Proof-of-principle for SERS imaging of Aspergillus nidulans hyphae using in vivo synthesis of gold nanoparticles
Bibliographic record
Abstract
High spatial resolution methods to assess the physiology of growing cells should permit analysis of fungal biochemical composition. Whole colony methods cannot capture the details of physiology and organism-environment interaction, in part because the structure, function and composition of fungal hyphae vary within individual cells depending on their distance from the growing apex. Surface Enhanced Raman Scattering (SERS) can provide chemical information on materials that are in close contact with appropriate metal substrates, such as nanopatterned gold surfaces and gold nanoparticles (AuNPs). Since nanoparticles can be generated by living cells, we have created conditions for AuNP formation within and on the surface of Aspergillus nidulans hyphae in order to explore their potential for SERS analysis. AuNP distribution and composition have been assessed by UV-Vis spectroscopy, fluorescence light microscopy, transmission electron microscopy, and scanning transmission X-ray microscopy. AuNPs were often associated with hyphal walls, both in the peripheral cytoplasm and on the outer wall surface. Interpretation of SERS spectra is challenging, and will require validation for the diversity of organic molecules present. Here, we show proof-of-principle that it is possible to generate SERS spectra from nanoparticles grown in situ by living hyphae.
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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".