Raster image correlation spectroscopy (RICS) for measuring fast protein dynamics and concentrations with a commercial laser scanning confocal microscope
Bibliographic record
Abstract
Raster image correlation spectroscopy (RICS) is a new and novel technique for measuring molecular dynamics and concentrations from fluorescence confocal images. The RICS technique extracts information about molecular dynamics and concentrations from images of living cells taken on commercial confocal systems. Here we develop guidelines for performing the RICS analysis on an analogue commercial laser scanning confocal microscope. Guidelines for typical instrument settings, image acquisition settings and analogue detector characterization are presented. Using appropriate instrument/acquisition parameters, diffusion coefficients and concentrations can be determined, even for highly dynamic dye molecules in solution. Standard curves presented herein demonstrate the ability to detect protein concentrations as low as approximately 2 nM. Additionally, cellular measurements give accurate values for the diffusion of paxillin-enhanced-green fluorescent protein (EGFP), an adhesion adaptor molecule, in the cytosol of the cell and also show slower paxillin dynamics near adhesions where paxillin interacts with immobile adhesion components. Methods are presented to account for bright immobile structures within the cell that dominate spatial correlation functions; allowing the extraction of fast protein dynamics within and near these structures. A running average algorithm is also presented to address slow cellular movement or movement of cellular features such as adhesions. Finally, methods to determine protein concentration in the presence of immobile structures within the cell are presented. A table is presented giving guidelines for instrument and imaging setting when performing RICS on the Olympus FV300 confocal and these guidelines are a starting point for performing the analysis on other commercial confocal systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".