Probing cellular processes by long-term live imaging – historic problems and current solutions
Bibliographic record
Abstract
Living organisms, tissues, cells and molecules are highly dynamic. The importance of their continuous and long-term observation has been recognized for over a century but has been limited by technological hurdles. Improvements in imaging technologies, genetics, protein engineering and data analysis have more recently allowed us to answer long-standing questions in biology using quantitative continuous long-term imaging. This requires a multidisciplinary collaboration between scientists of various backgrounds: biologists asking relevant questions, imaging specialists and engineers developing hardware, and informaticians and mathematicians developing software for data acquisition, analysis and computational modeling. Despite recent improvements, there are still obstacles to be addressed before this technology can achieve its full potential. This Commentary aims at providing an overview of currently available technologies for quantitative continuous long-term single-cell imaging, their limitations and what is required to bring this field to the next level. We provide an historical perspective on the development of this technology and discuss key issues in time-lapse imaging: keeping cells alive, using labels, reporters and biosensors, and hardware and software requirements. We highlight crucial and often non-obvious problems for researchers venturing into the field and hope to inspire experts in the field and from related disciplines to contribute to future solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".