Long‐Lived Organotypic Slice Culture Model of the Rat Basolateral Amygdala
Bibliographic record
Abstract
Organotypic slice cultures (OTCs) have been employed in the laboratory since the early 1980s and have proved to be useful for the study of a number of neural systems. Our recent work focuses on the development of behavioral stress resilience induced by repeated daily injections of neuropeptide Y into the basolateral amygdala (BLA). Resilience develops over weeks, persisting to 8 weeks. To unravel the cellular mechanisms underlying neuropeptide Y-induced stress resilience we developed in vitro OTCs of the BLA. Here, we provide an optimized protocol that consistently yields viable and healthy OTCs containing the BLA and surrounding tissue using the interface method, prepared with slices taken from postnatal (P) day 14 rats. We explain key points to optimizing tissue viability and discuss mitigation or avoidance of pitfalls that can arise to aid in successful implementation of this technique. We show that principal neurons in BLA OTCs (8 weeks in vitro = equivalent postnatal day 70) develop into networks that are electrophysiologically very similar to those from acute slices obtained from older rats (P70) and respond to pharmacological treatments in a comparable way. Furthermore, we highlight how these cultures be used to further understand the molecular, cellular, and circuit-level neuropathophysiological changes underlying stress disorders. BLA OTCs provide long-term physiological and pharmacological results whose predictions were borne out in vivo, supporting the validity of the BLA OTC as a model to unravel BLA neurocircuitry. Recent preliminary results also support the successful application of this approach to preparing long-lived OTCs of BLA and neocortex from mice. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Organotypic slice culture Support Protocol 1: Changing medium Support Protocol 2: Drug incubations Basic Protocol 2: Excision of OTC slices from inserts Support Protocol 3: Fixation of slices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".