scholarly journals 3D microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue

2021 ◽  
Vol 7 (8) ◽  
pp. eabe2480
Author(s):  
Junlong Geng ◽  
Xiaohui Zhang ◽  
Suma Prabhu ◽  
Sayyed Hamed Shahoei ◽  
Erik R. Nelson ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Deep Learning ◽  
Structural Characteristics ◽  
Three Dimensional ◽  
Light Sheet ◽  
Analysis Pipeline ◽  
Lean Tissue ◽  
Light Sheet Microscopy ◽  
Cell Densities ◽  
Visceral Adipose

Crown-like structures (CLSs) are adipose microenvironments of macrophages engulfing adipocytes. Their histological density in visceral adipose tissue (VAT) predicts metabolic disorder progression in obesity and is believed to initiate obesity comorbidities. Here, we use three-dimensional (3D) light sheet microscopy and deep learning to quantify 3D features of VAT CLSs in lean and obese states. Obese CLS densities are significantly higher, composing 3.9% of tissue volume compared with 0.46% in lean tissue. Across the states, individual CLS structural characteristics span similar ranges; however, subpopulations are distinguishable. Obese VAT contains large CLSs absent from lean tissues, located near the tissue center, while lean CLSs have higher volumetric cell densities and prolate shapes. These features are consistent with inefficient adipocyte elimination in obesity that contributes to chronic inflammation, representing histological biomarkers to assess adipose pathogenesis. This tissue processing, imaging, and analysis pipeline can be applied to quantitatively classify 3D microenvironments across diverse tissues.

scholarly journals Segmentor: A tool for manual refinement of 3D microscopy annotations

2021 ◽  
Author(s):  
David Borland ◽  
Carolyn M. McCormick ◽  
Niyanta K. Patel ◽  
Oleh Krupa ◽  
Jessica T. Mory ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Three Dimensional ◽  
Light Sheet ◽  
Training Data ◽  
Great Promise ◽  
Manual Annotation ◽  
Light Sheet Microscopy ◽  
Link Type ◽  
Segmentation Algorithms

AbstractBackgroundRecent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of manually and accurately labeled nuclei as training data.ResultsWe present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy.ConclusionsSegmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor.

scholarly journals Visualization and Analysis of Whole Depot Adipose Tissue Innervation

2019 ◽  
Author(s):  
Jake W. Willows ◽  
Magdalena Blaszkiewicz ◽  
Amy Lamore ◽  
Samuel Borer ◽  
Amanda L. Dubois ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Second Harmonic ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Light Sheet ◽  
Tissue Imaging ◽  
Label Free ◽  
Adipose Tissues ◽  
Light Sheet Microscopy ◽  
Neurite Density

AbstractAdipose tissue requires neural innervation in order to regulate important metabolic functions. Though seminal work on adipose denervation has underscored the importance of adipose-nerve interactions in both white (energy storing) and brown (energy expending) adipose tissues, much remains a mystery. This is due, in part, to the inability to effectively visualize the various nerve subtypes residing within these tissues and to gain a comprehensive quantitation of neurite density in an entire depot. With the recent surge of advanced imaging techniques such as light sheet microscopy and optical clearing procedures, adipose tissue imaging has been reinvigorated with a focus on three-dimensional analysis of tissue innervation. However, clearing techniques are time consuming, often require solvents caustic to objective lenses, alter tissue morphology, and greatly reduce fluorophore lifespan. Not only are current methods of imaging wholemount adipose tissues inconvenient, but often attempts to quantify neurite density across physiological or pathophysiological conditions have been limited to representative section sampling. We have developed a new method of adipose tissue neurite imaging and quantitation that is faster than current clearing-based methods, does not require caustic chemicals, and leaves the tissue fully intact. Maintenance of a fully intact depot allowed for tiling z-stacks and producing maximum intensity projections of the entire adipose depot, which were then used to quantify neurite density across the tissue. With this processing method we were able to characterize the nerves, nerve-subtypes, and neurovascular interactions within the inguinal subcutaneous white adipose tissue in mice using up to five fluorescent channels at high resolution. We also utilized second harmonic generation, which provides label-free imaging, to investigate collagen fiber abundance in adipose of obese mice.

scholarly journals Segmentor: a tool for manual refinement of 3D microscopy annotations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
David Borland ◽  
Carolyn M. McCormick ◽  
Niyanta K. Patel ◽  
Oleh Krupa ◽  
Jessica T. Mory ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Three Dimensional ◽  
Light Sheet ◽  
Training Data ◽  
Great Promise ◽  
Manual Annotation ◽  
Light Sheet Microscopy ◽  
Link Type ◽  
Segmentation Algorithms

Abstract Background Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. Results We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. Conclusions Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor.

scholarly journals Three-dimensional quantification of twisting in the Arabidopsis petiole

2021 ◽  
Author(s):  
Yuta Otsuka ◽  
Hirokazu Tsukaya
Keyword(s):  
Cross Sections ◽  
Three Dimensional ◽  
Light Sheet ◽  
Underlying Mechanism ◽  
Two Dimensions ◽  
Observation Angle ◽  
Differential Growth ◽  
Light Sheet Microscopy ◽  
Definition Of ◽  
Twisting Angle

AbstractOrganisms have a variety of three-dimensional (3D) structures that change over time. These changes include twisting, which is 3D deformation that cannot happen in two dimensions. Twisting is linked to important adaptive functions of organs, such as adjusting the orientation of leaves and flowers in plants to align with environmental stimuli (e.g. light, gravity). Despite its importance, the underlying mechanism for twisting remains to be determined, partly because there is no rigorous method for quantifying the twisting of plant organs. Conventional studies have relied on approximate measurements of the twisting angle in 2D, with arbitrary choices of observation angle. Here, we present the first rigorous quantification of the 3D twisting angles of Arabidopsis petioles based on light sheet microscopy. Mathematical separation of bending and twisting with strict definition of petiole cross-sections were implemented; differences in the spatial distribution of bending and twisting were detected via the quantification of angles along the petiole. Based on the measured values, we discuss that minute degrees of differential growth can result in pronounced twisting in petioles.

scholarly journals Three-Dimensional Cross-Sectional Light-Sheet Microscopy Imaging of the Inflamed Mouse Gut

2017 ◽  
Vol 153 (4) ◽  
pp. 898-900 ◽  
Author(s):  
Sebastian Zundler ◽  
Anika Klingberg ◽  
Daniela Schillinger ◽  
Sarah Fischer ◽  
Clemens Neufert ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Light Sheet ◽  
Cross Sectional ◽  
Microscopy Imaging ◽  
Light Sheet Microscopy

scholarly journals Three Dimensional Fluorescence Imaging Using Multiple Light-Sheet Microscopy

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e96551 ◽  
Author(s):  
Kavya Mohan ◽  
Subhajit B. Purnapatra ◽  
Partha Pratim Mondal
Keyword(s):  
Fluorescence Imaging ◽  
Three Dimensional ◽  
Light Sheet ◽  
Light Sheet Microscopy

scholarly journals Actin-based protrusions of migrating neutrophils are intrinsically lamellar and facilitate direction changes

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Lillian K Fritz-Laylin ◽  
Megan Riel-Mehan ◽  
Bi-Chang Chen ◽  
Samuel J Lord ◽  
Thomas D Goddard ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Light Sheet ◽  
Time Lapse ◽  
Three Dimensions ◽  
Cortical Actin ◽  
Linear Arrangement ◽  
Collagen Matrices ◽  
Flat Surfaces ◽  
Actin Networks ◽  
Light Sheet Microscopy

Leukocytes and other amoeboid cells change shape as they move, forming highly dynamic, actin-filled pseudopods. Although we understand much about the architecture and dynamics of thin lamellipodia made by slow-moving cells on flat surfaces, conventional light microscopy lacks the spatial and temporal resolution required to track complex pseudopods of cells moving in three dimensions. We therefore employed lattice light sheet microscopy to perform three-dimensional, time-lapse imaging of neutrophil-like HL-60 cells crawling through collagen matrices. To analyze three-dimensional pseudopods we: (i) developed fluorescent probe combinations that distinguish cortical actin from dynamic, pseudopod-forming actin networks, and (ii) adapted molecular visualization tools from structural biology to render and analyze complex cell surfaces. Surprisingly, three-dimensional pseudopods turn out to be composed of thin (<0.75 µm), flat sheets that sometimes interleave to form rosettes. Their laminar nature is not templated by an external surface, but likely reflects a linear arrangement of regulatory molecules. Although we find that Arp2/3-dependent pseudopods are dispensable for three-dimensional locomotion, their elimination dramatically decreases the frequency of cell turning, and pseudopod dynamics increase when cells change direction, highlighting the important role pseudopods play in pathfinding.

Deep-learning on-chip light-sheet microscopy enabling video-rate volumetric imaging of dynamic biological specimens

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Xiaopeng Chen ◽  
Junyu Ping ◽  
Yixuan Sun ◽  
Chengqiang Yi ◽  
Sijian Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Scattering ◽  
Light Sheet ◽  
High Contrast ◽  
Spatiotemporal Resolution ◽  
Volumetric Imaging ◽  
Light Sheet Microscopy ◽  
High Spatiotemporal Resolution ◽  
On Chip

Volumetric imaging of dynamic signals in a large, moving, and light-scattering specimen is extremely challenging, owing to the requirement on high spatiotemporal resolution and difficulty in obtaining high-contrast signals. Here...

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