Patterning cellular compartments within TRACER cultures using sacrificial gelatin printing
Bibliographic record
Abstract
In the past decade, it has been well recognised that the tumour microenvironment contains microenvironmental components such as hypoxia that significantly influence tumour cell behaviours such, invasiveness and therapy resistance, all of which provide new targets for studying cancer biology and developing anticancer therapeutics. In response, a large number of two-dimensional and three-dimensional (3D) in vitro tumour models have been developed to recapitulate different aspects of the tumour microenvironment and enable the study of related biological questions. While more complex models enable new biological insight, such models often involve time-consuming and complex fabrication or analysis processes, which limit their adoption by the broader cancer biology community. To address this, we recently reported the development of a new platform that enables easy assembly and analysis of 3D tumour cultures, the tissue roll for analysis of cellular environment response (TRACER). The TRACER platform enables recapitulation of many spatial aspects of the tumour microenvironment to ask a variety of questions, however its original design contains only one cell type. In contrast tumours in vivo often contain a neoplastic and stromal compartment. To expand the types of questions the TRACER system is useful for asking, here we present a strategy to pattern distinct cell type domains into TRACER layers using a custom-built gelatin-dispensing pen. The pen allows deposition of a temporary gelatin barrier into the TRACER scaffold to define domain boundaries between cell populations. The gelatin can be melted away after cell seeding to allow interaction of cell populations from adjacent domains. Our device offers a simple strategy to generate complex multi-cell type tumour cultures for analysis of fundamental biology and drug development applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".