding of the effect of Aβ on blood vessels is required to find new biomarkers and treatments for ARIA. METHOD: We generated tissue microarrays of brain biopsies of 4 controls, 3 primary CNS vasculitis (PCNVS), 4 cerebral amyloid angiopathy (CAA), and 5 amyloid-β-related angiitis (ABRA) patients and performed histopathology and Visium 10x spatial RNA-sequencing. We performed clustering, compared the spots that contained blood vessels between the 4 conditions and used gene ontology analyses to define disease-specific signatures. Last, we tested whether the inflammatory blood vessel signatures could be identified in CSF SOMAscan proteomics of 173 A-T-N-, 82 A+T-N-, 164 A+T+N-, and 144 A+T+N+ individuals of the AD neuroimaging initiative (ADNI). Correlation analyses in A+ and A- individuals of the inflammatory signature with more than 7000 proteins were performed. RESULTS: Tissue microarrays allowed a scalable approach for spatial transcriptomics and histology. CAA and ABRA patients showed a strong vascular Aβ accumulation but only ABRA patients had additional T cell infiltrates and stronger blood brain barrier leakage. Spatial transcriptomics allowed the deconvolution of spots that contained different parenchymal cell types like neurons, astrocytes or endothelial cells. Comparing the blood vessels between the conditions revealed a CAA-specific signature that was characterized by cellular detoxification. PCNVS and ABRA showed an overlapping inflammatory signature, however, the blood vessels from ABRA patients showed a type I interferon response that was absent in CAA and PCNVS. Using OAS1 as a biomarker for the type I interferon response, we identified strong correlations between OAS1 and endothelial cell biomarkers in the CSF of ADNI participants. In contrast, OAS1 was stronger associated with the activation of the interferon response and cell death pathways in A+ than in A- participants. CONCLUSION: We identified Aβ-specific inflammatory blood vessel signatures in brain biopsies and the CSF of an AD continuum cohort. This study will help to prioritize biomarkers and treatments for Aβ-induced vascular pathologies and potentially ARIA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".