On-chip clearing of arrays of 3-D cell cultures and micro-tissues
Bibliographic record
Abstract
Three-dimensional (3-D) cell cultures are beneficial models for mimicking the complexities of in vivo tissues, especially in tumour studies where transport limitations can complicate response to cancer drugs. 3-D optical microscopy techniques are less involved than traditional embedding and sectioning, but are impeded by optical scattering properties of the tissues. Confocal and even two-photon microscopy limit sample imaging to approximately 100-200 μm depth, which is insufficient to image hypoxic spheroid cores. Optical clearing methods have permitted high-depth imaging of tissues without physical sectioning, but they are difficult to implement for smaller 3-D cultures due to sample loss in solution exchange. In this work, we demonstrate a microfluidic platform for high-throughput on-chip optical clearing of breast cancer spheroids using the SeeDB, Clear(T2), and ScaleSQ clearing methods. Although all three methods are able to effectively clear the spheroids, we find that SeeDB and ScaleSQ more effectively clear the sample than Clear(T2); however, SeeDB induces green autofluorescence while ScaleS causes sample expansion. Our unique on-chip implementation permits clearing arrays of 3-D cultures using perfusion while monitoring the 3-D cultures throughout the process, enabling visualization of the clearing endpoint as well as monitoring of transient changes that could induce image artefacts. Our microfluidic device is compatible with on-chip 3-D cell culture, permitting the use of on-chip clearing at the endpoint after monitoring the same spheroids during their culture. This on-chip method has the potential to improve readout from 3-D cultures, facilitating their use in cell-based assays for high-content drug screening and other 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".