Lipidomic Signatures in Microglial Extracellular Vesicles During Acute Inflammation: A Gateway to Neurological Biomarkers
Bibliographic record
Abstract
Extracellular vesicles (EVs) are membrane-bound vesicles released from all cells throughout the body, including the central nervous system, and are known to carry both membrane-bound proteins and cargo reflective of their cell of origin. EVs show promise as neurological disease biomarkers due to their molecular makeup reflecting their parent-cell composition signature and due to their ability to cross the blood-brain barrier. To date, the vast majority of research in this field has explored the protein profiles of EVs; however, lipids play an important role not only in the formation of EVs, but also in mediating cellular function and the pathological progression of many neurodegenerative conditions. Herein, we take a critical first step in determining the potential utility of EV lipids as biomarkers in neurological disease. In vitro we exposed BV-2 microglia to either control media or media containing lipopolysaccharides (LPS), a known pro-inflammatory stimulus, for 24 h then isolated both the cells and their EVs and performed LC-MS/MS. For the first time, we reveal distinct lipidomic changes can differentiate resting versus pro-inflammatory microglia and their EVs, while distinct lipids are preserved between EVs and their parent cell. Moreover, we add to current literature by demonstrating acute pro-inflammatory activation of microglia results in the activation and suppression of distinct lipidomic pathways. Finally, we demonstrate that analysis of lipid-based relationships between parent cells and their EVs may be a useful tool to infer cellular function. This study is the first of its kind to demonstrate that lipidomic analysis can not only differentiate the functional state of cells in vitro but can also differentiate their EVs. We lay the first brick in a foundation to support future research into EV lipids as novel and exciting biomarker candidates in neurological disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".