P040 Prediction of endoscopic features of disease from single-cell RNA sequencing data in sigmoid colon of patients with ulcerative colitis
Bibliographic record
Abstract
Abstract Background Patients suffering from ulcerative colitis (UC) have reduced life qualities and an increased risk of developing cancer. Current treatment options are limited and have a ceiling effect with remission rates of ~35%. A possible reason is the multi-factorial aetiology, which are associated with an overactivation of the immune system and a dysbiosis of the gut microbiome. An important step towards a better understanding is the characterization of epithelial lesions. Their severity is classified by the Mayo score system based on their macroscopic appearance. While previous studies analysed cellular changes within the colon in UC patients, a detailed characterization of different UC-associated lesions are still missing, which is the aim of our study in collaboration with Sanofi Pharmaceuticals. Methods We recruit patients with active disease at our endoscopy unit and collect different biopsies from multiple regions within these patients to compare the reproducibility of single cell RNA sequencing (scRNAseq) results of similar appearing lesions within and between patients. For the sample preparation, we include a cellular enrichment step for immune, epithelial and stromal cells before performing 10x scRNAseq to obtain equal numbers for each cell fraction from every sampled area. Results Our cellular isolation strategy allows successful recovery of fibroblast, epithelial and immune cell fractions from each lesion. All expected epithelial cell types as previously described are detected in our dataset as well. Moreover, our experimental setup to obtain comparable numbers of fibroblasts, epithelial and immune cells enables us now to investigate cell-cell communication differences within different Mayo score lesions. For example, CXCL signal increased with the severity of macroscopic lesions and involves multiple cell types. Conclusion Our preliminary results promise exciting insights into cell-cell communications at an unprecedented depth, which identifies aberrant signalling pathway that may contribute to treatment failure in the clinics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".