Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy
Bibliographic record
Abstract
Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".