Organoids in concert: engineering in vitro models toward enhanced fidelity
Bibliographic record
Abstract
Abstract Organoids have emerged as a powerful platform for studying complex biological processes and diseases in vitro. However, most studies have focused on individual organoids, overlooking the inter‐organ interactions in vivo and limiting the physiological relevance of the models. To address this limitation, the development of a multi‐organoid system has gained considerable attention. This system aims to recapitulate inter‐organ communication and enable the study of complex physiological processes. This review provides a comprehensive overview of the recent advancements in organoid engineering and the emerging strategies for constructing a multi‐organoid system. First, we highlight the critical mechanical, structural, and biochemical factors involved in designing suitable materials for the growth of different organoids. Additionally, we discuss the incorporation of dynamic culture environments to enhance organoid culture and enable inter‐organoid communication. Furthermore, we explore techniques for manipulating organoid morphogenesis and spatial positioning of organoids to establish effective inter‐organoid communication networks. We summarize the achievements in utilizing organoids to recapitulate inter‐organ communication in vitro, including assembloids and microfluidic multi‐organoid platforms. Lastly, we discuss the existing challenges and opportunities in developing a multi‐organoid system from its technical bottlenecks in scalability to its applications toward complex human diseases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".