Coincidence Measurements in Dual-Color Confocal Microscopy: A Combined Single-Particle and Fluorescence Correlation Approach
Bibliographic record
Abstract
In this paper we discuss how the coincident detection of mobile particles in dual-color confocal images can be improved. Optimal coincidence detection requires a careful choice of experimental conditions and image acquisition parameters in order to maximize the overlap between the two detection volumes. By measuring this overlap with fluorescence cross-correlation spectroscopy, we show in particular that a small confocal field of view is necessary in order to maintain good coincidence. Most importantly, coincidence detection also requires a dedicated image analysis strategy. Traditionally, two approaches have been adopted to assess coincidence of mobile particles: fluorescence fluctuation measurements, notably cross-correlation spectroscopy, and single particle detection. Here we propose to combine these two approaches by calculating a cross-correlation coefficient for each of the detected single particles. We show that this allows to remove accidental coincidence events from a data set, and thus to unambiguously identify particles that instead carry two different fluorophores. This strategy can help increase the available concentration range for confocal coincidence measurements and detect rare binding events. [Formula: see text]Special Issue Comments: This article about coincident detection of mobile particle in two-color confocal images is thematically related to several articles in this Special Issue, namely the review of FRET-based single-molecule fluorescence techniques by Ruedas-Rama et al.,1 the single particle detection work presented by de Keersmaecker et al.2 and the general considerations on the mathematical treatment of single molecule trajectories presented by Flomenbom3. Our study of single liposomes is also relevant to experiments involving proteins and liposomes, such as the enzyme experiments described in the review by Jørgensen and Hatzakis.4
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".