Eukaryotic membrane tethers revisited using magnetic tweezers
Bibliographic record
Abstract
Membrane nanotubes, under physiological conditions, typically form en masse. We employed magnetic tweezers (MTW) to extract tethers from human brain tumor cells and compared their biophysical properties with tethers extracted after disruption of the cytoskeleton and from a strongly differing cell type, Chinese hamster ovary cells. In this method, the constant force produced with the MTW is transduced to cells through super-paramagnetic beads attached to the cell membrane. Multiple sudden jumps in bead velocity were manifest in the recorded bead displacement-time profiles. These discrete events were interpreted as successive ruptures of individual tethers. Observation with scanning electron microscopy supported the simultaneous existence of multiple tethers. The physical characteristics, in particular, the number and viscoelastic properties of the extracted tethers were determined from the analytic fit to bead trajectories, provided by a standard model of viscoelasticity. Comparison of tethers formed with MTW and atomic force microscopy (AFM), a technique where the cantilever-force transducer is moved at constant velocity, revealed significant differences in the two methods of tether formation. Our findings imply that extreme care must be used to interpret the outcome of tether pulling experiments performed with single molecular techniques (MTW, AFM, optical tweezers, etc). First, the different methods may be testing distinct membrane structures with distinct properties. Second, as soon as a true cell membrane (as opposed to that of a vesicle) can attach to a substrate, upon pulling on it, multiple nonspecific membrane tethers may be generated. Therefore, under physiological conditions, distinguishing between tethers formed through specific and nonspecific interactions is highly nontrivial if at all possible.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".