Investigating Uncertainties in Single-Molecule Localization Microscopy Using Experimentally Informed Monte Carlo Simulation.
Wei-Hong YeoYang ZhangAmy E NeelyXiaomin BaoHao F ZhangHao F ZhangPublished in: Nano letters (2023)
Single-molecule localization microscopy (SMLM) enables the visualization of cellular nanostructures in vitro with sub-20 nm resolution. While substructures can generally be imaged with SMLM, the structural understanding of the images remains elusive. To better understand the link between SMLM images and the underlying structure, we developed a Monte Carlo (MC) simulation based on experimental imaging parameters and geometric information to generate synthetic SMLM images. We chose the nuclear pore complex (NPC), a nanosized channel on the nuclear membrane which gates nucleo-cytoplasmic transport of biomolecules, as a test geometry for testing our MC model. Using the MC model to simulate SMLM images, we first optimized our clustering algorithm to separate >10 6 molecular localizations of fluorescently labeled NPC proteins into hundreds of individual NPCs in each cell. We then illustrated using our MC model to generate cellular substructures with different angles of labeling to inform our structural understanding through the SMLM images obtained.
Keyphrases
- single molecule
- deep learning
- monte carlo
- convolutional neural network
- optical coherence tomography
- atomic force microscopy
- living cells
- single cell
- high resolution
- machine learning
- healthcare
- stem cells
- photodynamic therapy
- mass spectrometry
- computed tomography
- bone marrow
- mesenchymal stem cells
- high speed
- health information
- positron emission tomography
- pet imaging