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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".