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Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

ABSTRACT: SignificanceDeep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function.AimWe aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy.ApproachAssisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth.ResultsThe WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of 800 μm in mice.ConclusionsWe built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.

SUBMITTER: Meng J

PROVIDER: S-EPMC9587757 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

Meng Jia J Zhou Lingxi L Qian Shuhao S Wang Chuncheng C Feng Zhe Z Jiang Shenyi S Jiang Rushan R Ding Zhihua Z Qian Jun J Zhuo Shuangmu S Liu Zhiyi Z

Journal of biomedical optics 20221001 10

<h4>Significance</h4>Deep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function.<h4>Aim</h4>We aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy.<h4>Approach</h4>Assisted by AIEgens with near-infrared-II excitation, three-photon fl ...[more]

PMID: 36273250

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Project description:Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. "mitochondrial dynamics") are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D imaging and quantification are crucial for proper understanding of mitochondrial shape and topology in non-flat cells. In summary, we here present an integrative method for unbiased 3D quantification of mitochondrial shape and network properties in mammalian cells.

Dataset Information

Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

Publications

Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

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