Bacterial and Mineral Elements in an Arctic Biofilm: A Correlative Study Using Fluorescence and Electron Microscopy
Bibliographic record
Abstract
Few simple labeling methods exist for simultaneous fluorescence and electron microscopy of bacteria and biofilms. Here we describe the synthesis, characterization, and application of fluorescent nanoparticle quantum dot (QD) conjugates to target microbial species, including difficult to label Gram-negative strains. These QD conjugates impart contrast for both environmental scanning electron microscopy (ESEM) and fluorescence microscopy, permitting observation of living and fixed bacteria and biofilms. We apply these probes for studying biofilms extracted from perennial cold springs in the Canadian High Arctic, which is a particularly challenging system. In these biofilms, sulfur-metabolizing bacteria live in close association with unusual sulfur mineral formations. Following simple labeling protocols with the QD conjugates, we are able to image these organisms in fully-hydrated samples and visualize their relationship to the sulfur minerals using both ESEM and fluorescence microscopy. We then use scanning transmission electron microscopy to observe precipitated sulfur around individual cells and within the biofilm lattice. All combined, this information sheds light on the possible mechanisms of biofilm and mineral structure formation. These new QD conjugates and techniques are highly transferable to many other microbiological applications, especially those involving Gram-negative bacteria, and can be used for correlated fluorescence and electron microscopy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".