Optimizing the Acquisition and Analysis of Confocal Images for Quantitative Single‐Mobile‐Particle Detection
Bibliographic record
Abstract
Quantification of the fluorescence properties of diffusing particles in solution is an invaluable source of information for characterizing the interactions, stoichiometry, or conformation of molecules directly in their native environment. In the case of heterogeneous populations, single-particle detection should be the method of choice and it can, in principle, be achieved by using confocal imaging. However, the detection of single mobile particles in confocal images presents specific challenges. In particular, it requires an adapted set of imaging parameters for capturing the confocal images and an adapted event-detection scheme for analyzing the image. Herein, we report a theoretical framework that allows a prediction of the properties of a homogenous particle population. This model assumes that the particles have linear trajectories with reference to the confocal volume, which holds true for particles with moderate mobility. We compare the predictions of our model to the results as obtained by analyzing the confocal images of solutions of fluorescently labeled liposomes. Based on this comparison, we propose improvements to the simple line-by-line thresholding event-detection scheme, which is commonly used for single-mobile-particle detection. We show that an optimal combination of imaging and analysis parameters allows the reliable detection of fluorescent liposomes for concentrations between 1 and 100 pM. This result confirms the importance of confocal single-particle detection as a complementary technique to ensemble fluorescence-correlation techniques for the studies of mobile particle.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".