Correlated SEM, FIB-SEM, TEM, and NanoSIMS Imaging of Microbes from the Hindgut of a Lower Termite: Methods for<i>In Situ</i>Functional and Ecological Studies of Uncultivable Microbes
Bibliographic record
Abstract
The hindguts of lower termites harbor highly diverse, endemic communities of symbiotic protists, bacteria, and archaea essential to the termite's ability to digest wood. Despite over a century of experimental studies, ecological roles of many of these microbes are unknown, partly because almost none can be cultivated. Many of the protists associate with bacterial symbionts, but hypotheses for their respective roles in nutrient exchange are based on genomes of only two such bacteria. To show how the ecological roles of protists and nutrient transfer with symbiotic bacteria can be elucidated by direct imaging, we combined stable isotope labeling (13C-cellulose) of live termites with analysis of fixed hindgut microbes using correlated scanning electron microscopy, focused ion beam-scanning electron microscopy (FIB-SEM), transmission electron microscopy, and high resolution imaging mass spectrometry (NanoSIMS). We developed methods to prepare whole labeled cells on solid substrates, whole labeled cells milled with a FIB-SEM instrument to reveal cell interiors, and ultramicrotome sections of labeled cells for NanoSIMS imaging of 13C enrichment in protists and associated bacteria. Our results show these methods have the potential to provide direct evidence for nutrient flow and suggest the oxymonad protist Oxymonas dimorpha phagocytoses and enzymatically degrades ingested wood fragments, and may transfer carbon derived from this to its surface bacterial symbionts.
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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 | 0.000 | 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) | 0.001 | 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".