Chen Jianhua, Mirvis Mary, Ekman Axel, Vanslembrouck Bieke, Gros Mark Le, Larabell Carolyn, Marshall Wallace F
National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
X-ray Imaging Group, Experimental Facility Division, National Synchrotron Radiation Research Center, Hsinchu, Taiwan.
Mol Biol Cell. 2025 Aug 28:mbcE24100486. doi: 10.1091/mbc.E24-10-0486.
Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at sub-optical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based auto-segmentation pipeline to segment and label cellular structures in hundreds of cells across three strains. This task-based pipeline employs manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the 3D whole-cell morphometric characteristics of wild-type, VPH1-GFP, and strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].
软X射线断层扫描（SXT）是一种用于在亚光学各向同性分辨率下定量分析细胞结构的宝贵工具。然而，它传统上依赖于手动分割，限制了其对大型数据集的可扩展性。在这里，我们利用基于深度学习的自动分割管道对三个菌株的数百个细胞中的细胞结构进行分割和标记。这个基于任务的管道采用手动迭代细化来提高关键结构（包括细胞体、细胞核、液泡和脂滴）的分割精度，从而实现高通量和精确的表型分析。使用这种方法，我们定量比较了野生型、VPH1-GFP和其他菌株的三维全细胞形态特征，揭示了特定菌株的详细细胞和细胞器大小及形状变化。我们展示了SXT数据在对整个细胞器和细胞进行精确的三维曲率分析以及使用表面网格检测精细形态特征方面的效用。我们的方法有助于进行具有高空间精度和统计通量的比较分析，揭示单细胞和群体水平上的细微形态特征。这种工作流程显著增强了我们表征细胞解剖结构的能力，并支持在中尺度上进行可扩展的研究，在跨多种生物学背景研究细胞结构、细胞器生物学和基因研究中具有应用价值。
