Development of TRACER: tissue roll for analysis of cellular environment and response
Bibliographic record
Abstract
The tumour microenvironment is heterogeneous and consists of multiple cell types, variable extracellular matrix (ECM) composition, and contains cell-defined gradients of small molecules, oxygen, nutrients and waste. Emerging in vitro cell culture systems that attempt to replicate these features often fail to incorporate design strategies to facilitate efficient data collection and stratification. The tissue roll for analysis of cellular environment and response (TRACER) is a novel strategy to assemble layered, three-dimensional tumours with cell-defined, graded heterogeneous microenvironments that also facilitates cellular separation and stratification of data from different cell populations from specific microenvironments. Here we describe the materials selection and development of TRACER. We find that cellulose fibre scaffolding is an ideal support to generate tissue constructs having homogenous cell seeding and consistent properties. We explore ECM remodeling and long-term cell growth in the scaffold, and characterize the tumour microenvironment in assembled TRACERs using multiple established analysis methods. Finally, we confirm that TRACERs replicate small molecule gradients of glucose and lactate, and explore cell phenotype associated with these gradients using confocal microscopy, flow cytometry, and quantitative PCR analysis. We envision this technology will provide a platform to create complex, yet controlled tumour microenvironments that can be easily disassembled for snapshot analysis of cell phenotype and response to therapy in relation to microenvironment properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".