Single‐molecule localization microscopy of septin bundles in mammalian cells
Bibliographic record
Abstract
Septins are a conserved family of GTPases that associate with numerous components of the cytoskeleton and the inner leaflet of the plasma membrane. These proteins are involved in many biological processes, including cell division and membrane trafficking, and serving as a scaffolding component of the cytoskeleton used to recruit other proteins and form diffusion barriers to maintain the composition of membrane domains. In order to carry out their cellular functions, septins undergo interactions via their NC or G interfaces to form heteromeric rod-like structures that can polymerize into filaments and associate laterally into bundles. While electron microscopy studies of affinity-tagged and purified Saccharomyces cerevisiae septin complexes have provided evidence for this periodic organization and in-registry lateral bundling in vitro, the in-vivo arrangement of stress fiber-associated septin bundles in mammalian cells remains poorly characterized. We report here on a direct stochastic optical reconstruction microscopy and photoactivated localization microscopy study of the 2D spatial distribution of septins in mammalian cells. From simulated and experimental results, we show the effects of labeling method, labeling efficiency, and fluorescent emitter photophysics on image reconstruction and interpretation. Our experimental results are consistent with septin organization by polymerization of hetero-octamers and an approximate 30-35 nm periodicity between subsequent units of SEPT2-SEPT2 or SEPT9-SEPT9.
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 | 0.000 | 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".