Optimizing live-cell fluorescence imaging conditions to minimize phototoxicity
Bibliographic record
Abstract
Fluorescence illumination can cause phototoxicity that negatively affects living samples. This study demonstrates that much of the phototoxicity and photobleaching experienced with live-cell fluorescence imaging occurs as a result of 'illumination overhead' (IO). This occurs when a sample is illuminated but fluorescence emission is not being captured by the microscope camera. Several technological advancements have been developed, including fast-switching LED lamps and transistor-transistor logic (TTL) circuits, to diminish phototoxicity caused by IO. These advancements are not standard features on most microscopes and many biologists are unaware of their necessity for live-cell imaging. IO is particularly problematic when imaging rapid processes that require short exposure times. This study presents a workflow to optimize imaging conditions for measuring both slow and dynamic processes while minimizing phototoxicity on any standard microscope. The workflow includes a guide on how to (1) determine the maximum image exposure time for a dynamic process, (2) optimize excitation light intensity and (3) assess cell health with mitochondrial markers.This article has an associated First Person interview with the first author of the paper.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".