Innovations in ex vivo Light Sheet Fluorescence Microscopy

Delgado-Rodriguez, Pablo; Brooks, Claire J.; Vaquero, J.J.; Muñoz-Barrutia, Arrate

doi:10.1016/j.pbiomolbio.2021.07.002

Cited by 10 publications

(8 citation statements)

References 108 publications

(165 reference statements)

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“…35,36 Another limitation to the broad adoption of LSFM is the necessary optical clearing step, and validation of compatible antibodies for immunostaining, both of which remain a large time investment. 37 Additionally, with regards to the Labkit analysis, the application segmentation algorithm classifies pixels independent of their position in the image. 17 In the case where significant adventitial fat is still present, for example, Labkit may include this tissue in the final plaque volume.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning and Virtual Reality for Volumetric Analysis of Arterial Lesions

Cartaya¹,

Maiocchi²,

Torzone³

et al. 2022

Preprint

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Cardiovascular disease (CVD) remains the leading cause of mortality worldwide. Preclinical studies to research and validate therapeutic interventions for CVD often depend on two-dimensional histological surveys. The use of light sheet fluorescent microscopy together with optical clearing methods amenable to immunofluorescent staining are recent advances, all of which deliver detailed three-dimensional rendering of vessels. This offers the ability to describe and quantify features critical in CVD models, specifically, atherosclerotic plaque burden in atherosclerotic animal models. The main challenge for this approach remains the lengthy, hands-on, analysis time. Labkit is a user-friendly Fiji plugin that applies a machine-learning algorithm to create three-dimensional renderings from large microscopy data. The application of this plugin is expected to decrease the hands-on analysis time required to generate accurate volumetric renderings of atherosclerotic plaque burden in athero-prone mice. For analysis, Ldlr -/- (C57/Bl6) mice aged 6-8 weeks were fed a high-fat diet for 15 weeks to allow the development of atherosclerotic plaque along the aorta. Aged-matched chow-fed C57/Bl6 mice were used as athero-free controls. Aortic roots were sectioned and stained with hematoxylin and eosin, or oil red o stains, and later imaged and analyzed using ImageJ. AdipoClear and immunolabeling together with light-sheet fluorescent microscopy allowed for three-dimensional visualization. Both Imaris software v9.9.1 and the built-in bridge to ImageJ/Labkit were used to quantify the plaque burden in the mice manually or automatically, respectively. Our findings indicate that Labkit offers an effective and user-friendly platform for the segmentation of atherosclerotic plaque in aortas.

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Section: Discussionmentioning

confidence: 99%

Application of Machine Learning and Virtual Reality for Volumetric Analysis of Arterial Lesions

Cartaya¹,

Maiocchi²,

Torzone³

et al. 2022

Preprint

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“…Light sheet (LS)- fluorescence microscopy has several advantages over point-scanning confocal microscopy, including faster acquisition, reduced scattered light, and less photobleaching and phototoxicity to cells ( Huisken et al, 2004 ; Delgado-Rodriguez et al, 2022 ) ( Figure 1C ). In LS microscopy, a sheet of light is produced within the sample that aligns with the focal plane of a high-NA objective orthogonal to the light sheet ( Peel and Jackman, 2021 ).…”

Section: Microscopy Techniques For Imaging Cerebral Organoidsmentioning

confidence: 99%

Applications of multiphoton microscopy in imaging cerebral and retinal organoids

Lacin,

Yildirim

2024

Front. Neurosci.

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Cerebral organoids, self-organizing structures with increased cellular diversity and longevity, have addressed shortcomings in mimicking human brain complexity and architecture. However, imaging intact organoids poses challenges due to size, cellular density, and light-scattering properties. Traditional one-photon microscopy faces limitations in resolution and contrast, especially for deep regions. Here, we first discuss the fundamentals of multiphoton microscopy (MPM) as a promising alternative, leveraging non-linear fluorophore excitation and longer wavelengths for improved imaging of live cerebral organoids. Then, we review recent applications of MPM in studying morphogenesis and differentiation, emphasizing its potential for overcoming limitations associated with other imaging techniques. Furthermore, our paper underscores the crucial role of cerebral organoids in providing insights into human-specific neurodevelopmental processes and neurological disorders, addressing the scarcity of human brain tissue for translational neuroscience. Ultimately, we envision using multimodal multiphoton microscopy for longitudinal imaging of intact cerebral organoids, propelling advancements in our understanding of neurodevelopment and related disorders.

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“…Like tissue clearing, the literature on light sheet microscopy is prolific and somewhat confusing given the diversity of terminology and abbreviations used to designate the multiple systems and variants of the technique ( Lemon and McDole, 2020 ). Notably, many research groups have implemented their home-built systems, with a variety of features, such as multiview imaging, dipping lenses, or closed imaging chambers ( Delgado-Rodriguez et al, 2022 ; Olarte et al, 2018 ; Ueda et al, 2020b ). Open-source designs are accessible online, such as OpenSPIM ( Pitrone et al, 2013 ) or mesoSPIM ( Voigt et al, 2019 ), offering a cheaper “do-it-yourself” alternative compared with commercial light sheets, which have appeared on the market relatively recently.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Open-source designs are accessible online, such as OpenSPIM ( Pitrone et al, 2013 ) or mesoSPIM ( Voigt et al, 2019 ), offering a cheaper “do-it-yourself” alternative compared with commercial light sheets, which have appeared on the market relatively recently. While this approach is ideal for matching the specific needs of a given research project, it requires time and technical expertise, and the resulting equipment may lack versatility in its applications ( Delgado-Rodriguez et al, 2022 ; Girstmair et al, 2016 ). Therefore, core facilities or other multiuser environments often prefer turnkey commercial systems, which are more user-friendly and can accommodate a wide range of biological samples and imaging media, including organic solvents.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Whether these techniques are physical, based on mirrors or spatial light modulators (adaptive optics; Ji, 2017 ; Liu et al, 2023 ; Olarte et al, 2018 ), part of a complete framework ( Royer et al, 2018 ) or, more recently, deep learning based ( Rai et al, 2023 ), they can significantly improve the quality of acquisitions. Excellent reviews summarizing the strategies allowing for improvement of LSFM resolution can be found in the literature ( Delgado-Rodriguez et al, 2022 ; Fiolka, 2021 ; Lemon and McDole, 2020 ; Olarte et al, 2018 ; Parthasarathy, 2018 ; Watkins and St. Croix, 2018 ). However, it should be noted that most of these advanced systems are not widespread and will not be accessible to many users.…”

Section: Image Acquisitionmentioning

confidence: 99%

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Successful 3D imaging of cleared biological samples with light sheet fluorescence microscopy

Delage,

Guilbert,

Yates

2023

Journal of Cell Biology

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In parallel with the development of tissue-clearing methods, over the last decade, light sheet fluorescence microscopy has contributed to major advances in various fields, such as cell and developmental biology and neuroscience. While biologists are increasingly integrating three-dimensional imaging into their research projects, their experience with the technique is not always up to their expectations. In response to a survey of specific challenges associated with sample clearing and labeling, image acquisition, and data analysis, we have critically assessed the recent literature to characterize the difficulties inherent to light sheet fluorescence microscopy applied to cleared biological samples and to propose solutions to overcome them. This review aims to provide biologists interested in light sheet fluorescence microscopy with a primer for the development of their imaging pipeline, from sample preparation to image analysis. Importantly, we believe that issues could be avoided with better anticipation of image analysis requirements, which should be kept in mind while optimizing sample preparation and acquisition parameters.

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Innovations in ex vivo Light Sheet Fluorescence Microscopy

Cited by 10 publications

References 108 publications

Application of Machine Learning and Virtual Reality for Volumetric Analysis of Arterial Lesions

Application of Machine Learning and Virtual Reality for Volumetric Analysis of Arterial Lesions

Applications of multiphoton microscopy in imaging cerebral and retinal organoids

Successful 3D imaging of cleared biological samples with light sheet fluorescence microscopy

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