3D tracking of single nanoparticles and quantum dots in living cells by out-of-focus imaging with diffraction pattern recognition

Anno: 2015

Autori: Gardini L., Capitanio C., Pavone F.S.

Affiliazione autori: [Gardini, Capitanio, Pavone] LENS-European Laboratory for Non-Linear Spectroscopy; [Gardini, Pavone] INO-Istituto Nazionale di Ottica; [Capitanio, Pavone] Dipartimento di Fisica e Astronomia, Università degli Studi di Firenze

Abstract: Live cells are three-dimensional environments where biological molecules move to find their targets and accomplish their functions. However, up to now, most single molecule investigations have been limited to bi-dimensional studies owing to the complexity of 3d-tracking techniques. Here, we present a novel method for three-dimensional localization of single nano-emitters based on automatic recognition of out-of-focus diffraction patterns. Our technique can be applied to track the movements of single molecules in living cells using a conventional epifluorescence microscope. We first demonstrate three-dimensional localization of fluorescent nanobeads over 4 microns depth with accuracy below 2 nm in vitro. Remarkably, we also establish three-dimensional tracking of Quantum Dots, overcoming their anisotropic emission, by adopting a ligation strategy that allows rotational freedom of the emitter combined with proper pattern recognition. We localize commercially available Quantum Dots in living cells with accuracy better than 7 nm over 2 microns depth. We validate our technique by tracking the three-dimensional movements of single protein-conjugated Quantum Dots in living cell. Moreover, we find that important localization errors can occur in off-focus imaging when improperly calibrated and we give indications to avoid them. Finally, we share a Matlab script that allows readily application of our technique by other laboratories.

Giornale/Rivista: SCIENTIFIC REPORTS

Volume: 5      Da Pagina: 16088-1  A: 16088-10

Parole chiavi: Single-molecule imaging, 3D tracking, Quantum Dots, Pattern recognition, PROOF
DOI: 10.1038/srep16088

Citazioni: 32
dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2023-02-05
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