Dataset Information

Single-molecule recognition imaging microscopy.

ABSTRACT: Atomic force microscopy is a powerful and widely used imaging technique that can visualize single molecules and follow processes at the single-molecule level both in air and in solution. For maximum usefulness in biological applications, atomic force microscopy needs to be able to identify specific types of molecules in an image, much as fluorescent tags do for optical microscopy. The results presented here demonstrate that the highly specific antibody-antigen interaction can be used to generate single-molecule maps of specific types of molecules in a compositionally complex sample while simultaneously carrying out high-resolution topographic imaging. Because it can identify specific components, the technique can be used to map composition over an image and to detect compositional changes occurring during a process.

SUBMITTER: Stroh C

PROVIDER: S-EPMC514657 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature

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Single-molecule recognition imaging microscopy.

Stroh C C Wang H H Bash R R Ashcroft B B Nelson J J Gruber H H Lohr D D Lindsay S M SM Hinterdorfer P P

Proceedings of the National Academy of Sciences of the United States of America 20040816 34

Atomic force microscopy is a powerful and widely used imaging technique that can visualize single molecules and follow processes at the single-molecule level both in air and in solution. For maximum usefulness in biological applications, atomic force microscopy needs to be able to identify specific types of molecules in an image, much as fluorescent tags do for optical microscopy. The results presented here demonstrate that the highly specific antibody-antigen interaction can be used to generate ...[more]

PMID: 15314231

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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

Single-molecule recognition imaging microscopy.

Publications

Single-molecule recognition imaging microscopy.

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