Computational methods in super-resolution microscopy

Zeng, Zhiping; Xie, Hao; Chen, Long; Zhanghao, Karl; Zhao, Kaixuan; Yang, Xusan; Xi, Peng

doi:10.1631/fitee.1601628

Cited by 22 publications

(14 citation statements)

References 56 publications

(80 reference statements)

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“…On the other hand, since the image in fluorescence microscopy can be modeled as a convolution of an ideal object with the PSF of the optical system, adding photon/detection noises, deconvolution can be used to solve an inverse problem and present an enhanced resolution and contrast. This strategy is frequently used in many microscopy techniques, such as confocal microscopy (McNally et al, 1999), superresolution microscopy (Hugelier et al, 2018) (Zeng et al, 2017), and electron microscopy (Roels et al, 2018). In de-convolution, the PSF can be obtained from theory or experiments, or even regarded as an unknown operator (corresponding to blind deconvolution), and the inverse problem can be solved using linear methods (inverse filtering, Wiener filters, linear least squares, etc.…”

Section: Indirect Measurement Of Image Resolutionmentioning

confidence: 99%

Rethinking resolution estimation in fluorescence microscopy: from theoretical resolution criteria to super-resolution microscopy

Huang

2020

Sci. China Life Sci.

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Resolution is undoubtedly the most important parameter in optical microscopy by providing an estimation on the maximum resolving power of a certain optical microscope. For centuries, the resolution of an optical microscope is generally considered to be limited only by the numerical aperture of the optical system and the wavelength of light. However, since the invention and popularity of various advanced fluorescence microscopy techniques, especially super-resolution fluorescence microscopy, many new methods have been proposed for estimating the resolution, leading to confusions for researchers who need to quantify the resolution of their fluorescence microscopes. In this paper, we firstly summarize the early concepts and criteria for predicting the resolution limit of an ideal optical system. Then, we discuss some important influence factors that deteriorate the resolution of a certain fluorescence microscope. Finally, we provide methods and examples on how to measure the resolution of a fluorescence microscope from captured fluorescence images. This paper aims to answer as best as possible the theoretical and practical issues regarding the resolution estimation in fluorescence microscopy.

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Section: Indirect Measurement Of Image Resolutionmentioning

confidence: 99%

Rethinking resolution estimation in fluorescence microscopy: from theoretical resolution criteria to super-resolution microscopy

Huang

2020

Sci. China Life Sci.

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“…Therefore, in any apparentely homogeneous biological sample a certain degree of diversity in the imaged molecular conformations is expected. Due to the fact that valuable biological information can be retrieved from the imaged structure, it is currently required to develop computational methods to analyze in detail the microscopy data of biomolecules [16], [17]. Such a scenario is even more critical when imaging biomolecules using AFM.…”

Section: Introductionmentioning

confidence: 99%

Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks

et al. 2019

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Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg 2+. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules. INDEX TERMS Artificial neural networks, atomic force microscopy (AFM), biomolecules, growing cell structures (GCS), hepatitis C virus (HCV), Image analysis, internal ribosome entry site (IRES), ribonucleic acid (RNA), self-organizing maps (SOM).

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“…In addition, superresolution by deconvolution could be also included (Zeng et al, 2017). This aims to remove corruption and out-of-focus contribution within image data.…”

Section: Introductionmentioning

confidence: 99%

Quantitative colocalization analysis of DNA delivery by PEI‐mediated cationic polymers in mammalian cells

Cervera

Gòdia

Roldán

2018

Journal of Microscopy

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Summary Although cationic polymers are widely used for DNA delivery, the relationship between the properties of the formed complexes and their biological activity is not fully understood. Here, we propose a novel procedure consisting of superresolved images coupled with quantitative colocalization to analyse DNA release in living cells. This work compares the different workflows available in a quantitative colocalization study of DNA delivery using polyethylenimine as transfection reagent. A nimble workflow with deconvolution in three‐dimensional images was developed. Among the different colocalization coefficients, Manders' colocalization coefficient was the best to track the complexes. Results showed that DNA/polyethylenimine complexes were tightly interacting at the time of transfection and their disassembly was observed between 2 and 10 h after their uptake. Heterogenicity was found in the intracellular fate of each complex. At 24 h, some complexes were still present underneath the nuclear envelope. Overall, this study opens the door for particle tracking assessment with three‐dimensional imaging at intracellular level. Lay Description DNA delivery technologies in living cells are of high relevance in the biotechnology field. The transient expression of a gene of interest enables the production of a wide range of new therapeutic candidates for clinical purposes. However, the introduction of an exogenous DNA construct into a cell culture requires the use of certain vehicles that protect the DNA from host cell DNases and deliver it into the cell nucleus. From the different systems available, polyethylenimine (PEI) has been extensively used in transient gene expression strategies for the last three decades. However, the intracellular fate of the formed DNA/PEI complexes and the DNA release from the complexes is still poorly understood. In this work, we propose the application of combined superresolved images through mathematical deconvolution to colocalization studies of DNA/PEI complexes evolution in living mammalian cell cultures. Both specimens were covalently labelled with Cy3 and Cy5 dye, respectively, and the kinetics of its disassembly process within the cells was tracked over the time. Because of the specific features of the formed‐complexes, a comparative study of the different colocalization coefficients was performed towards optimizing the analysis of these particles with confocal microscopy. Besides, the 3D imaging of the process allowed the direct visualization of a partial DNA/PEI complexes disassembly and the location of those complexes underneath the nuclear envelope during the cell production phase (24 h after the uptake).

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Computational methods in super-resolution microscopy

Cited by 22 publications

References 56 publications

Rethinking resolution estimation in fluorescence microscopy: from theoretical resolution criteria to super-resolution microscopy

Rethinking resolution estimation in fluorescence microscopy: from theoretical resolution criteria to super-resolution microscopy

Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks

Quantitative colocalization analysis of DNA delivery by PEI‐mediated cationic polymers in mammalian cells

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