Imaging of native transcription factors and histone phosphorylation at high resolution in live cells
Bibliographic record
Abstract
Fluorescent labeling of endogenous proteins for live-cell imaging without exogenous expression of tagged proteins or genetic manipulations has not been routinely possible. We describe a simple versatile antibody-based imaging approach (VANIMA) for the precise localization and tracking of endogenous nuclear factors. Our protocol can be implemented in every laboratory allowing the efficient and nonharmful delivery of organic dye-conjugated antibodies, or antibody fragments, into different metazoan cell types. Live-cell imaging permits following the labeled probes bound to their endogenous targets. By using conventional and super-resolution imaging we show dynamic changes in the distribution of several nuclear transcription factors (i.e., RNA polymerase II or TAF10), and specific phosphorylated histones (γH2AX), upon distinct biological stimuli at the nanometer scale. Hence, considering the large panel of available antibodies and the simplicity of their implementation, VANIMA can be used to uncover novel biological information based on the dynamic behavior of transcription factors or posttranslational modifications in the nucleus of single live cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".