Transgenic force sensors and software to measure force transmission across the mammalian nuclear envelope <i>in vivo</i>
Bibliographic record
Abstract
Nuclear mechanotransduction is a growing field with exciting implications for the regulation of gene expression and cellular function. Mechanical signals may be transduced to the nuclear interior biochemically or physically through connections between the cell surface and chromatin. To define mechanical stresses upon the nucleus in physiological settings, we generated transgenic mouse strains that harbour FRET-based tension sensors or control constructs in the outer and inner aspects of the nuclear envelope. We knocked-in a published esprin-2G sensor to measure tensions across the LINC complex and generated a new sensor that links the inner nuclear membrane to chromatin. To mitigate challenges inherent to fluorescence lifetime analysis in vivo, we developed software (FLIMvivo) that markedly improves the fitting of fluorescence decay curves. In the mouse embryo, the sensors responded to cytoskeletal relaxation and stretch applied by micro-aspiration. They reported organ-specific differences and a spatiotemporal tension gradient along the proximodistal axis of the limb bud, raising the possibility that mechanical mechanisms coregulate pattern formation. These mouse strains and software are potentially valuable tools for testing and refining mechanotransduction hypotheses in vivo.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".