A guide to accurate measurement of diffusion using fluorescence correlation techniques with blinking quantum dot nanoparticle labels
Bibliographic record
Abstract
Fluctuation-based fluorescence correlation techniques are widely used to study dynamics of fluorophore labeled biomolecules in cells. Semiconductor quantum dots (QDs) have been developed as bright and photostable fluorescent probes for various biological applications. However, the fluorescence intermittency of QDs, commonly referred to as "blinking", is believed to complicate quantitative correlation spectroscopy measurements of transport properties, as it is an additional source of fluctuations that contribute on a wide range of time scales. The QD blinking fluctuations obey power-law distributions so there is no single characteristic fluctuation time for this phenomenon. Consequently, it is highly challenging to separate fluorescence blinking fluctuations from those due to transport dynamics. Here, we quantify the bias introduced by QD blinking in transport measurements made using fluctuation methods. Using computer simulated image time series of diffusing point emitters with set "on" and "off" time emission characteristics, we show that blinking results in a systematic overestimation of the diffusion coefficients measured with correlation analysis when a simple diffusion model is used to fit the time correlation decays. The relative error depends on the inherent blinking power-law statistics, the sampling rate relative to the characteristic diffusion time and blinking times, and the total number of images in the time series. This systematic error can be significant; moreover, it can often go unnoticed in common transport model fits of experimental data. We propose an alternative fitting model that incorporates blinking and improves the accuracy of the recovered diffusion coefficients. We also show how to completely eliminate the bias by applying k-space image correlation spectroscopy, which completely separates the diffusion and blinking dynamics, and allows the simultaneous recovery of accurate diffusion coefficients and QD blinking probability distribution function exponents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".