Multiscale microscopy to decipher plant cell structure and dynamics

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Probing the dynamic crosstalk of lysosomes and mitochondria with structured illumination microscopy

Structured illumination microscopy (SIM) is a super-resolution technology for imaging living cells and has been used for studying the dynamics of lysosomes and mitochondria. Recently, new probes and analyzing methods have been developed for SIM imaging, enabling the quantitative analysis of these subcellular structures and their interactions. This review provides an overview of the working principle and advances of SIM, as well as the organelle-targeting principles and types of fluorescence probes, including small molecules, metal complexes, nanoparticles, and fluorescent proteins. Additionally, quantitative methods based on organelle morphology and distribution are outlined. Finally, the review provides an outlook on the current challenges and future directions for improving the combination of SIM imaging and image analysis to further advance the study of organelles. We hope that this review will be useful for researchers working in the field of organelle research and help to facilitate the development of SIM imaging and analysis techniques.