Accelerating 3D single-molecule localization microscopy using blind sparse inpainting

Gaire, Sunil Kumar; Wang, Yanhua; Liang, Dong; Ying, Leslie

doi:10.1117/1.jbo.26.2.026501

Cited by 6 publications

(1 citation statement)

References 55 publications

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“…Building upon wide-field imaging principles, SMLM methods exploit the ability to precisely determine the spatial coordinates of single fluorophores provided their point spread functions (PSFs) remain non-overlap and emit a sufficient number of photons 7 – 9 Recently, spectroscopic single-molecule localization microscopy (sSMLM) has emerged that utilizes additional spectral information, often overlooked in conventional SMLM imaging 10 , 11 . In sSMLM, incorporating a dispersive optical component, such as a prism or grating, emission light from the sample is split into two imaging paths, both of which are captured using a single camera.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based spectroscopic single-molecule localization microscopy

Gaire,

Daneshkhah,

Flowerday

et al. 2024

J. Biomed. Opt.

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Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10 nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale.Aim: Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data.Approach: We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including labelfree single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler.Results: For label-free imaging, a spatial resolution of 6.22 nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40 nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging. Conclusions:We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality superresolution imaging for a deeper understanding of DNA molecules' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based spectroscopic single-molecule localization microscopy

Gaire,

Daneshkhah,

Flowerday

et al. 2024

J. Biomed. Opt.

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Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Özdemir

Reski

2021

Computational and Structural Biotechnology Journal

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Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

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From whole organism to ultrastructure: progress in axonal imaging for decoding circuit development

Weaver

Poulain

2021

Development

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Since the pioneering work of Ramón y Cajal, scientists have sought to unravel the complexities of axon development underlying neural circuit formation. Micrometer-scale axonal growth cones navigate to targets that are often centimeters away. To reach their targets, growth cones react to dynamic environmental cues that change in the order of seconds to days. Proper axon growth and guidance are essential to circuit formation, and progress in imaging has been integral to studying these processes. In particular, advances in high- and super-resolution microscopy provide the spatial and temporal resolution required for studying developing axons. In this Review, we describe how improved microscopy has revolutionized our understanding of axonal development. We discuss how novel technologies, specifically light-sheet and super-resolution microscopy, led to new discoveries at the cellular scale by imaging axon outgrowth and circuit wiring with extreme precision. We next examine how advanced microscopy broadened our understanding of the subcellular dynamics driving axon growth and guidance. We finally assess the current challenges that the field of axonal biology still faces for imaging axons, and examine how future technology could meet these needs.

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Accelerating 3D single-molecule localization microscopy using blind sparse inpainting

Cited by 6 publications

References 55 publications

Deep learning-based spectroscopic single-molecule localization microscopy

Deep learning-based spectroscopic single-molecule localization microscopy

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

From whole organism to ultrastructure: progress in axonal imaging for decoding circuit development

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