利用基于粒子的多参数方法分析细胞器景观

IF 7.8 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Yoshitaka Kurikawa, Ikuko Koyama-Honda, Norito Tamura, Seiichi Koike, Noboru Mizushima
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引用次数: 0

摘要

细胞器具有与其功能相适应的独特结构和分子组成,并据此进行分类。然而,许多细胞器是异质的,处于成熟和分化过程中。由于传统方法的参数数量和空间分辨率有限,它们很难捕捉到细胞器的异质性景观。在这里,我们提出了一种基于粒子的细胞器多参数分析方法。在破坏细胞后,我们获得了用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图像,并用二维均匀流形近似和投影(UMAP)空间表示其多维数据。通过这种方法,7 种主要细胞器的景观以及内细胞器向循环和降解途径的过渡状态得以可视化。此外,在这些图谱中还检测到了内质网与线粒体的接触点。我们提出的方法成功地同时检测了多种细胞器,从而实现了对异质性细胞器景观的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Organelle landscape analysis using a multiparametric particle-based method
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.
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来源期刊
PLoS Biology
PLoS Biology 生物-生化与分子生物学
CiteScore
14.40
自引率
2.00%
发文量
359
审稿时长
3 months
期刊介绍: PLOS Biology is an open-access, peer-reviewed general biology journal published by PLOS, a nonprofit organization of scientists and physicians dedicated to making the world's scientific and medical literature freely accessible. The journal publishes new articles online weekly, with issues compiled and published monthly. ISSN Numbers: eISSN: 1545-7885 ISSN: 1544-9173
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