Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation
Zhijian Liu,Xiangying Kong,Yanping Long,Hong Zhang,Jinbu Jia,Zunmian Zhang,Xianwei Song,Lijuan Qiu,Jixian Zhai,Zhe Yan +9 more
TL;DR: In this paper , a cell atlas of soybean nodules and roots was established by integrating single-nucleus and spatial transcriptomics, which provided a single-cell perspective for understanding rhizobium-legume symbiosis.
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Abstract: Legumes form symbiosis with rhizobium leading to the development of nitrogen-fixing nodules. By integrating single-nucleus and spatial transcriptomics, we established a cell atlas of soybean nodules and roots. In central infected zones of nodules, we found that uninfected cells specialize into functionally distinct subgroups during nodule development, and revealed a transitional subtype of infected cells with enriched nodulation-related genes. Overall, our results provide a single-cell perspective for understanding rhizobium-legume symbiosis.
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Citations
Spatial transcriptomics reveals light-induced chlorenchyma cells involved in promoting shoot regeneration in tomato callus.
Xiehai Song,Pengru Guo,Keke Xia,Meiling Wang,Yongqi Liu,Lichuan Chen,Jinhui Zhang,Mengyuan Xu,Naixu Liu,Zhiliang Yue,Xun Xu,Ying Gu,Gang Li,Min Liu,Liang Fang,Xing Wang Deng,Bosheng Li +16 more
TL;DR: This study provides a spatial single-cell perspective on shoot regeneration in crop callus, offering valuable insights into its regulatory mechanisms.
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Dancing to a different tune, can we switch from chemical to biological nitrogen fixation for sustainable food security?
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TL;DR: In this paper , the root nodule symbiosis from legumes to other crops is explored and future research directions that might help to overcome the barrier of achieving self-fertilising crops are highlighted.
Spatial transcriptomics drives a new era in plant research.
TL;DR: Recent advances in spatial transcriptomics enable the study of single-cells derived from plant tissues from a spatial perspective and is already successfully used to identify cell types, reconstruct cell-fate lineages, and reveal cell-to-cell interactions.
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Understanding plant pathogen interactions using spatial and single-cell technologies
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TL;DR: This review highlights the importance of comprehending plant-pathogen interactions at the single-cell and spatial levels, while also providing an overview of relevant cutting-edge technologies.
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Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics.
Carolin Grones,Thomas Eekhout,Dongbo Shi,Manuel Neumann,L. S. Berg,Yuji Ke,Rachel Shahan,Kevin L. Cox,Fabio Gomez-Cano,Hilde Nelissen,Jan U. Lohmann,Stefania Giacomello,Olivier C. Martin,Benjamin Cole,Jia-Wei Wang,Kerstin Kaufmann,Michael T. Raissig,Gergo Palfalvi,Thomas Greb,Marc Libault,Bert De Rybel +20 more
TL;DR: Common challenges associated with the use of single-cell transcriptomics in plants are discussed and general guidelines to improve reproducibility, quality, comparability, and interpretation are proposed to make the data readily available to the community in this fast-developing field of research.
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References
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Yuhan Hao,Stephanie Hao,Erica Andersen-Nissen,William M. Mauck,Shiwei Zheng,Andrew Butler,Maddie Jane Lee,Aaron J. Wilk,Charlotte A. Darby,Michael Zagar,Paul Hoffman,Marlon Stoeckius,Efthymia Papalexi,Eleni P. Mimitou,Jaison Jain,Avi Srivastava,Tim Stuart,Lamar Ballweber Fleming,Bertrand Z. Yeung,Angela J. Rogers,Juliana M. McElrath,Catherine A. Blish,Raphael Gottardo,Peter Smibert,Rahul Satija +24 more
TL;DR: ‘weighted-nearest neighbor’ analysis is introduced, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.
Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky,Nghia Millard,Jean Fan,Kamil Slowikowski,Fan Zhang,Kevin Wei,Yuriy Baglaenko,Michael B. Brenner,Po-Ru Loh,Po-Ru Loh,Po-Ru Loh,Soumya Raychaudhuri +11 more
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
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