3D Single-Molecule Super-Resolution Imaging of Microfabricated Multiscale Fractal Substrates for Calibration and Cell Imaging
Bibliographic record
Abstract
Microstructures arrayed over a substrate have shown increasing interest due to their ability to provide advanced 3D cellular models, which open up new possibilities for cell culture, proliferation, and differentiation. Still, the mechanisms by which physical cues impact the cell phenotype are not fully understood, hence the necessity to interrogate cell behavior at the highest resolution. However, cell 3D high-resolution optical imaging on such microstructured substrates remains challenging due to their complexity as well as axial calibration issues. In this work, we address this issue by leveraging the geometrical characteristics of fractal-like structures, which serve as axial calibration tools and modulate cell growth. To this end, we use multiscale 3D SiO 2 substrates consisting of spatially arrayed octahedral features of a few micrometers to hundreds of nanometers. Through optimizations of both the structures and optical imaging conditions, we demonstrate the potential of these 3D multiscale structures as an alternative to electron microscopy for material imaging but also as calibration tools for 3D super-resolution microscopy. We used their multiscale and known geometry to perform lateral and axial calibrations in 3D single-molecule localization microscopy (SMLM) and assess imaging resolutions. We then utilized these substrates as a platform for high-resolution bioimaging. As a proof of concept, we cultivate human mesenchymal stem cells on these substrates, revealing very different growth patterns compared to flat glass. Specifically, the spatial distribution of cytoskeleton proteins is vastly modified, as we demonstrate with a 3D SMLM assessment.
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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 | 0.000 | 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 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".