An automation workflow for high-throughput manufacturing and analysis of scaffold-supported 3D tissue arrays
Bibliographic record
Abstract
Abstract The success rate of bringing novel cancer therapies to the clinic remains extremely low due to the lack of relevant pre-clinical culture models that capture the complexity of human tumours. Patient-derived organoids have emerged as a useful tool to model patient and tumour heterogeneity to begin addressing this need. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations remains a challenge, however. One strategy to enable higher throughput organoid cultures that also enables easy image-based analysis is the Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform. SPOT allows the generation of flat, thin and dimensionally-defined microtissues in both 96- and 384-well plate footprints and is compatible with tumour organoid culture and downstream image-based readouts. SPOT manufacturing is currently a manual process however, limiting the use of SPOT to perform larger-scale screening. In this study, we integrate and optimize an automation approach to generate tumour-mimetic 3D engineered microtissues in SPOT using a liquid handler, and show comparable within-sample and between-sample variation as the standard manual manufacturing process. Furthermore, we develop a liquid handler-supported whole-cell extraction protocol and as a proof-of-value demonstration, we generate 3D complex tissues containing different proportions of tumour and stromal cells and perform single-cell-based end-point analysis to demonstrate the impact of co-culture on the tumour cell population specifically. We also demonstrate we can incorporate primary patient-derived organoids into the pipeline to capture patient-level tumour heterogeneity. We envision that this automated workflow integrated with 96/384-SPOT and multiple cell types and patient-derived organoid models will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets. This pipeline also allows the user to assess dynamic cell responses using high-content longitudinal imaging or downstream single-cell-based analyses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".