Three-dimensional nanoscale analysis of light-dependent organelle changes inArabidopsismesophyll cells

Abstract

Different organelles function coordinately in numerous intracellular processes. Photorespiration incidental to photosynthetic carbon fixation is organized across three subcellular compartments: chloroplasts, peroxisomes, and mitochondria. Under light conditions, these three organelles often form a ternary organellar complex in close proximity, suggesting a connection with metabolism during photorespiration. However, due to the heterogeneity of intercellular organelle localization and morphology, organelles' responses to changes in the external environment remain poorly understood. Here we used array tomography by field emission scanning electron microscopy to image organelles inside the whole plant cell at nanometer resolution, generating a three-dimensional (3D) spatial map of the light-dependent positioning of chloroplasts, peroxisomes, nuclei, and vacuoles. Our results show, in light-treated cells, the volume of peroxisomes increased, and mitochondria were simplified. In addition, the population of free organelles decreased, and the ternary complex centered on chloroplasts increased. Moreover, our results emphasized the expansion of the proximity area rather than the increase in the number of proximity sites inter-organelles. All of these phenomena were quantified for the first time on the basis of nanoscale spatial maps. In summary, we provide the first 3D reconstruction of Arabidopsis mesophyll cells, together with nanoscale quantified organelle morphology and their positioning via proximity areas, and then evidence of their light-dependent changes.

Peroxisomes undergo morphological changes in a light‐dependent manner with proximity to the nucleus

“…Based on previously reconstructed 3D images generated by array tomography [17], we used imaris software to measure the shortest pairwise distances between five organelles: chloroplasts, peroxisomes, mitochondria, nuclei, and vacuoles. We performed a correlation analysis between each shortest distance and the morphology (sphericity and volume) of chloroplasts, peroxisomes, and mitochondria (Fig.…”

“…The 3D data reconstructions used in this study were generated in a previous study; details about the experimental materials used, sample preparation for electron microscopy, and 3D image reconstruction methods are described in Midorikawa et al . [17]. Briefly, the experimental materials used were true leaves of 2‐week‐old Arabidopsis seedlings from the Col‐0 accession.…”

“…The volume, sphericity, and proximity area of chloroplasts, peroxisomes, and mitochondria, as well as the shortest distance between each pair of organelles, were calculated by imaris (ver 9.8.0; Bitplane, Zurich, Switzerland) software. The proximity area was calculated as the area in close proximity to the membrane (10 × 10 × 100 nm) within a single voxel, with the XY pixel being 10 × 10 nm [17]. The ‘ShortestDistanceToSurfaces’ function in imaris measures the distance between the surfaces of each organelle object and calculates the shortest distance.…”

“…We previously performed a quantitative analysis of the morphology and inter-organelle contacts of chloroplasts, peroxisomes, and mitochondria involved in photorespiratory metabolism in Arabidopsis (Arabidopsis thaliana) by array tomography using a field emission scanning electron microscope (FE-SEM) [17]. Organelles involved in photorespiration increased Abbreviations 3D, three-dimensional; ATP, adenosine 5 0 -triphosphate; Chl, chloroplast; FE-SEM, field emission scanning electron microscope; MCSs, membrane contact sites; Nuc, nucleus; Per, peroxisome.…”

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration

“…This key information will help us to better understand the capacities of plant metabolic networks as a whole in relation to plant development. It is especially important to consider the bigger picture given our current capacity to massively produce data using sophisticated modern tools such as plant phenotyping platforms, imaging techniques (Midorikawa et al, 2022 ; Oi et al, 2017 ; Pipitone et al, 2021 ; Weiner et al, 2022 ), segmentation tools (Harline & Roeder, 2023 ; Wolny et al, 2020 ), proteomics (Mergner et al, 2020 ), laser microdissection (Balasubramanian et al, 2021 ) and single-cell analyses (de Souza et al, 2020 ; Seyfferth et al, 2021 ).…”