Computer-based fluorescence quantification: a novel approach to study nucleolar biology
Bibliographic record
Abstract
BACKGROUND: Nucleoli are composed of possibly several thousand different proteins and represent the most conspicuous compartments in the nucleus; they play a crucial role in the proper execution of many cellular processes. As such, nucleoli carry out ribosome biogenesis and sequester or associate with key molecules that regulate cell cycle progression, tumorigenesis, apoptosis and the stress response. Nucleoli are dynamic compartments that are characterized by a constant flux of macromolecules. Given the complex and dynamic composition of the nucleolar proteome, it is challenging to link modifications in nucleolar composition to downstream effects. RESULTS: In this contribution, we present quantitative immunofluorescence methods that rely on computer-based image analysis. We demonstrate the effectiveness of these techniques by monitoring the dynamic association of proteins and RNA with nucleoli under different physiological conditions. Thus, the protocols described by us were employed to study stress-dependent changes in the nucleolar concentration of endogenous and GFP-tagged proteins. Furthermore, our methods were applied to measure de novo RNA synthesis that is associated with nucleoli. We show that the techniques described here can be easily combined with automated high throughput screening (HTS) platforms, making it possible to obtain large data sets and analyze many of the biological processes that are located in nucleoli. CONCLUSIONS: Our protocols set the stage to analyze in a quantitative fashion the kinetics of shuttling nucleolar proteins, both at the single cell level as well as for a large number of cells. Moreover, the procedures described here are compatible with high throughput image acquisition and analysis using HTS automated platforms, thereby providing the basis to quantify nucleolar components and activities for numerous samples and experimental conditions. Together with the growing amount of information obtained for the nucleolar proteome, improvements in quantitative microscopy as they are described here can be expected to produce new insights into the complex biological functions that are orchestrated by the nucleolus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".