The Neurolipid Atlas: a lipidomics resource for neurodegenerative diseases
Bibliographic record
Abstract
Lipid alterations in the brain have been implicated in many neurodegenerative diseases. To facilitate comparative lipidomic research across brain diseases, we establish a data common named the Neurolipid Atlas that we prepopulated with isogenic induced pluripotent stem cell (iPS cell)-derived lipidomics data for different brain diseases. Additionally, the resource contains lipidomics data of human and mouse brain tissue. Leveraging multiple datasets, we demonstrate that iPS cell-derived neurons, microglia and astrocytes exhibit distinct lipid profiles that recapitulate in vivo lipotypes. Notably, the Alzheimer disease (AD) risk gene ApoE4 drives cholesterol ester (CE) accumulation specifically in human astrocytes and we also observe CE accumulation in whole-brain lipidomics from persons with AD. Multiomics interrogation of iPS cell-derived astrocytes revealed that altered cholesterol metabolism has a major role in astrocyte immune pathways such as the immunoproteasome and major histocompatibility complex class I antigen presentation. Our data commons, available online ( https://neurolipidatlas.com/ ), allows for data deposition by the community and provides a user-friendly tool and knowledge base for a better understanding of lipid dyshomeostasis in neurodegenerative diseases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.031 |
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".