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本文引用的文献

1
Recent progress and current opinions in Brillouin microscopy for life science applications.用于生命科学应用的布里渊显微镜的最新进展与当前观点。
Biophys Rev. 2020 Jun;12(3):615-624. doi: 10.1007/s12551-020-00701-9. Epub 2020 May 26.
2
SciPy 1.0: fundamental algorithms for scientific computing in Python.SciPy 1.0:Python 中的科学计算基础算法。
Nat Methods. 2020 Mar;17(3):261-272. doi: 10.1038/s41592-019-0686-2. Epub 2020 Feb 3.
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All-digital histopathology by infrared-optical hybrid microscopy.全数字组织病理学通过红外-光学混合显微镜实现。
Proc Natl Acad Sci U S A. 2020 Feb 18;117(7):3388-3396. doi: 10.1073/pnas.1912400117. Epub 2020 Feb 3.
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Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.利用受激拉曼组织学和深度神经网络进行近实时术中脑瘤诊断。
Nat Med. 2020 Jan;26(1):52-58. doi: 10.1038/s41591-019-0715-9. Epub 2020 Jan 6.
5
Brillouin Microscopy Visualizes Centralized Corneal Edema in Fuchs Endothelial Dystrophy.布里渊显微镜可视化 Fuchs 内皮营养不良的中央性角膜水肿。
Cornea. 2020 Feb;39(2):168-171. doi: 10.1097/ICO.0000000000002191.
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Brillouin microscopy: an emerging tool for mechanobiology.布里渊显微镜:力学生物学的新兴工具。
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Detection of proteoglycan loss from articular cartilage using Brillouin microscopy, with applications to osteoarthritis.利用布里渊显微镜检测关节软骨中蛋白聚糖的流失及其在骨关节炎中的应用。
Biomed Opt Express. 2019 Apr 17;10(5):2457-2466. doi: 10.1364/BOE.10.002457. eCollection 2019 May 1.
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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.基于深度学习的无标记组织自发荧光图像的虚拟组织学染色。
Nat Biomed Eng. 2019 Jun;3(6):466-477. doi: 10.1038/s41551-019-0362-y. Epub 2019 Mar 4.
9
Tissue biomechanics during cranial neural tube closure measured by Brillouin microscopy and optical coherence tomography.应用布里渊显微镜和光相干断层扫描技术测量颅神经管闭合过程中的组织生物力学。
Birth Defects Res. 2019 Aug 15;111(14):991-998. doi: 10.1002/bdr2.1389. Epub 2018 Sep 21.
10
Reply to 'Water content, not stiffness, dominates Brillouin spectroscopy measurements in hydrated materials'.对《水含量而非硬度主导水合材料中的布里渊光谱测量》的回复。
Nat Methods. 2018 Aug;15(8):562-563. doi: 10.1038/s41592-018-0075-2.

使用布里渊光散射对比度对组织进行无标记组织学成像。

Label-free histological imaging of tissues using Brillouin light scattering contrast.

作者信息

Ryu Seungmi, Martino Nicola, Kwok Sheldon J J, Bernstein Liane, Yun Seok-Hyun

机构信息

Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, 65 Landsdowne St., Cambridge, MA 02139, USA.

National Center for Advancing Translational Sciences, National Institute of Health, Rockville, MD 20850, USA.

出版信息

Biomed Opt Express. 2021 Feb 17;12(3):1437-1448. doi: 10.1364/BOE.414474. eCollection 2021 Mar 1.

DOI:10.1364/BOE.414474
PMID:33796364
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7984781/
Abstract

Brillouin light scattering offers a unique label-free approach to measure biomechanical properties non-invasively. While this technique is used in biomechanical analysis of cells and tissues, its potential for visualizing structural features of tissues based on the biomechanical contrast has not been much exploited. Here, we present high-resolution Brillouin microscopy images of four basic tissue types: muscular, connective, epithelial, and nervous tissues. The Brillouin contrast distinguishes between muscle fiber cells and endomysium in skeletal muscle and reveals chondrocytes along with spatially varying stiffness of the extracellular matrix in articular cartilage. The hydration-sensitive contrast can visualize the stratum corneum, epidermis, and dermis in the skin epithelium. In brain tissues, the Brillouin images show the mechanical heterogeneity across the cortex and deeper regions. This work demonstrates the versatility of using the Brillouin shift as histological contrast for examining intact tissue substructures via longitudinal modulus without the need for laborious tissue processing steps.

摘要

布里渊光散射提供了一种独特的无标记方法,可用于非侵入性地测量生物力学特性。虽然该技术已用于细胞和组织的生物力学分析,但其基于生物力学对比度可视化组织结构特征的潜力尚未得到充分利用。在此,我们展示了四种基本组织类型(肌肉组织、结缔组织、上皮组织和神经组织)的高分辨率布里渊显微镜图像。布里渊对比度区分了骨骼肌中的肌纤维细胞和肌内膜,并揭示了关节软骨中的软骨细胞以及细胞外基质在空间上变化的硬度。对水合作用敏感的对比度可以可视化皮肤上皮中的角质层、表皮和真皮。在脑组织中,布里渊图像显示了整个皮质和更深区域的力学异质性。这项工作证明了使用布里渊频移作为组织学对比度,通过纵向模量检查完整组织亚结构的多功能性,而无需繁琐的组织处理步骤。