Department of Biochemistry and Molecular Biology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
PLoS Biol. 2024 Sep 17;22(9):e3002777. doi: 10.1371/journal.pbio.3002777. eCollection 2024 Sep.
Organelles have unique structures and molecular compositions for their functions and have been classified accordingly. However, many organelles are heterogeneous and in the process of maturation and differentiation. Because traditional methods have a limited number of parameters and spatial resolution, they struggle to capture the heterogeneous landscapes of organelles. Here, we present a method for multiparametric particle-based analysis of organelles. After disrupting cells, fluorescence microscopy images of organelle particles labeled with 6 to 8 different organelle markers were obtained, and their multidimensional data were represented in two-dimensional uniform manifold approximation and projection (UMAP) spaces. This method enabled visualization of landscapes of 7 major organelles as well as the transitional states of endocytic organelles directed to the recycling and degradation pathways. Furthermore, endoplasmic reticulum-mitochondria contact sites were detected in these maps. Our proposed method successfully detects a wide array of organelles simultaneously, enabling the analysis of heterogeneous organelle landscapes.
细胞器具有独特的结构和分子组成，以行使其功能，并因此进行了分类。然而，许多细胞器是异质的，并处于成熟和分化的过程中。由于传统方法的参数和空间分辨率有限，它们难以捕捉细胞器的异质景观。在这里，我们提出了一种用于细胞器的基于多参数粒子的分析方法。在破坏细胞后，获得了用 6 到 8 种不同细胞器标记物标记的细胞器颗粒的荧光显微镜图像，并将它们的多维数据表示在二维均匀流形逼近和投影 (UMAP) 空间中。该方法能够可视化 7 种主要细胞器的景观以及定向到回收和降解途径的内吞细胞器的过渡状态。此外，在这些图谱中检测到了内质网-线粒体接触位点。我们提出的方法能够成功地同时检测广泛的细胞器，从而分析异质的细胞器景观。
