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1.
Nat Nanotechnol ; 19(8): 1150-1157, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38750166

RESUMO

Multiplexed, real-time fluorescence detection at the single-molecule level can reveal the stoichiometry, dynamics and interactions of multiple molecular species in mixtures and other complex samples. However, fluorescence-based sensing is typically limited to the detection of just 3-4 colours at a time due to low signal-to-noise ratio, high spectral overlap and the need to maintain the chemical compatibility of dyes. Here we engineered a palette of several dozen composite fluorescent labels, called FRETfluors, for multiplexed spectroscopic measurements at the single-molecule level. FRETfluors are compact nanostructures constructed from three chemical components (DNA, Cy3 and Cy5) with tunable spectroscopic properties due to variations in geometry, fluorophore attachment chemistry and DNA sequence. We demonstrate FRETfluor labelling and detection for low-concentration (<100 fM) mixtures of mRNA, dsDNA and proteins using an anti-Brownian electrokinetic trap. In addition to identifying the unique spectroscopic signature of each FRETfluor, this trap differentiates FRETfluors attached to a target from unbound FRETfluors, enabling wash-free sensing. Although usually considered an undesirable complication of fluorescence, here the inherent sensitivity of fluorophores to the local physicochemical environment provides a new design axis complementary to changing the FRET efficiency. As a result, the number of distinguishable FRETfluor labels can be combinatorically increased while chemical compatibility is maintained, expanding prospects for spectroscopic multiplexing at the single-molecule level using a minimal set of chemical building blocks.


Assuntos
DNA , Transferência Ressonante de Energia de Fluorescência , Corantes Fluorescentes , Nanoestruturas , Transferência Ressonante de Energia de Fluorescência/métodos , Nanoestruturas/química , DNA/química , DNA/análise , Corantes Fluorescentes/química , Carbocianinas/química , Imagem Individual de Molécula/métodos , RNA Mensageiro/genética , RNA Mensageiro/análise , Proteínas/química , Proteínas/análise , Espectrometria de Fluorescência/métodos
2.
ArXiv ; 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38344222

RESUMO

Multiplexed, real-time fluorescence detection at the single-molecule level is highly desirable to reveal the stoichiometry, dynamics, and interactions of individual molecular species within complex systems. However, traditionally fluorescence sensing is limited to 3-4 concurrently detected labels, due to low signal-to-noise, high spectral overlap between labels, and the need to avoid dissimilar dye chemistries. We have engineered a palette of several dozen fluorescent labels, called FRETfluors, for spectroscopic multiplexing at the single-molecule level. Each FRETfluor is a compact nanostructure formed from the same three chemical building blocks (DNA, Cy3, and Cy5). The composition and dye-dye geometries create a characteristic F\"orster Resonance Energy Transfer (FRET) efficiency for each construct. In addition, we varied the local DNA sequence and attachment chemistry to alter the Cy3 and Cy5 emission properties and thereby shift the emission signatures of an entire series of FRET constructs to new sectors of the multi-parameter detection space. Unique spectroscopic emission of each FRETfluor is therefore conferred by a combination of FRET and this site-specific tuning of individual fluorophore photophysics. We show single-molecule identification of a set of 27 FRETfluors in a sample mixture using a subset of constructs statistically selected to minimize classification errors, measured using an Anti-Brownian ELectrokinetic (ABEL) trap which provides precise multi-parameter spectroscopic measurements. The ABEL trap also enables discrimination between FRETfluors attached to a target (here: mRNA) and unbound FRETfluors, eliminating the need for washes or removal of excess label by purification. We show single-molecule identification of a set of 27 FRETfluors in a sample mixture using a subset of constructs selected to minimize classification errors.

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