Molecular phenotyping of single pancreatic islet leader beta cells by “Flash-Seq”
Bibliographic record
Abstract
Abstract Aims Spatially-organised increases in cytosolic Ca 2+ within pancreatic beta cells in the pancreatic islet underlie the stimulation of insulin secretion by high glucose. Recent data have revealed the existence of subpopulations of beta cells including “leaders” which initiate Ca 2+ waves. Whether leader cells possess unique molecular features, or localisation, is unknown. Main methods High speed confocal Ca 2+ imaging was used to identify leader cells and connectivity analysis, running under MATLAB and Python, to identify highly connected “hub” cells. To explore transcriptomic differences between beta cell sub-groups, individual leaders or followers were labelled by photo-activation of the cryptic fluorescent protein PA-mCherry and subjected to single cell RNA sequencing (“Flash-Seq”). Key findings Distinct Ca 2+ wave types were identified in individual islets, with leader cells present in 73 % (28 of 38 islets imaged). Scale-free, power law-adherent behaviour was also observed in 29% of islets, though “hub” cells in these islets did not overlap with leaders. Transcripts differentially expressed (295; padj<0.05) between leader and follower cells included genes involved in cilium biogenesis and transcriptional regulation. Functionally validating these findings, cilia number and length tended to be lower in leader vs follower cells. Leader cells were also located significantly closer to delta cells in Euclidian space than were follower cells. Significance The existence of both a discrete transcriptome and unique localisation implies a role for these features in defining the specialized function of leaders. Specifically, these data raise the possibility of altered signalling from delta cells towards somatostatin receptors present on leader cell cilia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".