A Protocol for Neuralized Murine Olfactory Organoids
Bibliographic record
Abstract
Abstract Chronic olfactory dysfunction can be associated with parkinsonism, dementia, demyelinating disorders and schizophrenia. The olfactory epithelium (OE) represents an interface between the environment and the central nervous system. Mounting evidence implicates environmental factors in neurodegenerative disease processes, necessitating investigations into their interactions with the host’s genome. In Parkinson disease, hyposmia often precedes motor symptoms, raising the possibility that the OE could be involved in disease initiation. We previously demonstrated abundant α-synuclein expression in mammalian OE as well as aggregate formation in the olfactory nerve. Current in vitro models of OE are limited, relying primarily on post-mitotic cultures established from biopsies. To address this gap, we present a method for generating olfactory organoids of OE from adult mice. These organoids comprise neuronal and non-neuronal cell types, including sustentacular cells, thus encompassing structural elements of OE in situ . Expression of the olfactory sensory neuron marker OMP and Parkinson’s-linked α-synuclein was also detected in olfactory organoids, highlighting their potential usefulness to mechanistic research. We established OE organoids that were kept in culture for up to 3 weeks. In addition, we inoculated organoids with the neurotropic vesicular stomatitis virus to model infections. We conclude that this olfactory organoid model system offers a new platform for studying airborne environmental factors in their interactions with a genetically defined host; this, to study OE biology and enable the exploration of disease processes within olfactory tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".