Spatial proteomic mapping of human nuclear bodies reveals new functional insights into RNA regulation
Bibliographic record
Abstract
Abstract Nuclear bodies are diverse membraneless suborganelles with emerging links to development and disease. Explaining their structure, function, regulation, and implications in human health will require understanding their protein composition; however, isolating nuclear bodies for proteomic analysis remains challenging. We present the first comprehensive proximity proteomics-based map of nuclear bodies, featuring 140 bait proteins (encoded by 119 genes) and 1,816 unique prey proteins. We identified 641 potential nuclear body components, including 131 paraspeckle proteins and 147 nuclear speckle proteins. After validating 31 novel paraspeckle and nuclear speckle components, we discovered regulatory functions for the poorly characterised nuclear speckle- and RNA export-associated proteins PAXBP1, PPIL4, and C19ORF47, and revealed that QKI regulates paraspeckle size. This work provides a systematic framework of nuclear body composition in live cells that will accelerate future research into their organisation and roles in human health and disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".