COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains.
COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains.
复制标题
DOI：
10.1016/j.crmeth.2023.100454
复制
发表时间：
2023-04-24
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
组织透明化使整个器官透明，加速全组织成像；例如，使用光片荧光显微镜。然而，分析由 TB 级图像和数百万个标记细胞信息组成的大型 3D 数据集仍然存在挑战。之前的工作已经建立了对组织清除的小鼠大脑进行自动分析的管道，但重点是单色通道和/或在相对低分辨率图像中检测核局部信号。在这里，我们提出了一个自动化工作流程（COMBINe，小鼠大脑中的细胞检测），使用双标记马赛克分析（MADM）来绘制遗传上不同的小鼠前脑中稀疏标记的神经元和星形胶质细胞。 COMBINe 将来自多个管道的模块以 RetinaNet 为核心进行混合。我们定量分析了基于 MADM 的表皮生长因子受体 (EGFR) 缺失对小鼠前脑神经元和星形胶质细胞群的区域和亚区域影响。 COMBINe 基于形态和颜色执行自动细胞检测 COMBINe 准确检测组织清除的大脑中失焦的细胞 COMBINe 克服颜色通道之间的轻微错位 分层分析揭示稀疏 Egfr 缺失对星形胶质细胞的区域影响 组织清除已广泛用于探索完整器官中的细胞组织。通过以细胞分辨率对整个小鼠大脑进行成像，生成的数据集包含需要处理的 TB 级图像以及需要根据形态和颜色进行计数和分类的数百万个标记细胞。因此，数据处理和分析的自动化是迫切需要的。我们提出了一个工作流程（COMBINe），可以自动定位和分类此类 3D 数据集中的标记细胞，并在注册到参考的 Allen Brain Atlas 后定量分析区域效应。我们应用该方法来研究稀疏表皮生长因子受体缺失对胶质生成的影响。蔡等人。提出了一个名为 COMBINe 的工作流程，可以自动检测和量化组织清除的小鼠大脑中的所有标记细胞，并将其应用于研究稀疏表皮生长因子受体缺失的影响。 COMBINe 适用于需要全脑量化和分析的各种神经科学项目。
Tissue clearing renders entire organs transparent to accelerate whole-tissue imaging; for example, with light-sheet fluorescence microscopy. Yet, challenges remain in analyzing the large resulting 3D datasets that consist of terabytes of images and information on millions of labeled cells. Previous work has established pipelines for automated analysis of tissue-cleared mouse brains, but the focus there was on single-color channels and/or detection of nuclear localized signals in relatively low-resolution images. Here, we present an automated workflow (COMBINe, Cell detectiOn in Mouse BraIN) to map sparsely labeled neurons and astrocytes in genetically distinct mouse forebrains using mosaic analysis with double markers (MADM). COMBINe blends modules from multiple pipelines with RetinaNet at its core. We quantitatively analyzed the regional and subregional effects of MADM-based deletion of the epidermal growth factor receptor (EGFR) on neuronal and astrocyte populations in the mouse forebrain. COMBINe performs automated cell detection based on morphology and color COMBINe accurately detects out-of-focus cells in tissue-cleared brains COMBINe overcomes slight misalignments between color channels Hierarchical analysis reveals regional effect of sparse Egfr deletion on astrocytes Tissue clearing has been extensively used to explore cellular organizations in intact organs. By imaging entire mouse brains with cellular resolution, the resulting datasets contain terabytes of images to process and millions of labeled cells that need to be counted and classified based on morphology and color. Therefore, automation of data processing and analysis is an urgent need. We present a workflow (COMBINe) to automatically locate and classify labeled cells in such 3D datasets and to quantitatively analyze the regional effects after registration to the referenced Allen Brain Atlas. We applied the approach to study the effects of sparse epidermal growth factor receptor deletion on gliogenesis. Cai et al. present a workflow called COMBINe to automatically detect and quantify all labeled cells in tissue-cleared mouse brains and apply it to study the effects of sparse epidermal growth factor receptor deletion. COMBINe is adaptable for various neuroscience projects that require whole-brain quantification and analysis.
影响因子：
3.7
作者：
Cai Y;Zhang X;Kovalsky SZ;Ghashghaei HT;Greenbaum A
通讯作者：
Greenbaum A