Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis
Bibliographic record
Abstract
On-chip imaging of intact three-dimensional tissues within microfluidic devices is fundamentally hindered by intratissue optical scattering, which impedes their use as tissue models for high-throughput screening assays. Here, we engineered a microfluidic system that preserves and converts tissues into optically transparent structures in less than 1 d, which is 20× faster than current passive clearing approaches. Accelerated clearing was achieved because the microfluidic system enhanced the exchange of interstitial fluids by 567-fold, which increased the rate of removal of optically scattering lipid molecules from the cross-linked tissue. Our enhanced clearing process allowed us to fluorescently image and map the segregation and compartmentalization of different cells during the formation of tumor spheroids, and to track the degradation of vasculature over time within extracted murine pancreatic islets in static culture, which may have implications on the efficacy of beta-cell transplantation treatments for type 1 diabetes. We further developed an image analysis algorithm that automates the analysis of the vasculature connectivity, volume, and cellular spatial distribution of the intact tissue. Our technique allows whole tissue analysis in microfluidic systems, and has implications in the development of organ-on-a-chip systems, high-throughput drug screening devices, and in regenerative medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".