The MEMIC: An <i>ex vivo</i> system to model the complexity of the tumor microenvironment
Bibliographic record
Abstract
ABSTRACT There is an urgent need for accurate, scalable, and cost-efficient models of the complexity and heterogeneity of the tumor microenvironment. Here, we detail how to fabricate and use the Metabolic Microenvironment Chamber (MEMIC) – a 3D-printed ex vivo model of intratumoral heterogeneity. A major driver of the cellular and molecular diversity in tumors is the accessibility to the blood stream that provides key resources such as oxygen and nutrients. While some tumor cells have direct access to these resources, many others must survive under progressively more ischemic environments as they reside further from the vasculature. The MEMIC is designed to simulate the differential access to nutrients and allows co-culturing different cell types, such as tumor and immune cells. This system is optimized for live imaging and other microscopy-based approaches and it is a powerful tool to study tumor features such as the effect of nutrient scarcity on tumor-stroma interactions. Due to its adaptable design and full experimental control, the MEMIC can provide novel insights into the tumor microenvironment that would be difficult to obtain via other methods. As a proof of principle, we show that cells can sense gradual changes in metabolite concentration, and tune intracellular cell signaling to form multicellular spatial patterns of cell proliferation. We also show that ischemic macrophages reduce epithelial features in neighboring tumor cells highlighting the power of this system to study cell-cell interactions and non-cell autonomous effects of the metabolic microenvironment. We propose that the MEMIC can be easily adapted to study early development, ischemic stroke, and other systems where multiple cell types interact within heterogeneous environments.
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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 | 0.001 | 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".