Concentric FRET: a review of the emerging concept, theory, and applications
Bibliographic record
Abstract
Concentric Förster resonance energy transfer (cFRET) is an emerging concept for single-vector multiplexed bioanalysis and imaging. It features a network of competitive and sequential energy transfer pathways, which, to date, has been assembled with a central semiconductor quantum dot (QD) and biomolecular linkers to multiple copies of multiple types of concentrically-arranged fluorescent dyes. In this review, we provide a first-hand account of the concept and development of cFRET, starting from its place in the broader context of FRET probes and assemblies. Topics of discussion include materials for cFRET, with a focus on the enabling properties of QDs and the ideal properties of nominal acceptor dyes; characterization and analysis of cFRET configurations via photoluminescence intensity, emission ratio, lifetime, and photobleaching measurements; semi-empirical modeling to determine the rates and efficiencies of competitive and sequential FRET pathways from overall quenching efficiencies; and archetypical examples of cFRET configurations and their application in bioanalysis and imaging. Most of the latter examples demonstrate multiplexed detection of protease activity or nucleic acid targets. Examples of atypical and cFRET-like configurations are also discussed, including those that utilize time-gated FRET relays and charge-transfer quenching. We conclude with a perspective on challenges and directions for future research with cFRET. Although still emerging as a method, many exciting opportunities in bioanalysis, imaging, and beyond are envisioned for cFRET.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".