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Liu J, Zheng Y, Lin L, Guo J, Lv Y, Yuan J, Zhai H, Chen X, Shen L, Li L, Bai S, Han H. A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images. JOURNAL OF PLANT PHYSIOLOGY 2024; 297:154236. [PMID: 38621330 DOI: 10.1016/j.jplph.2024.154236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/17/2024]
Abstract
Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.
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Affiliation(s)
- Jiazheng Liu
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Yafeng Zheng
- College of Life Sciences, Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, Beijing 100871, China
| | - Limei Lin
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Jingyue Guo
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Yanan Lv
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Jingbin Yuan
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Hao Zhai
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Xi Chen
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Lijun Shen
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - LinLin Li
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China.
| | - Shunong Bai
- College of Life Sciences, Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, Beijing 100871, China.
| | - Hua Han
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China.
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Lewis A. A hypothesis of teleological evolution, via endogenous acetylcholine, nitric oxide, and calmodulin pathways. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 188:68-76. [PMID: 38552848 DOI: 10.1016/j.pbiomolbio.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/30/2024] [Accepted: 03/22/2024] [Indexed: 04/08/2024]
Abstract
The Extended Evolutionary Synthesis (EES) addresses the issues in evolutionary biology which cannot be explained by neo-Darwinian theory. The EES paradigm recognises teleology and agency in living systems, and identifies that organisms can directly affect their evolutionary trajectory in a goal-directed manner, yet the physiological pathways via which this occurs remain unidentified. Here, I propose a physiological pathway via which organisms can alter their genotype and phenotype by making behavioural decisions with respect their activity levels, partitioning of resources either toward growth, defence against disease, or their behavioural response to stressors. Specifically, I hypothesize that agential, teleological decisions mediated by acetylcholine result in induced nitric oxide (NO) activity, which regulates metabolism, blood flow, and immune response. Nitric oxide, however, is also a key epigenetic molecule, being involved in DNA acetylation, methylation, and de-methylation. Further, NO alters the histone complexes which scaffold nuclear DNA strands, and is thus a good candidate in identifying a system which allows an organisms to make teleological genetic changes. The proposed mechanisms of inheritance of these genetic changes is via the paternal line, whereby epigenetic changes in the somatic Sertoli cells in animals are transcribed by mRNA and included in the germline cells - the male gametes. The microsporangium in plants, and the sporophore cells in fungi, meanwhile, are proposed to form similar systems in response to sensory detection of stressors. Whilst the hypothesis is presented as a simplified model for future testing, it opens new avenues for study in evolutionary biology.
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Vainshelbaum NM, Giuliani A, Salmina K, Pjanova D, Erenpreisa J. The Transcriptome and Proteome Networks of Malignant Tumours Reveal Atavistic Attractors of Polyploidy-Related Asexual Reproduction. Int J Mol Sci 2022; 23:ijms232314930. [PMID: 36499258 PMCID: PMC9736112 DOI: 10.3390/ijms232314930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/18/2022] [Accepted: 11/26/2022] [Indexed: 12/02/2022] Open
Abstract
The expression of gametogenesis-related (GG) genes and proteins, as well as whole genome duplications (WGD), are the hallmarks of cancer related to poor prognosis. Currently, it is not clear if these hallmarks are random processes associated only with genome instability or are programmatically linked. Our goal was to elucidate this via a thorough bioinformatics analysis of 1474 GG genes in the context of WGD. We examined their association in protein-protein interaction and coexpression networks, and their phylostratigraphic profiles from publicly available patient tumour data. The results show that GG genes are upregulated in most WGD-enriched somatic cancers at the transcriptome level and reveal robust GG gene expression at the protein level, as well as the ability to associate into correlation networks and enrich the reproductive modules. GG gene phylostratigraphy displayed in WGD+ cancers an attractor of early eukaryotic origin for DNA recombination and meiosis, and one relative to oocyte maturation and embryogenesis from early multicellular organisms. The upregulation of cancer-testis genes emerging with mammalian placentation was also associated with WGD. In general, the results suggest the role of polyploidy for soma-germ transition accessing latent cancer attractors in the human genome network, which appear as pre-formed along the whole Evolution of Life.
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Affiliation(s)
- Ninel M. Vainshelbaum
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
- Faculty of Biology, The University of Latvia, LV-1586 Riga, Latvia
- Correspondence: (N.M.V.); (J.E.)
| | - Alessandro Giuliani
- Environmen and Health Department, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Kristine Salmina
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
| | - Dace Pjanova
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
| | - Jekaterina Erenpreisa
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
- Correspondence: (N.M.V.); (J.E.)
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Abstract
Germ cells (GCs) are the key carriers delivering genetic information from one generation to the next. In a majority of animals, GCs segregate from somatic cells during embryogenesis by forming germlines. In land plants, GCs segregate from somatic cells during postembryonic development. In a majority of angiosperms, male GCs (archesporial cells) initiate at the four corners of the anther primordia. Little is known about the mechanism underlying this initiation. Here, we discovered that the dynamic auxin distribution in developing anthers coincided with GC initiation. A centripetal auxin gradient gradually formed toward the four corners where GCs will initiate. Local auxin biosynthesis was necessary for this patterning and for GC specification. The GC determinant protein SPOROCYTELESS/NOZZLE (SPL/NZZ) mediated the effect of auxin on GC specification and modified auxin biosynthesis to maintain a centripetal auxin distribution. Our work reveals that auxin is a key factor guiding GC specification in Arabidopsis anthers. Moreover, we demonstrate that the GC segregation from somatic cells is not a simple switch on/off event but rather a complicated process that involves a dynamic feedback circuit among local auxin biosynthesis, transcription of SPL/NZZ, and a progressive GC specification. This finding sheds light on the mystery of how zygote-derived somatic cells diverge into GCs in plants.
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Abstract
Zhi-Hong Xu is a plant physiologist who studied botany at Peking University (1959–1965). He joined the Shanghai Institute of Plant Physiology (SIPP), Chinese Academy of Sciences (CAS), as a graduate student in 1965. He recalls what has happened for the institute, during the Cultural Revolution, and he witnessed the spring of science eventually coming to China. Xu was a visiting scholar at the John Innes Institute and in the Department of Botany at Nottingham University in the United Kingdom (1979–1981). He became deputy director of SIPP in 1983 and director in 1991; he also chaired the State Key Laboratory of Plant Molecular Genetics SIPP (1988–1996). He worked as a visiting scientist in the Institute of Molecular and Cell Biology, National University of Singapore, for three months each year (1989–1992). He served as vice president of CAS (1992–2002) and as president of Peking University (1999–2008). Over these periods he was heavily involved in the design and implementation of major scientific projects in life sciences and agriculture in China. He is an academician of CAS and member of the Academy of Sciences for the Developing World. His scientific contributions mainly cover plant tissue culture, hormone mechanism in development, as well as plant developmental response to environment. Xu, as a scientist and leader who has made an impact in the community, called up a lot of excellent young scientists returning to China. His efforts have promoted the fast development of China's plant and agricultural sciences.
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Affiliation(s)
- Zhi-Hong Xu
- School of Life Sciences, Peking University, Beijing 100871, China
- Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
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Wang X, Bai SN. Key innovations in transition from homospory to heterospory. PLANT SIGNALING & BEHAVIOR 2019; 14:1596010. [PMID: 30892985 PMCID: PMC6546143 DOI: 10.1080/15592324.2019.1596010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/07/2019] [Accepted: 03/10/2019] [Indexed: 06/01/2023]
Abstract
Heterospory (i.e. dimorphic spores) is a long-lasting topic discussed in plant biology. It is observed in many of ferns, fern allies, and seed plants. The rise of heterospory and the mechanisms underlying its success in plant evolution are not clearly elucidated. In this short communication, an attempt is made to shed some light on these two questions.
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Affiliation(s)
- Xin Wang
- CAS Key Laboratory of Economic Stratigraphy and Paleogeography, Nanjing Institute of Geology and Palaeontology, Nanjing, China
- Center for Excellence in Life and Paleoenvironment, CAS, Nanjing, China
| | - Shu-Nong Bai
- State Key Laboratory of Protein & Plant Gene Research, Quantitative Biology Center, College of Life Science, Peking University, Beijing, China
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