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Recent progress in single-cell transcriptomic studies in plants.

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Title: Recent progress in single-cell transcriptomic studies in plants.
Authors: Cho, Yuhan1 (AUTHOR), Kadam, Ulhas1 (AUTHOR) ukadam@gnu.ac.kr, Park, Bogun1 (AUTHOR), Amariillis, Shandra1 (AUTHOR), Nguyen, Kim-Ngan Thi1 (AUTHOR), Can, Mai-Huong Thi1 (AUTHOR), Lee, Kyun Oh1 (AUTHOR), Park, Soon Ju1 (AUTHOR), Chung, Woo Sik1 (AUTHOR), Hong, Jong Chan1 (AUTHOR) jchong@gnu.ac.kr
Source: Plant Biotechnology Reports. Apr2025, Vol. 19 Issue 2, p91-103. 13p.
Subjects: Plant cells & tissues, Cytology, Plant genetics, Plant RNA, Life sciences
Abstract: Plants are complex multi-cellular organisms. Each tissue has its unique role and a variety of cell types that contribute to overall function. Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to study this cellular diversity. This technology allows us to identify rare cell types and understand their functions within the plant. Additionally, spatial transcriptomics provides a gene expression map within tissue and empowers us to see how cells interact and contribute to tissue-specific functions within their spatial context. While spatial transcriptomics has dramatically advanced our understanding of plant biology, it still faces challenges in capturing individual cells' complete gene expression profiles. Here, we provide a comprehensive overview of scRNA-seq and spatial transcriptomics, including the experimental procedures, computational methods, and data integration strategies. It highlights the impact of these technologies on plant cell biology, discusses their strengths and limitations, and offers a glimpse into the future of this exciting field. As these technologies continue to evolve, they will provide an increasingly detailed and comprehensive view of plant cells, leading to discoveries about plant development, function, and response to the environment. [ABSTRACT FROM AUTHOR]
Copyright of Plant Biotechnology Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Recent progress in single-cell transcriptomic studies in plants.
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  Data: <searchLink fieldCode="AR" term="%22Cho%2C+Yuhan%22">Cho, Yuhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kadam%2C+Ulhas%22">Kadam, Ulhas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ukadam@gnu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Park%2C+Bogun%22">Park, Bogun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Amariillis%2C+Shandra%22">Amariillis, Shandra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Kim-Ngan+Thi%22">Nguyen, Kim-Ngan Thi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Can%2C+Mai-Huong+Thi%22">Can, Mai-Huong Thi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Kyun+Oh%22">Lee, Kyun Oh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Soon+Ju%22">Park, Soon Ju</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chung%2C+Woo+Sik%22">Chung, Woo Sik</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hong%2C+Jong+Chan%22">Hong, Jong Chan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jchong@gnu.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Plant+Biotechnology+Reports%22">Plant Biotechnology Reports</searchLink>. Apr2025, Vol. 19 Issue 2, p91-103. 13p.
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  Data: Plants are complex multi-cellular organisms. Each tissue has its unique role and a variety of cell types that contribute to overall function. Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to study this cellular diversity. This technology allows us to identify rare cell types and understand their functions within the plant. Additionally, spatial transcriptomics provides a gene expression map within tissue and empowers us to see how cells interact and contribute to tissue-specific functions within their spatial context. While spatial transcriptomics has dramatically advanced our understanding of plant biology, it still faces challenges in capturing individual cells' complete gene expression profiles. Here, we provide a comprehensive overview of scRNA-seq and spatial transcriptomics, including the experimental procedures, computational methods, and data integration strategies. It highlights the impact of these technologies on plant cell biology, discusses their strengths and limitations, and offers a glimpse into the future of this exciting field. As these technologies continue to evolve, they will provide an increasingly detailed and comprehensive view of plant cells, leading to discoveries about plant development, function, and response to the environment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Plant Biotechnology Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Plant genetics
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              Text: Apr2025
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