DRGquant: A new modular AI-based pipeline for 3D analysis of the DRG.
DRGquant: A new modular AI-based pipeline for 3D analysis of the DRG.
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DOI：
10.1016/j.jneumeth.2022.109497
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发表时间：
2022-04-01
影响因子：
3
通讯作者：
Yaksh, Tony L.
中科院分区：
文献类型：
--
作者：
Hunt, Matthew A.;Lund, Harald;Delay, Lauriane;Dos Santos, Gilson Goncalves;Pham, Albert;Kurtovic, Zerina;Telang, Aditya;Lee, Adam;Parvathaneni, Akhil;Kussick, Emily;Corr, Maripat;Yaksh, Tony L.
关键词：
背根神经节（DRG）结构复杂，是处理痛觉系统的关键。整个mount分析允许在三维（3D）空间中检查DRG复杂的微建筑和细胞关系。我们为DRGquant提供了一套优化的工具和技术，用于DRG内细胞/结构的自动图像分析和重建。我们开发了一个开源软件管道，利用机器学习来识别DRG中的子结构，并可靠地对它们进行分类和量化。我们的方法足够灵敏，可以分离、分析和分类包括巨噬细胞在内的单个DRG亚结构。在鞘内注射脂多糖和化疗诱导的周围神经病变模型中，巨噬细胞的激活在DRG中被可视化和量化。浸润巨噬细胞的百分比体积在定量上与商业来源相似。循环荧光右旋糖酐在DRG巨噬细胞内可见，这使得DRG的3D重建和DRGquant在单个巨噬细胞内展示了亚细胞的空间分辨率。在这里，我们描述了一种可靠和有效的方法管道来制备清除和整个mount DRG组织。DRGquant允许自动图像分析，而无需繁琐的手动门控来减少偏差。DRG巨噬细胞的定量优于商业溶液。DRGquant使用机器学习从噪声中分离信号并识别单个细胞，使我们能够在DRG中隔离单个结构或感兴趣的区域，以便进行更细粒度和微调的分析。使用这些3D技术，我们能够更好地了解实验性炎症条件下DRG的生物学特性。
The dorsal root ganglion (DRG) is structurally complex and pivotal to systems processing nociception. Whole mount analysis allows examination of intricate microarchitectural and cellular relationships of the DRG in three-dimensional (3D) space. We present DRGquant a set of tools and techniques optimized as a pipeline for automated image analysis and reconstruction of cells/structures within the DRG. We have developed an open source software pipeline that utilizes machine learning to identify substructures within the DRG and reliably classify and quantify them. Our methods were sufficiently sensitive to isolate, analyze, and classify individual DRG substructures including macrophages. The activation of macrophages was visualized and quantified in the DRG following intrathecal injection of lipopolysaccharide, and in a model of chemotherapy induced peripheral neuropathy. The percent volume of infiltrating macrophages was similar to a commercial source in quantification. Circulating fluorescent dextran was visualized within DRG macrophages using whole mount preparations, which enabled 3D reconstruction of the DRG and DRGquant demonstrated subcellular spatial resolution within individual macrophages. Here we describe a reliable and efficient methodologic pipeline to prepare cleared and whole mount DRG tissue. DRGquant allows automated image analysis without tedious manual gating to reduce bias. The quantitation of DRG macrophages was superior to commercial solutions. Using machine learning to separate signal from noise and identify individual cells, DRGquant enabled us to isolate individual structures or areas of interest within the DRG for a more granular and fine-tuned analysis. Using these 3D techniques, we are better able to appreciate the biology of the DRG under experimental inflammatory conditions.
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