PDF(Pubmed)
Abstract：
The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.
摘要：
胰岛中α和β细胞的功能障碍可导致糖尿病。在疾病进展过程中，胰岛细胞的亚细胞组织仍然存在许多问题。现有的三维细胞映射方法面临诸如时间密集的样品切片和主观细胞识别的挑战。为了应对这些挑战，我们开发了一种基于亚细胞特征的分类方法，这使我们能够使用软X射线断层扫描（SXT）识别α和β细胞并量化其亚细胞结构特征。我们观察到两种细胞类型之间的全细胞形态和细胞器统计存在显着差异。此外,我们通过分析囊泡大小和分子密度分布来表征单个胰岛素和胰高血糖素囊泡之间的细微生物物理差异，这在以前使用其他方法是不可能的。这些亚囊泡参数使我们能够使用监督机器学习系统地预测细胞类型。我们还使用均匀流形近似和投影（UMAP）嵌入可视化不同的囊泡和细胞亚型，这为我们提供了一种探索胰岛细胞结构异质性的创新方法。该方法提出了一种用于跟踪细胞中生物学上有意义的异质性的创新方法，可应用于任何细胞系统。
