Three-Dimensional Imaging of Transparent Tissues via Metal Nanoparticle Labeling
Bibliographic record
Abstract
Chemical probes are key components of the bioimaging toolbox, as they label biomolecules in cells and tissues. The new challenge in bioimaging is to design chemical probes for three-dimensional (3D) tissue imaging. In this work, we discovered that light scattering of metal nanoparticles can provide 3D imaging contrast in intact and transparent tissues. The nanoparticles can act as a template for the chemical growth of a metal layer to further enhance the scattering signal. The use of chemically grown nanoparticles in whole tissues can amplify the scattering to produce a 1.4 million-fold greater photon yield than obtained using common fluorophores. These probes are non-photobleaching and can be used alongside fluorophores without interference. We demonstrated three distinct biomedical applications: (a) molecular imaging of blood vessels, (b) tracking of nanodrug carriers in tumors, and (c) mapping of lesions and immune cells in a multiple sclerosis mouse model. Our strategy establishes a distinct yet complementary set of imaging probes for understanding disease mechanisms in three dimensions.
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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 | 0.001 | 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".