Isolation, Culture, and Characterization of Primary Salivary Gland Cells
Bibliographic record
Abstract
Primary cells are an essential tool for in vitro studies and are obtained directly from living tissues or organs. They closely mimic the physiological state and maintain in vivo functions for short periods of time under optimal conditions. Isolation and culture of salivary gland (SG) cells are useful to decipher the various mechanisms involved in salivary gland dysfunction. However, unlike some other primary cell cultures, SG cell cultures from patient-derived tissues present several challenges. They are difficult to obtain, culture, expand, and characterize due to their sensitive heterogenous cell population and limited expansion potential. In addition, the majority of saliva-secreting acinar cells fail to maintain a differentiated state ex vivo for long periods, and eventually succumb to an acinar-to-ductal metaplasia, losing their secretory phenotype and functions. Herein, we describe two detailed protocols for primary SG cell isolation, culture, and expansion from human (or mouse) salivary tissues using serum-free culture media. We also describe the growth kinetics of these primary cells along with their immunocytochemical characterization. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of SG single-cell culture from freshly obtained human or mouse SG tissues. Basic Protocol 2: Preparation of SG explant culture from freshly obtained human or mouse SG tissues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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".