Mapping of Heavy Metal Ion Sorption to Cell-Extracellular Polymeric Substance-Mineral Aggregates by Using Metal-Selective Fluorescent Probes and Confocal Laser Scanning Microscopy
Bibliographic record
Abstract
Biofilms, organic matter, iron/aluminum oxides, and clay minerals bind toxic heavy metal ions and control their fate and bioavailability in the environment. The spatial relationship of metal ions to biomacromolecules such as extracellular polymeric substances (EPS) in biofilms with microbial cells and biogenic minerals is complex and occurs at the micro- and submicrometer scale. Here, we review the application of highly selective and sensitive metal fluorescent probes for confocal laser scanning microscopy (CLSM) that were originally developed for use in life sciences and propose their suitability as a powerful tool for mapping heavy metals in environmental biofilms and cell-EPS-mineral aggregates (CEMAs). The benefit of using metal fluorescent dyes in combination with CLSM imaging over other techniques such as electron microscopy is that environmental samples can be analyzed in their natural hydrated state, avoiding artifacts such as aggregation from drying that is necessary for analytical electron microscopy. In this minireview, we present data for a group of sensitive fluorescent probes highly specific for Fe(3+), Cu(2+), Zn(2+), and Hg(2+), illustrating the potential of their application in environmental science. We evaluate their application in combination with other fluorescent probes that label constituents of CEMAs such as DNA or polysaccharides and provide selection guidelines for potential combinations of fluorescent probes. Correlation analysis of spatially resolved heavy metal distributions with EPS and biogenic minerals in their natural, hydrated state will further our understanding of the behavior of metals in environmental systems since it allows for identifying bonding sites in complex, heterogeneous systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".