On-chip clearing of arrays of 3-D cell cultures and micro-tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it