Uncovering single‐nucleus RNA velocity variations in neuropathologic Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Differential single‐nucleus (snRNA‐seq) gene expression analyses in Alzheimer’s disease (AD) provide fixed snapshots of cellular alterations, failing to detect the temporal dynamics of genes at individual cells. To overcome this limitation, here we analyze single‐nucleus RNA velocities (RNA‐vel) from the prefrontal cortex to characterize dynamic genetic and cellular differences in neuropathological AD progression. RNA‐vel is the rate of change of gene expression obtained by comparing intronic and exonic sequence counts. Comparison of AD pathology associated gene expression with parallel RNA‐vel differences reveals sets of genes and molecular pathways that underlie the static and putative dynamic regimes of cell type‐specific dysregulations underlying the disease. Method We used snRNA‐seq data from the prefrontal cortex of 48 subjects1 with varied levels of AD pathology from ROSMAP2. We then calculated both the expression and RNA‐vel differences between low AD‐pathology and mild‐to‐severe AD‐pathology. These differences were cell‐specific across six major cell types: excitatory neurons, inhibitory neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte progenitor cells. 1. We characterized RNA‐vel differences associated with four AD neuropathology traits: neuritic plaque, neuronal neurofibrillary tangle, overall β‐amyloid load, and PHF tau tangle density. 2. We demonstrated the reproducibility of the RNA‐vel differences in two independent datasets: dorsolateral prefrontal cortex (N = 24) from ROSMAP3 and superior frontal gyrus (N = 6) from GEO4. Result Only 10 of 843 (1.2%) genes overlap between the differential RNA expression and velocity analyses, suggesting substantial AD‐pathology related differences between these two RNA descriptors. The genes with only velocity differences relate to cell developmental and synaptic processes. Conversely, the genes with only differential expression are majorly associated with mitochondrial activity and ribosomal processes. Many genes were reproduced in the independent datasets; these overlapping genes are again associated with neural development and synaptic activities across many cell types, as well as vascular‐ and immune‐related processes in astrocytes and microglia, respectively Conclusion We use RNA velocity to characterize, for the first time to our knowledge, the dynamical multicellular processes underlying neuropathological AD progression. The results support the validity of the novel RNA‐vel concept for achieving a complementary molecular characterization of AD, which may be obscured by typical analysis of RNA abundance alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".