432. SINGLE-CELL RNA SEQUENCING OF MORPHOLOGICALLY-PURE PATIENT-DERIVED ORGANOIDS FROM ESOPHAGEAL ADENOCARCINOMA PATIENTS
Bibliographic record
Abstract
Abstract Background We have successfully cultured esophageal adenocarcinoma (EAC) patient-derived organoids (PDOs) from endoscopic biopsies. These PDOs recapitulate the histological and molecular features of the originating tumour and frequently exhibit morphological heterogeneity within the same patient sample. The underlying biology of these morphologies and their relation to treatment response remains unknown. This study will examine the gene expression profile of morphologically pure organoids. Methods EAC tissue samples collected from patients were processed and embedded into Matrigel to generate PDOs. Parental PDOs with heterogenous morphology were sorted to isolate clonal pure morphology organoids. Multiple clones were expanded and clones of different morphology were collected and dissociated to single cells for single-cell RNA sequencing. Results Multiple single morphology clones were grown from nine different mixed morphology parental PDOs, demonstrating that EAC organoids can be generated from single cells. Successful formation of organoids from single cells took between two to four weeks. The percentage of single cells successfully generating organoids was sample-dependent. Six clones of solid, cystic, budding or grape-like morphology from two PDOs have been expanded and dissociated to single cells for single-cell RNA sequencing. Conclusion PDOs have emerged as a powerful tool to study drug response and personalize therapy. This study will examine the correlation of EAC organoid morphology with gene expression. Future directions will include the identification of morphology-dependent drug targets, enabling the development of more precise targeted drug screening for each patient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".