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Local dimensionality determines imaging speed in localization microscopy.

ABSTRACT: Localization microscopy allows biological samples to be imaged at a length scale of tens of nanometres. Live-cell super-resolution imaging is rare, as it is generally assumed to be too slow for dynamic samples. The speed of data acquisition can be optimized by tuning the density of activated fluorophores in each time frame. Here, we show that the maximum achievable imaging speed for a particular structure varies by orders of magnitude, depending on the sample dimensionality (that is, whether the sample is more like a point, a strand or an extended structure such as a focal adhesion). If too high an excitation density is used, we demonstrate that the analysis undergoes silent failure, resulting in reconstruction artefacts. We are releasing a tool to allow users to identify areas of the image in which the activation density was too high and correct for them, in both live- and fixed-cell experiments.

SUBMITTER: Fox-Roberts P

PROVIDER: S-EPMC5241698 | biostudies-other | 2017 Jan

REPOSITORIES: biostudies-other

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Local dimensionality determines imaging speed in localization microscopy.

Fox-Roberts Patrick P Marsh Richard R Pfisterer Karin K Jayo Asier A Parsons Maddy M Cox Susan S

Nature communications 20170112

Localization microscopy allows biological samples to be imaged at a length scale of tens of nanometres. Live-cell super-resolution imaging is rare, as it is generally assumed to be too slow for dynamic samples. The speed of data acquisition can be optimized by tuning the density of activated fluorophores in each time frame. Here, we show that the maximum achievable imaging speed for a particular structure varies by orders of magnitude, depending on the sample dimensionality (that is, whether the ...[more]

PMID: 28079054

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Project description:To be able to resolve molecular-clusters it is crucial to access vital information (such as, molecule density, cluster-size, and others) that are key in understanding disease progression and the underlying mechanism. Traditional single-molecule localization microscopy (SMLM) techniques use molecules of variable sizes (as determined by its localization precision (LP)) to reconstruct a super-resolution map. This results in an image with overlapping and superimposing PSFs (due to a wide size-spectrum of single-molecules) that undermine image resolution. Ideally, it should be possible to identify the brightest molecules (also termed as the fortunate molecules) to reconstruct ultra-superresolution map, provided sufficient statistics is available from the recorded data. Probabilistic Optically-Selective Single-molecule Imaging Based Localization Encoded (POSSIBLE) microscopy explores this possibility by introducing a narrow probability size-distribution of single-molecules (narrow size-spectrum about a predefined mean-size). The reconstruction begins by presetting the mean and variance of the narrow distribution function (Gaussian function). Subsequently, the dataset is processed and single-molecules are filtered by the Gaussian function to remove unfortunate molecules. The fortunate molecules thus retained are then mapped to reconstruct an ultra-superresolution map. In-principle, the POSSIBLE microscopy technique is capable of infinite resolution (resolution of the order of actual single-molecule size) provided enough fortunate molecules are experimentally detected. In short, bright molecules (with large emissivity) holds the key. Here, we demonstrate the POSSIBLE microscopy technique and reconstruct single-molecule images with an average PSF sizes of σ ± Δσ = 15 ± 10 nm, 30 ± 2 nm & 50 ± 2 nm. Results show better-resolved Dendra2-HA clusters with large cluster-density in transfected NIH3T3 fibroblast cells as compared to the traditional SMLM techniques. Cluster analysis indicates densely-packed HA molecules, HA-HA interaction, and a surge in the number of HA molecules per cluster post 24 Hrs of transfection. The study using POSSIBLE microscopy introduces new insights in influenza biology. We anticipate exciting applications in the multidisciplinary field of disease biology, oncology, and biomedical imaging.

Dataset Information

Local dimensionality determines imaging speed in localization microscopy.

Publications

Local dimensionality determines imaging speed in localization microscopy.

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