Evidence map›Paper›PMID 26526410›Full record

ArticleScientific reports2015

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

Lucia Gardini, Marco Capitanio, Francesco S Pavone

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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  9. Holographic Traction Force Microscopy.Scientific reports · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lucia GardiniLENS - European Laboratory for Non-linear Spectroscopy, Via Nello Carrara 1, 50019 Sesto Fiorentino, Italy.
Marco CapitanioLENS - European Laboratory for Non-linear Spectroscopy, Via Nello Carrara 1, 50019 Sesto Fiorentino, Italy.
Francesco S PavoneLENS - European Laboratory for Non-linear Spectroscopy, Via Nello Carrara 1, 50019 Sesto Fiorentino, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Microscopy, FluorescenceCell Line, TumorFluorescent DyesGangliosidesHumansImaging, Three-DimensionalNanoparticlesQuantum DotsFluorescent DyesGangliosides

Identifiers

PMID26526410
PMCPMC4630642

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.