Design and analysis of a long‐term live‐cell imaging chamber for tracking cellular dynamics within cultured human islets of Langerhans
Bibliographic record
Abstract
A means of expanding islet cell mass is urgently needed to supplement the limited availability of donor islets of Langerhans for transplant. Live cell imaging of human islets in culture has the potential to identify the specific cells and processes involved in islet expansion. A novel imaging chamber was developed to facilitate long-term three-dimensional imaging of human islets during transformation. Islets have been induced to transform into duct-like epithelial cystic structures and revert back to glucose responsive endocrine cells under appropriate conditions (Jamal et al. Cell Death Differ. 2005 12:702-712). Here we aim to further our understanding by characterizing the process at a single cell level over time-essentially constructing a high resolution recorded history of each cell and its progeny during transformation and reversion. The imaging chamber enables high resolution imaging of three-dimensional islets while maintaining the structure of the islet cells and intercellular matrix components. A mathematical model was developed to validate the imaging chamber design by determining the required chamber dimensions to avoid introduction of oxygen and nutrient transport limitations. Human islets were embedded in collagen in the imaging chamber and differential interference contrast time course images were obtained at 3 min intervals. Immunofluorescent imaging confirmed that islet phenotype was maintained for at least 5 days during imaging. Analysis of the time courses confirms our ability to identify and track individual cells over time and to observe cell death and phenotype transformation in isolated human islets.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".