Optimization of protoplast regeneration in the model plant Arabidopsis thaliana

Background Plants have a remarkable reprogramming potential, which facilitates plant regeneration, especially from a single cell. Protoplasts have the ability to form a cell wall and undergo cell division, allowing whole plant regeneration. With the growing need for protoplast regeneration in genetic engineering and genome editing, fundamental studies that enhance our understanding of cell cycle re-entry, pluripotency acquisition, and de novo tissue regeneration are essential. To conduct these studies, a reproducible and efficient protoplast regeneration method using model plants is necessary. Results Here, we optimized cell and tissue culture methods for improving protoplast regeneration efficiency in Arabidopsis thaliana. Protoplasts were isolated from whole seedlings of four different Arabidopsis ecotypes including Columbia (Col-0), Wassilewskija (Ws-2), Nossen (No-0), and HR (HR-10). Among these ecotypes, Ws-2 showed the highest potential for protoplast regeneration. A modified thin alginate layer was applied to the protoplast culture at an optimal density of 1 × 106 protoplasts/mL. Following callus formation and de novo shoot regeneration, the regenerated inflorescence stems were used for de novo root organogenesis. The entire protoplast regeneration process was completed within 15 weeks. The in vitro regenerated plants were fertile and produced morphologically normal progenies. Conclusion The cell and tissue culture system optimized in this study for protoplast regeneration is efficient and reproducible. This method of Arabidopsis protoplast regeneration can be used for fundamental studies on pluripotency establishment and de novo tissue regeneration.This work was supported by the Samsung Science and Technology Foundation under Project Number SSTF-BA2001-10

Optimization of protoplast regeneration in the model plant Arabidopsis thaliana

Background Plants have a remarkable reprogramming potential, which facilitates plant regeneration, especially from a single cell. Protoplasts have the ability to form a cell wall and undergo cell division, allowing whole plant regeneration. With the growing need for protoplast regeneration in genetic engineering and genome editing, fundamental studies that enhance our understanding of cell cycle re-entry, pluripotency acquisition, and de novo tissue regeneration are essential. To conduct these studies, a reproducible and efficient protoplast regeneration method using model plants is necessary. Results Here, we optimized cell and tissue culture methods for improving protoplast regeneration efficiency in Arabidopsis thaliana. Protoplasts were isolated from whole seedlings of four different Arabidopsis ecotypes including Columbia (Col-0), Wassilewskija (Ws-2), Nossen (No-0), and HR (HR-10). Among these ecotypes, Ws-2 showed the highest potential for protoplast regeneration. A modified thin alginate layer was applied to the protoplast culture at an optimal density of 1 x 10(6) protoplasts/mL. Following callus formation and de novo shoot regeneration, the regenerated inflorescence stems were used for de novo root organogenesis. The entire protoplast regeneration process was completed within 15 weeks. The in vitro regenerated plants were fertile and produced morphologically normal progenies. Conclusion The cell and tissue culture system optimized in this study for protoplast regeneration is efficient and reproducible. This method of Arabidopsis protoplast regeneration can be used for fundamental studies on pluripotency establishment and de novo tissue regeneration.Y

Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks

Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models

Arabidopsis HISTONE DEACETYLASE 9 Stimulates Hypocotyl Cell Elongation by Repressing GIGANTEA Expression Under Short Day Photoperiod

Copyright © 2022 Lee, Jeong, Lee and Seo.Developmental plasticity contributes to plant adaptation and fitness in a given condition. Hypocotyl elongation is under the tight control of complex genetic networks encompassing light, circadian, and photoperiod signaling. In this study, we demonstrate that HISTONE DEACETYLASE 9 (HDA9) mediates day length-dependent hypocotyl cell elongation. HDA9 binds to the GIGANTEA (GI) locus involved in photoperiodic hypocotyl elongation. The short day (SD)-accumulated HDA9 protein promotes histone H3 deacetylation at the GI locus during the dark period, promoting hypocotyl elongation. Consistently, HDA9-deficient mutants display reduced hypocotyl length, along with an increase in GI gene expression, only under SD conditions. Taken together, our study reveals the genetic basis of day length-dependent cell elongation in plants.N

Configurable topological textures in strain graded ferroelectric nanoplates

Exploring topological textures in ferroelectrics facilitates the understanding and application of topological features in matter. Here the authors demonstrate the strain field induced evolution of topological vortices in nanoplatelets of rhombohedral phase BiFeO3 using the angle-resolved piezoresponse force microscopy