Back to Search Start Over

Organelle landscape analysis using a multiparametric particle-based method.

Authors :
Kurikawa, Yoshitaka
Koyama-Honda, Ikuko
Tamura, Norito
Koike, Seiichi
Mizushima, Noboru
Source :
PLoS Biology. 9/17/2024, Vol. 22 Issue 9, p1-22. 22p.
Publication Year :
2024

Abstract

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. Organelles have unique structures and molecular composition, but can form heterogeneous populations and change substantially during maturation and differentiation, and traditional methods have failed to reflect these variations. In this study, the authors introduce a multi-parametric, fluorescence spectroscopy method that allows simultaneous visualization of a wide array of organelles in different maturation states. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15449173
Volume :
22
Issue :
9
Database :
Academic Search Index
Journal :
PLoS Biology
Publication Type :
Academic Journal
Accession number :
179689281
Full Text :
https://doi.org/10.1371/journal.pbio.3002777