Wavelet Imaging on Multiple Scales (WIMS) reveals focal adhesion distributions, dynamics and coupling between actomyosin bundle stability
Bibliographic record
Abstract
We introduce and use Wavelet Imaging on Multiple Scales (WIMS) as an improvement to fluorescence correlation spectroscopy to measure physical processes and features that occur across multiple length scales. In this study, wavelet transforms of cell images are used to characterize molecular dynamics at the cellular and subcellular levels (i.e. focal adhesions). We show the usefulness of the technique by applying WIMS to an image time series of a migrating osteosarcoma cell expressing fluorescently labelled adhesion proteins, which allows us to characterize different components of the cell ranging from optical resolution scale through to focal adhesion and whole cell size scales. Using WIMS we measured focal adhesion numbers, orientation and cell boundary velocities for retraction and protrusion. We also determine the internal dynamics of individual focal adhesions undergoing assembly, disassembly or elongation. Thus confirming as previously shown, WIMS reveals that the number of adhesions and the area of the protruding region of the cell are strongly correlated, establishing a correlation between protrusion size and adhesion dynamics. We also apply this technique to characterize the behavior of adhesions, actin and myosin in Chinese hamster ovary cells expressing a mutant form of myosin IIB (1935D) that displays decreased filament stability and impairs front-back cell polarity. We find separate populations of actin and myosin at each adhesion pole for both the mutant and wild type form. However, we find these populations move rapidly inwards toward one another in the mutant case in contrast to the cells that express wild type myosin IIB where those populations remain stationary. Results obtained with these two systems demonstrate how WIMS has the potential to reveal novel correlations between chosen parameters that belong to different scales.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".