Phenotypic Analysis of Organoids by Proteomics
Bibliographic record
Abstract
The development of 3D cell cultures into self-organizing organ-like structures named organoids provides a model that better reflects in vivo organ physiology and their functional properties. Organoids have been established from several organs, such as the intestine, prostate, brain, liver, kidney and pancreas. With recent advances in high-throughput and -omics profiling technologies, it is now possible to study the mechanisms of cellular organisation at the systems level. It is therefore not surprising that these methods are now used to characterize organoids at the transcriptomic, proteomic, chromatin state and transcription factor DNA-binding levels. These approaches can therefore provide a wealth of information regarding both the mechanisms involved in different diseases, and those involved in cell responses to different conditions, in a more in vivo setting. The authors provide an overview of the potential applications of quantitative mass spectrometry with organoid culture, and how the use of large-scale proteome measurements is emerging in different organoid systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".