Expanded Quantum Dot-Based Concentric Förster Resonance Energy Transfer: Adding and Characterizing Energy-Transfer Pathways for Triply Multiplexed Biosensing
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Concentric Förster resonance energy transfer (cFRET) is an emerging application of semiconductor quantum dots (QDs) that takes advantage of their excellent photoluminescence (PL) properties, small size, and versatile surface area. To date, cFRET configurations have combined QDs with only two fluorescent dyes, limiting multiplexed bioanalysis to two targets. Here, we expanded cFRET to create a triply multiplexed configuration that comprised a central QD assembled with multiple copies of three different peptide sequences labeled with one of three different fluorescent dyes. These dyes were Alexa Fluor 555 (A555), either Cyanine 3.5 (Cy3.5) or Atto 594 (At594), and Alexa Fluor 647 (A647). The expanded cFRET configuration had double the number of FRET pathways of previous systems, with three competitive pathways from the QD to the dyes and three sequential pathways between the dyes. The quenching efficiencies for QD and dye PL in the full three-acceptor cFRET configuration were successfully predicted from rate analyses of the simpler one- and two-acceptor configurations. This capability provides a basis for a priori design of cFRET probes with new luminescent materials or for new applications. Importantly, combinations of the A555/QD, Cy3.5/QD or At594/QD, and A647/QD PL intensity ratios were unique to particular combinations of the number of each acceptor dye per QD, permitting calculation of the number of each dye per QD from the measured PL ratios. This uniqueness was the basis of quantitative biosensing and enabled multiplexed and selective assays of the activities of three proteases (trypsin, chymotrypsin, and enterokinase) in parallel. The three-acceptor cFRET configuration is a potentially powerful multifunctional probe vector that challenges the current paradigm of N colors of QD for N biological targets. It is anticipated to be adaptable to many types of multiplexed bioanalysis and bioimaging applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".