Correlative atomic force microscopy quantitative imaging-laser scanning confocal microscopy quantifies the impact of stressors on live cells in real-time
Bibliographic record
Abstract
There is an urgent need to assess the effect of anthropogenic chemicals on model cells prior to their release, helping to predict their potential impact on the environment and human health. Laser scanning confocal microscopy (LSCM) and atomic force microscopy (AFM) have each provided an abundance of information on cell physiology. In addition to determining surface architecture, AFM in quantitative imaging (QI) mode probes surface biochemistry and cellular mechanics using minimal applied force, while LSCM offers a window into the cell for imaging fluorescently tagged macromolecules. Correlative AFM-LSCM produces complimentary information on different cellular characteristics for a comprehensive picture of cellular behaviour. We present a correlative AFM-QI-LSCM assay for the simultaneous real-time imaging of living cells in situ, producing multiplexed data on cell morphology and mechanics, surface adhesion and ultrastructure, and real-time localization of multiple fluorescently tagged macromolecules. To demonstrate the broad applicability of this method for disparate cell types, we show altered surface properties, internal molecular arrangement and oxidative stress in model bacterial, fungal and human cells exposed to 2,4-dichlorophenoxyacetic acid. AFM-QI-LSCM is broadly applicable to a variety of cell types and can be used to assess the impact of any multitude of contaminants, alone or in combination.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".