Live-cell microscopy – tips and tools
Bibliographic record
Abstract
Imaging of living cells and tissue is now common in many fields of the life and physical sciences, and is instrumental in revealing a great deal about cellular dynamics and function. It is crucial when performing such experiments that cell viability is at the forefront of any measurement to ensure that the physiological and biological processes that are under investigation are not altered in any way. Many cells and tissues are not normally exposed to light during their life cycle, so it is important for microscopy applications to minimize light exposure, which can cause phototoxicity. To ensure minimal light exposure, it is crucial that microscope systems are optimized to collect as much light as possible. This can be achieved using superior-quality optical components and state-of-the-art detectors. This Commentary discusses how to set up a suitable environment on the microscope stage to maintain living cells. There is also a focus on general and imaging-platform-specific ways to optimize the efficiency of light throughput and detection. With an efficient optical microscope and a good detector, the light exposure can be minimized during live-cell imaging, thus minimizing phototoxicity and maintaining cell viability. Brief suggestions for useful microscope accessories as well as available fluorescence tools are also presented. Finally, a flow chart is provided to assist readers in choosing the appropriate imaging platform for their experimental 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.030 |
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".