Dataset Information

Superresolution Imaging with Single-Antibody Labeling.

ABSTRACT: We present a versatile single-molecule localization microscopy technique utilizing time-lapse imaging of single-antibody labeling. By performing single-molecule imaging in the subminute time scale and tuning the antibody concentration to create sparse single-molecule binding, we captured antibody labeling of subcellular targets to generate superresolution images. Single-antibody labeling enabled dual-target superresolution imaging using dye-conjugated monoclonal and polyclonal antibodies. We further demonstrate a dual-color strategy to increase the sample labeling density. Single-antibody labeling paves a new way to evaluate antibody binding for superresolution imaging in the native cellular environment.

SUBMITTER: Gunasekara H

PROVIDER: S-EPMC10859171 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Superresolution Imaging with Single-Antibody Labeling.

Gunasekara Hirushi H Perera Thilini T Anderson Jesse J Saed Badeia B Ramseier Neal N Keshta Neama N Hu Ying S YS

Bioconjugate chemistry 20230505 5

We present a versatile single-molecule localization microscopy technique utilizing time-lapse imaging of single-antibody labeling. By performing single-molecule imaging in the subminute time scale and tuning the antibody concentration to create sparse single-molecule binding, we captured antibody labeling of subcellular targets to generate superresolution images. Single-antibody labeling enabled dual-target superresolution imaging using dye-conjugated monoclonal and polyclonal antibodies. We fur ...[more]

PMID: 37145839

Similar Datasets

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

Superresolution Imaging with Single-Antibody Labeling.

Publications

Superresolution Imaging with Single-Antibody Labeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets