Super-resolution microscopy is broadening our in-depth understanding of cellular structure. However, super-resolution approaches are limited, for numerous reasons, from utilization in longer-term intravital imaging. We devised a combinatorial imaging technique that combines deconvolution with stepwise optical saturation microscopy (DeSOS) to circumvent this issue and image cells in their native physiological environment. Other than a traditional confocal or two-photon microscope, this approach requires no additional hardware. Here, we provide an open-access application to obtain DeSOS images from conventional microscope images obtained at low excitation powers. We show that DeSOS can be used in time-lapse imaging to generate super-resolution movies in zebrafish. DeSOS was also validated in live mice. These movies uncover that actin structures dynamically remodel to produce a single pioneer axon in a 'top-down' scaffolding event. Further, we identify an F-actin population - stable base clusters - that orchestrate that scaffolding event. We then identify that activation of Rac1 in pioneer axons destabilizes stable base clusters and disrupts pioneer axon formation. The ease of acquisition and processing with this approach provides a universal technique for biologists to answer questions in living animals.
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http://dx.doi.org/10.1242/dev.171512 | DOI Listing |
bioRxiv
June 2024
Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows biologists to observe and analyze cellular activities in real-time. The analysis of time-lapse microscopy data relies on two fundamental tasks: cell segmentation and cell tracking.
View Article and Find Full Text PDFMol Microbiol
April 2024
Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg Eppendorf, Hamburg, Germany.
Super-resolution fluorescence microscopy technologies developed over the past two decades have pushed the resolution limit for fluorescently labeled molecules into the nanometer range. These technologies have the potential to study bacterial structures, for example, macromolecular assemblies such as secretion systems, with single-molecule resolution on a millisecond time scale. Here we review recent applications of super-resolution fluorescence microscopy with a focus on bacterial secretion systems.
View Article and Find Full Text PDFBiol Imaging
February 2023
Department of Computer Science, Duke University, Durham, North Carolina, USA.
Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest.
View Article and Find Full Text PDFFront Bioinform
November 2021
Third Institute of Physics-Biophysics, Georg August University, Göttingen, Germany.
Fluorescence-lifetime single molecule localization microscopy (FL-SMLM) adds the lifetime dimension to the spatial super-resolution provided by SMLM. Independent of intensity and spectrum, this lifetime information can be used, for example, to quantify the energy transfer efficiency in Förster Resonance Energy Transfer (FRET) imaging, to probe the local environment with dyes that change their lifetime in an environment-sensitive manner, or to achieve image multiplexing by using dyes with different lifetimes. We present a thorough theoretical analysis of fluorescence-lifetime determination in the context of FL-SMLM and compare different lifetime-fitting approaches.
View Article and Find Full Text PDFPLoS One
August 2022
School of Software, Hallym University, Chuncheon, Gangwon, Republic of Korea.
We propose an anisotropic constrained-boundary convolutional neural networks (hereafter, AnisoCBConvNet) that can stably express high-quality meshes without oscillation by applying super-resolution operations to low-resolution cloth meshes. As a training set for the neural network, we use a pair between simulation data of low resolution (LR) cloth and data obtained by applying the same simulation to high resolution (HR) cloth with increased quad mesh resolution of LR cloth. The actual data used for training are 2D geometry images converted from 3D meshes.
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