Article(id=1321785454987432529, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321785454333121105, articleNumber=null, orderNo=null, doi=10.11983/CBB24048, pmid=null, cstr=32102.14.CBB24048, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1711641600000, receivedDateStr=2024-03-29, revisedDate=null, revisedDateStr=null, acceptedDate=1721664000000, acceptedDateStr=2024-07-23, onlineDate=1793067810054, onlineDateStr=2026-10-27, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793067810054, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793067810054, creator=13701087609, updateTime=1793067810054, updator=13701087609, issue=Issue{id=1321785454333121105, tenantId=1146029695717560320, journalId=1316781310003478668, year='2025', volume='60', issue='1', pageStart='1', pageEnd='143', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793067809899, creator='13701087609', updateTime=1793068026855, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321786364417397082, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321785454333121105, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321786364417397083, tenantId=1146029695717560320, journalId=1316781310003478668, issueId=1321785454333121105, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=101, endPage=113, ext={EN=ArticleExt(id=1321785455205536338, articleId=1321785454987432529, tenantId=1146029695717560320, journalId=1316781310003478668, language=EN, title=Advances in the Application of Single-cell Transcriptomics in Plant Growth, Development and Stress Response, columnId=1317485676040409248, journalTitle=Chinese Bulletin of Botany, columnName=SPECIAL TOPICS, runingTitle=null, highlight=null, articleAbstract=

Single-cell transcriptomics has improved the spatiotemporal resolution from multi-cell to single-cell levels, and notable progress in this technique has facilitated the identification of new rare cell types, exploration of intercellular heterogeneity, and mapping of cell developmental trajectories. Single-cell transcriptomics is currently being widely used in various research fields such as plant growth and development, stress response, and environmental adaptability, which helps to more thoroughly and precisely uncover the molecular regulatory mechanisms underlying plant life processes. However, there are numerous challenges associated with the study and analysis of different plant species. In this review, we compare and evaluate various single-cell transcription techniques and processes, summarize plant single-cell studies in recent years, and explore new single-cell analysis tools to support researchers studying plant biology with high precision and dynamics. In addition, we propose future directions in using single-cell transcriptomics technologies to address some of the key challenges in plant research and breeding. Furthermore, some important methods for addressing plant research and breeding with single-cell transcriptomics are discussed, along with their difficulties and potential applications.

, authors=Yaping Wang, Wenquan Bao, Yu’e Bai*, authorsList=Yaping Wang, Wenquan Bao, Yu’e Bai, authorCompany=null, correspAuthors=Yu’e Bai, authorNote=null, correspAuthorsNote=
* E-mail:
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单细胞转录组学将时空分辨率从多细胞水平转移到单细胞水平, 该技术的快速发展能够更好地揭示新的稀有细胞类型、挖掘细胞间异质性并绘制细胞发育轨迹图。目前, 单细胞转录组学已广泛应用于植物生长发育、应激反应和环境适应等不同研究方向, 有助于更精确、全面地揭示植物生命过程中的分子调控机制。然而, 单细胞转录组学在不同植物中的研究及应用仍面临诸多挑战。该文比较和评估了不同类型的单细胞转录组技术及其流程, 总结了近年来单细胞转录组学在多种植物中的相关研究进展, 并探索了新型单细胞分析工具, 可为以高精度和高动态探究植物生物学的研究人员提供技术支持。最后, 提出了使用单细胞转录组学技术解决植物研究和育种中的一些关键问题、面临的挑战以及未来的发展方向。

, authors=王亚萍, 包文泉, 白玉娥*, authorsList=王亚萍, 包文泉, 白玉娥, authorCompany=null, correspAuthors=白玉娥, authorNote=null, correspAuthorsNote=
* 白玉娥, 女, 内蒙古农业大学教授, 博士生导师, 长期从事林木种质资源收集评价及改良和林木生物技术研究, 重点对生态经济型林木沙地云杉、仁用杏、红枣及杨树等开展深入研究。发表学术论文50多篇, 获批8项国家发明专利, 审定良种7个, 新品种1个, 出版学术专著和教材3部。E-mail:
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Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root. Nat Commun 12, 2053., articleTitle=Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root, refAbstract=null), Reference(id=1321791810603697027, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, doi=null, pmid=null, pmcid=null, year=2019a, volume=12, issue=null, pageStart=648, pageEnd=660, url=null, language=null, rfNumber=[73], rfOrder=79, authorNames=Zhang TQ, Xu ZG, Shang GD, Wang JW, journalName=Mol Plant, refType=null, unstructuredReference=Zhang TQ, Xu ZG, Shang GD, Wang JW (2019a). A single-cell RNA sequencing profiles the developmental landscape of Arabidopsis root. 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Massively parallel digital transcriptional profiling of single cells. Nat Commun 8, 14049., articleTitle=Massively parallel digital transcriptional profiling of single cells, refAbstract=null), Reference(id=1321791810842772358, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, doi=null, pmid=null, pmcid=null, year=2023, volume=42, issue=null, pageStart=112676, pageEnd=null, url=null, language=null, rfNumber=[76], rfOrder=82, authorNames=Zhu J, Lolle S, Tang A, Guel B, Kvitko B, Cole B, Coaker G, journalName=Cell Rep, refType=null, unstructuredReference=Zhu J, Lolle S, Tang A, Guel B, Kvitko B, Cole B, Coaker G (2023). Single-cell profiling of Arabidopsis leaves to Pseudomonas syringae infection. Cell Rep 42, 112676., articleTitle=Single-cell profiling of Arabidopsis leaves to Pseudomonas syringae infection, refAbstract=null), Reference(id=1321791810905686919, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, doi=null, pmid=null, pmcid=null, year=2017, volume=65, issue=null, pageStart=631, pageEnd=643, url=null, language=null, rfNumber=[77], rfOrder=83, authorNames=Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W, journalName=Mol Cell, refType=null, unstructuredReference=Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W (2017). Comparative analysis of single-cell RNA sequencing methods. 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Comparative analysis of three droplet-based single-cell RNA-seq systems

, figureFileSmall=null, figureFileBig=null, tableContent=
Drop-seq (Dolomite Bio) InDrop (1CellBio) 10× Genomics
珠型 脆性树脂 水凝胶 水凝胶
主要特点
温度控制; 无专有生化试剂
细胞捕获率非常高
乳液中的凝胶珠(GEM)液滴; 液滴内的反转录
每个样本捕获的细胞数 50000+
40000+
80000+
每次运行细胞数 最多8个
最多6个
最多8个
运行时间 20分钟 - <9分钟
捕获效率 10% >90% 高达65%
双峰率
6%-8%
4%
每捕获1000个细胞约0.9%; 每捕获10000个细胞约8%
成本 0.16美元(不包括测序) 0.65美元(不含反转录酶(RT), 含测序) <0.35美元(包括测序)
分析流程 开源 OneCellPipe Cell Ranger
参考文献 Macosko et al., 2015 Klein et al., 2015; Zilionis et al., 2017 Zheng et al., 2017; Zhang et al., 2019b
), ArticleFig(id=1321791797454553902, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, language=CN, label=表1, caption=

3种基于液滴的单细胞转录组测序系统比较分析

, figureFileSmall=null, figureFileBig=null, tableContent=
Drop-seq (Dolomite Bio) InDrop (1CellBio) 10× Genomics
珠型 脆性树脂 水凝胶 水凝胶
主要特点
温度控制; 无专有生化试剂
细胞捕获率非常高
乳液中的凝胶珠(GEM)液滴; 液滴内的反转录
每个样本捕获的细胞数 50000+
40000+
80000+
每次运行细胞数 最多8个
最多6个
最多8个
运行时间 20分钟 - <9分钟
捕获效率 10% >90% 高达65%
双峰率
6%-8%
4%
每捕获1000个细胞约0.9%; 每捕获10000个细胞约8%
成本 0.16美元(不包括测序) 0.65美元(不含反转录酶(RT), 含测序) <0.35美元(包括测序)
分析流程 开源 OneCellPipe Cell Ranger
参考文献 Macosko et al., 2015 Klein et al., 2015; Zilionis et al., 2017 Zheng et al., 2017; Zhang et al., 2019b
), ArticleFig(id=1321791797517468463, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, language=EN, label=Table 2, caption=

Current status of plant single-cell transcriptome research

, figureFileSmall=null, figureFileBig=null, tableContent=
物种 器官/组织 研究方向 测序平台 细胞数量 参考文献
拟南芥(Arabidopsis
thaliana)
根尖 转录图谱 10× Genomics 4727 Denyer et al., 2019
根尖 转录图谱 10× Genomics 7522 Ryu et al., 2019
根尖 转录图谱 10× Genomics 7695 Zhang et al., 2019a
根尖 低磷响应机制 10× Genomics 5145 Wendrich et al., 2020
侧根 侧根发育 10× Genomics 6658 Gala et al., 2021
根尖 转录图谱 10× Genomics 110000 Shahan et al., 2022
根 转录图谱 10× Genomics 3221 Jean-Baptiste et al., 2019
根 转录图谱 Drop-seq 12198 Shulse et al., 2019
根 木质部细胞亚型 Drop-seq 2400 Turco et al., 2019
根 基因复制 10× Genomics 11470 Coate et al., 2020
根 转录图谱 10× Genomics 5283 Dorrity et al., 2021
叶片 转录图谱 10× Genomics 5230 Kim et al., 2021
叶片 空间转录图谱 Stereo-seq 17406 Xia et al., 2022
胚珠原基 雌性细胞发育 10× Genomics 16872 Hou et al., 2021
叶片 细菌胁迫 10× Genomics 11000 Zhu et al., 2023
愈伤组织 器官再生 10× Genomics - Zhai and Xu, 2021
愈伤组织 茎顶端分生组织形成机制 Quartz-Seq2 - Ogura et al., 2023
愈伤组织 愈伤组织从头再生 10× Genomics 35669 Liu et al., 2022
水稻(Oryza sativa) 根尖 转录图谱 10× Genomics 20000 Liu et al., 2021b
根和叶片 转录图谱 10× Genomics 237431 Wang et al., 2021c
根 转录图谱 10× Genomics 27469 Zhang et al., 2021
雌蕊 雌蕊发育 10× Genomics 6004 Li et al., 2023a
玉米(Zea mays) 根尖 氮胁迫 10× Genomics 7000 Li et al., 2022
叶片 叶肉细胞发育 10× Genomics 14656 Tao et al., 2022
叶片 生长高效机制 10× Genomics 7000 Bezrutczyk et al., 2021
穗 转录图谱 10× Genomics 12525 Xu et al., 2021
根 抗茎腐病 10× Genomics 29217 Cao et al., 2023
大豆(Glycine max) 根 根瘤发育 10× Genomics 52775 Sun et al., 2023a
根瘤 根瘤发育 Spatial transcriptomic 26712 Liu et al., 2023
根瘤 根瘤形成 10× Genomics 14639 Cervantes-Pérez et al., 2023
小麦(Triticum aestivum) 保卫细胞 脱落酸(ABA)胁迫 10× Genomics 8629 Wang et al., 2021a
根 转录图谱 10× Genomics 6875 Zhang et al., 2023
番茄(Solanum lycopersicum) 茎尖 转录图谱 10× Genomics 13377 Tian et al., 2020
茎 茎生根和侧根启动调控机制 10× Genomics 3087 Omary et al., 2022
愈伤组织 空间转录图谱 Spatial transcriptomic 6759 Song et al., 2023
比克氏棉(Gossypium bickii) 子叶 色素腺体发育 10× Genomics 12222 Sun et al., 2023c
陆地棉(G. hirsutum) 胚珠 纤维细胞类型 10× Genomics 21389 Wang et al., 2023
白菜型油菜(Brassica rapa) 叶片 转录图谱 10× Genomics 17372 郭新磊, 2021
茎尖和
叶片
热胁迫 Drop-seq 30000 Sun et al., 2022
花生(Arachis hypogaea) 叶片 转录图谱 10× Genomics 6815 Liu et al., 2021a
草莓(Fragaria vesca) 叶片 真菌胁迫 10× Genomics 50000 Bai et al., 2022
长春花(Catharanthus ros- eus) 叶片 长春碱生物合成 10× Genomics 25000 Sun et al., 2023b
毛竹(Phyllostachys edulis) 根尖 基根发育 10× Genomics 14279 Cheng et al., 2023
杨树(Populus trichocarpa) 茎 木质部单细胞图谱 Drop-seq 9798 Li et al., 2021
茎 转录图谱 10× Genomics 12466 Xie et al., 2022
茎 转录图谱 10× Genomics 6796 Chen et al., 2021
茎 木质部发育及演化 10× Genomics+ MARS-seq2.0 25166 Tung et al., 2023
茎 转录图谱 10× Genomics+ Spatial transcriptomic 19185 Li et al., 2023b
茎 维管组织结构与发育 Spatial transcriptomic - Du et al., 2023
木棉(Bombax ceiba) 花及蒴果 纤维发育 10× Genomics 15567 Ding et al., 2023
龙眼(Dimocarpus longan) 愈伤组织 体胚发育 10× Genomics 28727 张舒婷, 2022
茶树(Camellia sinensis) 叶片 儿茶素酯代谢 10× Genomics 16977 Wang et al., 2022
橡胶树(Hevea brasiliensis) 叶片 真菌胁迫 10× Genomics 44102 Liang et al., 2023
红豆杉(Taxus mairei) 茎 紫杉烷合成调控机制 10× Genomics 10900000 Yu et al., 2023
), ArticleFig(id=1321791797597160240, tenantId=1146029695717560320, journalId=1316781310003478668, articleId=1321785454987432529, language=CN, label=表2, caption=

植物单细胞转录组研究现状

, figureFileSmall=null, figureFileBig=null, tableContent=
物种 器官/组织 研究方向 测序平台 细胞数量 参考文献
拟南芥(Arabidopsis
thaliana)
根尖 转录图谱 10× Genomics 4727 Denyer et al., 2019
根尖 转录图谱 10× Genomics 7522 Ryu et al., 2019
根尖 转录图谱 10× Genomics 7695 Zhang et al., 2019a
根尖 低磷响应机制 10× Genomics 5145 Wendrich et al., 2020
侧根 侧根发育 10× Genomics 6658 Gala et al., 2021
根尖 转录图谱 10× Genomics 110000 Shahan et al., 2022
根 转录图谱 10× Genomics 3221 Jean-Baptiste et al., 2019
根 转录图谱 Drop-seq 12198 Shulse et al., 2019
根 木质部细胞亚型 Drop-seq 2400 Turco et al., 2019
根 基因复制 10× Genomics 11470 Coate et al., 2020
根 转录图谱 10× Genomics 5283 Dorrity et al., 2021
叶片 转录图谱 10× Genomics 5230 Kim et al., 2021
叶片 空间转录图谱 Stereo-seq 17406 Xia et al., 2022
胚珠原基 雌性细胞发育 10× Genomics 16872 Hou et al., 2021
叶片 细菌胁迫 10× Genomics 11000 Zhu et al., 2023
愈伤组织 器官再生 10× Genomics - Zhai and Xu, 2021
愈伤组织 茎顶端分生组织形成机制 Quartz-Seq2 - Ogura et al., 2023
愈伤组织 愈伤组织从头再生 10× Genomics 35669 Liu et al., 2022
水稻(Oryza sativa) 根尖 转录图谱 10× Genomics 20000 Liu et al., 2021b
根和叶片 转录图谱 10× Genomics 237431 Wang et al., 2021c
根 转录图谱 10× Genomics 27469 Zhang et al., 2021
雌蕊 雌蕊发育 10× Genomics 6004 Li et al., 2023a
玉米(Zea mays) 根尖 氮胁迫 10× Genomics 7000 Li et al., 2022
叶片 叶肉细胞发育 10× Genomics 14656 Tao et al., 2022
叶片 生长高效机制 10× Genomics 7000 Bezrutczyk et al., 2021
穗 转录图谱 10× Genomics 12525 Xu et al., 2021
根 抗茎腐病 10× Genomics 29217 Cao et al., 2023
大豆(Glycine max) 根 根瘤发育 10× Genomics 52775 Sun et al., 2023a
根瘤 根瘤发育 Spatial transcriptomic 26712 Liu et al., 2023
根瘤 根瘤形成 10× Genomics 14639 Cervantes-Pérez et al., 2023
小麦(Triticum aestivum) 保卫细胞 脱落酸(ABA)胁迫 10× Genomics 8629 Wang et al., 2021a
根 转录图谱 10× Genomics 6875 Zhang et al., 2023
番茄(Solanum lycopersicum) 茎尖 转录图谱 10× Genomics 13377 Tian et al., 2020
茎 茎生根和侧根启动调控机制 10× Genomics 3087 Omary et al., 2022
愈伤组织 空间转录图谱 Spatial transcriptomic 6759 Song et al., 2023
比克氏棉(Gossypium bickii) 子叶 色素腺体发育 10× Genomics 12222 Sun et al., 2023c
陆地棉(G. hirsutum) 胚珠 纤维细胞类型 10× Genomics 21389 Wang et al., 2023
白菜型油菜(Brassica rapa) 叶片 转录图谱 10× Genomics 17372 郭新磊, 2021
茎尖和
叶片
热胁迫 Drop-seq 30000 Sun et al., 2022
花生(Arachis hypogaea) 叶片 转录图谱 10× Genomics 6815 Liu et al., 2021a
草莓(Fragaria vesca) 叶片 真菌胁迫 10× Genomics 50000 Bai et al., 2022
长春花(Catharanthus ros- eus) 叶片 长春碱生物合成 10× Genomics 25000 Sun et al., 2023b
毛竹(Phyllostachys edulis) 根尖 基根发育 10× Genomics 14279 Cheng et al., 2023
杨树(Populus trichocarpa) 茎 木质部单细胞图谱 Drop-seq 9798 Li et al., 2021
茎 转录图谱 10× Genomics 12466 Xie et al., 2022
茎 转录图谱 10× Genomics 6796 Chen et al., 2021
茎 木质部发育及演化 10× Genomics+ MARS-seq2.0 25166 Tung et al., 2023
茎 转录图谱 10× Genomics+ Spatial transcriptomic 19185 Li et al., 2023b
茎 维管组织结构与发育 Spatial transcriptomic - Du et al., 2023
木棉(Bombax ceiba) 花及蒴果 纤维发育 10× Genomics 15567 Ding et al., 2023
龙眼(Dimocarpus longan) 愈伤组织 体胚发育 10× Genomics 28727 张舒婷, 2022
茶树(Camellia sinensis) 叶片 儿茶素酯代谢 10× Genomics 16977 Wang et al., 2022
橡胶树(Hevea brasiliensis) 叶片 真菌胁迫 10× Genomics 44102 Liang et al., 2023
红豆杉(Taxus mairei) 茎 紫杉烷合成调控机制 10× Genomics 10900000 Yu et al., 2023
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单细胞转录组学在植物生长发育及胁迫响应中的应用进展
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王亚萍 , 包文泉 , 白玉娥 *
植物学报 | 专题论坛 2025,60(1): 101-113
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植物学报 |专题论坛 2025 , 60 (1) : 101 -113
单细胞转录组学在植物生长发育及胁迫响应中的应用进展
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王亚萍, 包文泉, 白玉娥*
作者信息
  • 内蒙古农业大学林学院, 呼和浩特 010019
通讯作者:
* 白玉娥, 女, 内蒙古农业大学教授, 博士生导师, 长期从事林木种质资源收集评价及改良和林木生物技术研究, 重点对生态经济型林木沙地云杉、仁用杏、红枣及杨树等开展深入研究。发表学术论文50多篇, 获批8项国家发明专利, 审定良种7个, 新品种1个, 出版学术专著和教材3部。E-mail:
Advances in the Application of Single-cell Transcriptomics in Plant Growth, Development and Stress Response
Yaping Wang, Wenquan Bao, Yu’e Bai*
Affiliations
  • College of Forestry, Inner Mongolia Agricultural University, Huhhot 010019
  • Corresponding Author:
出版时间: 2025-01-10 doi: 10.11983/CBB24048
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单细胞转录组学将时空分辨率从多细胞水平转移到单细胞水平, 该技术的快速发展能够更好地揭示新的稀有细胞类型、挖掘细胞间异质性并绘制细胞发育轨迹图。目前, 单细胞转录组学已广泛应用于植物生长发育、应激反应和环境适应等不同研究方向, 有助于更精确、全面地揭示植物生命过程中的分子调控机制。然而, 单细胞转录组学在不同植物中的研究及应用仍面临诸多挑战。该文比较和评估了不同类型的单细胞转录组技术及其流程, 总结了近年来单细胞转录组学在多种植物中的相关研究进展, 并探索了新型单细胞分析工具, 可为以高精度和高动态探究植物生物学的研究人员提供技术支持。最后, 提出了使用单细胞转录组学技术解决植物研究和育种中的一些关键问题、面临的挑战以及未来的发展方向。

植物生长发育  /  胁迫响应  /  单细胞转录组测序  /  10× Genomics  /  细胞类型

Single-cell transcriptomics has improved the spatiotemporal resolution from multi-cell to single-cell levels, and notable progress in this technique has facilitated the identification of new rare cell types, exploration of intercellular heterogeneity, and mapping of cell developmental trajectories. Single-cell transcriptomics is currently being widely used in various research fields such as plant growth and development, stress response, and environmental adaptability, which helps to more thoroughly and precisely uncover the molecular regulatory mechanisms underlying plant life processes. However, there are numerous challenges associated with the study and analysis of different plant species. In this review, we compare and evaluate various single-cell transcription techniques and processes, summarize plant single-cell studies in recent years, and explore new single-cell analysis tools to support researchers studying plant biology with high precision and dynamics. In addition, we propose future directions in using single-cell transcriptomics technologies to address some of the key challenges in plant research and breeding. Furthermore, some important methods for addressing plant research and breeding with single-cell transcriptomics are discussed, along with their difficulties and potential applications.

plant growth and development  /  stress response  /  single-cell RNA sequencing (scRNA-seq)  /  10× Genomics  /  cell type
王亚萍, 包文泉, 白玉娥. 单细胞转录组学在植物生长发育及胁迫响应中的应用进展. 植物学报, 2025 , 60 (1) : 101 -113 . DOI: 10.11983/CBB24048
Yaping Wang, Wenquan Bao, Yu’e Bai. Advances in the Application of Single-cell Transcriptomics in Plant Growth, Development and Stress Response[J]. Chinese Bulletin of Botany, 2025 , 60 (1) : 101 -113 . DOI: 10.11983/CBB24048
传统的测序(bulk RNA-seq)技术以整个个体、器官或组织为样本, 其分析结果是所研究效应的均值, 无法揭示不同类型细胞之间的基因表达异质性。单细胞RNA测序(single-cell RNA-seq, scRNA-seq)从单个细胞水平获取基因表达信息, 可以深入分析不同类型细胞基因表达变化及某些特异细胞的功能。2009年, 单细胞技术首次应用于小鼠囊胚细胞mRNA-seq检测, 比使用数百个囊胚细胞的微阵列检测增加了5 270个基因的表达(Tang et al., 2009)。目前, 在动物中已经通过scRNA-seq解析了许多复杂系统, 包括癌症、免疫紊乱以及疾病的表观遗传控制(Tang et al., 2009; 杨梅等, 2021; 蔡鹏飞, 2022; Chen et al., 2024)。然而, 由于植物细胞相对于动物细胞存在细胞壁, 且不同类型细胞的细胞壁厚薄不均, 酶解条件不易协调; 同时, 植物细胞大小存在巨大差异, 因此单细胞技术在植物中的应用相对迟缓。值得注意的是, 自2019年以来, 随着植物细胞图谱的建立(Rhee et al., 2019), 植物单细胞相关研究呈现加速趋势, 使人们对植物细胞类型、其动态轨迹和相互作用以及植物细胞内代谢物、核酸和蛋白质的高分辨率定位有了更深入的理解。
植物单细胞转录组测序作为一种先进的技术, 用于揭示植物细胞间的异质性, 识别不同细胞类型及其基因表达特征。随着单细胞测序技术的不断发展, 先后开发了Smart-Seq、Smart-Seq2、Drop-seq、InDrop和10× Genomics等(Tang et al., 2009)。本综述关注的范围侧重于液滴法(Droplet-based), 包括InDrop、Drop-seq和10× Genomics, 不同技术在细胞捕获效率、双峰率及成本方面有所不同, 具体特征见表1。其中, Drop-seq和10× Genomics是迄今为止已应用于植物研究的方法。Drop-seq是第一个利用微流体技术与条形码标记相结合的scRNA-seq技术, 可实现单细胞转录组的高通量并行测序(Macosko et al., 2015)。2017年, 最新的单细胞技术10× Genomics与高效的组织裂解方案相结合, 提高了捕获效率和基因检测精度, 极大地推进了scRNA-seq在植物中的应用(Zheng et al., 2017)。此外, 10× Genomics的灵敏度最高, 是检测低表达转录本的最佳选择。Drop-seq是最具成本效益的技术, 但捕获率和灵敏度较低。总体而言, 利用这两种方法都能为植物单细胞分析获得高质量的数据, 在植物相关研究中得到迅速发展。
scRNA-seq的一般工作流程包括样本准备、单细胞分离、RNA提取、cDNA合成、文库构建、测序和数据分析等多个步骤(图1) (Wang et al., 2021b)。在样本准备阶段, 要选择合适的植物样本, 如幼嫩的叶片、根部或茎尖(Chen et al., 2019; Kubo et al., 2019; Song et al., 2020; Xu et al., 2021)。单细胞分离通常采用多种方法, 其中液滴技术由于通量高、工作量小、污染少且精度高而成为目前最常用的技术。基于液滴的scRNA-seq技术基本原理是将单个细胞和条形码珠子封装到纳升级的液滴中。珠子上涂有DNA寡核苷酸探针(称为Unique Molecular Identifiers, UMI), 探针编码带有poly(T)序列的转录本特异性条形码。液滴包含互补DNA (complementary DNA, cDNA)合成所需的所有缓冲液和酶。封装后, 细胞通过酶促作用裂解, 带poly(A)尾的mRNA被条形码珠捕获, 随后反转录为带有条形码和UMI信息的cDNA, 用于文库构建(Ziegenhain et al., 2017)。单细胞转录组数据分析包含多个步骤。流程分析软件包括cell ranger (Efroni et al., 2016)、Copilot (Zheng et al., 2017)和Seurat (Shahan et al., 2022)等, 用于原始数据的质量控制、标准化、原始数据降维和可视化。通常会鉴定高变异基因(highly variable genes, HVGs), 并剔除无信息的基因, 从而得到可靠的数据集(Ryu et al., 2019)。进一步的分析包括PCA (principal component analysis)降维和聚类分析, 以及t-SNE (t-distributed stochastic neighbor embedding)和UMAP (uniform manifold approximation and projection)的应用(细胞簇的可视化和数据注释(Wendrich et al., 2020))。在植物研究中常使用Louwan聚类算法, 该方法通过优化类内和类间的重叠来改善聚类的分离效果(Dorrity et al., 2021)。
为了探索转录轨迹, 需要验证细胞簇中的新标记基因并预测它们的生物学功能, 此过程涵盖细胞和基因2个水平的探究。细胞水平的分析包括细胞类型鉴定和轨迹分析, 而基因水平的分析涵盖差异表达、基因集和基因调控网络。细胞类型鉴定方法主要包括人工鉴定和自动化注释, 前者综合利用多方信息进行标记基因注释, 后者则利用自动化工具提高注释效率, 所用工具包括scCATCH (single cell Cluster-based Annotation Toolkit for Cellular Heterogeneity)、SingleR及cellassign等(李益和孙超, 2021)。拟时分析揭示了细胞发育过程中的演化轨迹和分化过程, 有助于理解细胞类型的转变、分化及发育进程。Monocle是目前常用的拟时分析工具, 通过生成树寻找最小路径, 投射其它数据点, 绘制出细胞分化轨迹图(Trapnell et al., 2014; Qiu et al., 2017)。
单细胞转录组学已经成为当今植物研究中的一项新兴热门技术, 通过该技术可以更加深入地了解植物细胞的多样性、分化和功能, 高分辨率的细胞水平分析为植物研究提供新视角。自2019年以来, 植物中关于单细胞测序的研究文献已经超过100篇, 我们整理了涉及拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、玉米(Zea mays)、大豆(Glycine max)和杨树(Populus trichocarpa)等多个物种的54篇文章, 涵盖根、叶、花、果及愈伤组织等器官/组织, 揭示了植物组织和器官中不同细胞类型之间的转录调控网络, 及其在发育、应激和环境适应等方面的重要功能(表2)。
拟南芥根由于其结构简单且具有已知的细胞类型标记, 非常适合进行scRNA-seq。2019年发表了6篇相关研究论文, 都成功地将基于液滴的scRNA-seq测序应用于植物研究中, 且研究集中于拟南芥主根(Ryu et al., 2019; Denyer et al., 2019; Zhang et al., 2019a; Jean-Baptiste et al., 2019; Shulse et al., 2019; Turco et al., 2019)。随后, Gala等(2021)使用10× Genomics平台对拟南芥侧根进行scRNA-seq。目前,已经建立了拟南芥根全面且高分辨率的单细胞表达图谱(Cole et al., 2021), 确定了根组织包含的主要细胞类型, 且鉴定到之前未明确定义的根细胞类型亚类并明确不同根细胞之间的分子调控网络(Ryu et al., 2019; Zhang et al., 2019a; Jean-Baptiste et al., 2019; Shulse et al., 2019; Turco et al., 2019; Wendrich et al., 2020; Coate et al., 2020; Dorrity et al., 2021; Gala et al., 2021; Shahan et al., 2022)。Shahan等(2022)通过scRNA-seq比较野生型和突变植株(shr与scr)之间的细胞类型特征和组成, 揭示了转录因子(transcription factor, TF)精确调节根发育的机制。Omary等(2022)通过对番茄(Solanum lycopersicum)茎生根不同发育时间进行scRNA-seq, 表明茎生根源自番茄韧皮部薄壁组织, 进一步确定了根分生组织起始最早阶段特有的过渡状态。scRNA-seq已应用于解析农作物和模式物种中特定细胞类型位置和根尖分生组织(root apical meristem, RAM)的分化轨迹(Liu et al., 2021b; Zhang et al., 2021, 2023)。例如, 豆科植物根部转录动力学的单细胞分析为阐明根瘤形成相关分子调控网络提供了新见解(Sun et al., 2023a; Liu et al., 2023; Cervantes-Pérez et al., 2023)。为探明单子叶和双子叶植物根细胞类型发育的进化保守性, Liu等(2021b)比较了水稻和拟南芥根组织的scRNAseq数据, 有趣的是, 两者在根分生细胞、非根毛细胞和根毛细胞、韧皮部及木质部细胞相对应的簇具有高度相似性, 而表皮、内皮层、中柱和根冠细胞簇之间存在较大差异。此外, 通过原位杂交及拟时轨迹分析, 发现毛竹(Phyllostachys edulis)根冠和表皮起源于共同的初始细胞谱系, 揭示了毛竹基根发育的特殊性(Cheng et al., 2023)。
叶片是维管植物主要的光合作用场所, 是重要而复杂的植物器官。通过scRNA-seq可了解植物叶片的发育变化。例如, Kim等(2021)富集了拟南芥叶片的维管细胞, 建立了其叶片维管系统的单细胞转录组图谱, 鉴定了至少19个细胞簇, 包括表皮、保卫细胞、排水器、叶肉和所有维管细胞类型。牛艳丽等(2017)综述了单细胞组学技术在不同植物保卫细胞中的相关研究, 揭示了植物气孔发育及信号转导进程。最近的一项研究使用单细胞空间转录组测序(scStereo-seq), 成功区分拟南芥成熟叶片不同细胞亚型, 包括上下表皮细胞、栅栏叶肉细胞和海绵叶肉细胞, 并确定了叶肉细胞标记基因(Xia et al., 2022)。目前, 采用scRNA-seq方法探究叶片细胞类型及分化轨迹等研究已广泛应用于玉米(Bezrutczyk et al., 2021; Tao et al., 2022)、水稻(Wang et al., 2021c)、比克氏棉(Gossypium bickii) (Sun et al., 2023a)、白菜型油菜(Brassica rapa) (郭新磊, 2021)和花生(Arachis hypogaea) (Liu et al., 2021b)等农作物。最近, 在药用植物中也出现了scRNA-seq的研究热潮, 对于发现天然药用植物种群表型新资源以及改良药用植物种质等均具有重要作用。研究人员探索了长春花(Catharanthus roseus)叶片中单萜吲哚类生物碱(MIA)代谢的空间组织, 并且定位了20个MIA基因的转录本(Sun et al., 2023b)。此外, 通过研究茶树(Camellia sinensis)叶片的单细胞转录组, 分析了其次级代谢产物黄酮类化合物、咖啡因和茶氨酸途径生物合成相关基因在叶片发育过程中的细胞特异性表达, 并在植物中鉴定到一种新型儿茶素酯葡萄糖基转移酶, 还证明该代谢途径为儿茶素酯的新代谢途径(Wang et al., 2022)。
杨树作为木本模式植物, 也是目前木本植物中scRNA- seq应用最多的一个物种。Li等(2021)对84K杨(Populus alba × P. glandulosa)的茎木质部进行单细胞转录组测序, 分析显示木质部组织被分成12个细胞群, 系统解答了次生木质部细胞发育过程和细胞特异性表达模式等科学问题。Chen等(2021)通过对新疆杨(P. alba var. pyramidalis)的茎木质部进行scRNA- seq, 发现它们由高度异质性的细胞组成, 共鉴定到20个假定的细胞簇, 且发现一些新的细胞类型特异性标记基因, 其表达动态可进一步用于重建涉及韧皮部和木质部发育的细胞分化轨迹。Xie等(2022)对毛果杨(P. trichocarpa)茎木质部进行scRNA-seq, 确定了12个细胞簇。Tung等(2023)应用激光显微切割技术, 精细分离了毛果杨、巨桉(Eucalyptus grandis)、昆栏树(Trochodendron aralioides)及鹅掌楸(Liriodendron chinense)的木质部并进行scRNA-seq, 确定了4种不同木本被子植物茎分化木质部中射线和梭状细胞的发育谱系, 并基于对单细胞簇和重叠轨迹的跨物种分析, 揭示了被子植物中高度保守的射线薄壁细胞和可变的梭形细胞。跨物种的进化比较加深了对木质部细胞系形成的理解, 揭示了跨越数亿年的进化历史。最近, 为了鉴定韧皮部细胞和木质部细胞的起源, 在杨树中采用空间转录组及电子显微观察相结合的方法, 系统解析了植物维管组织干细胞的发生与发育过程, 绘制了维管组织干细胞及其衍生细胞各分化阶段的细胞形态结构与特征表达基因图谱, 证实了韧皮部细胞和木质部细胞起源于两类不同的干细胞(Du et al., 2023)。另外, 在杨树中采用scRNA-seq与空间转录组测序(ST-seq)相结合的方法, 对杨树茎初生和次生生长组织进行转录组分析, 细胞图谱与分化轨迹表明, 从形成层到木质部前体和韧皮部前体的细胞分化具有不同的调控网络, 且该调控网络可通过生长素的积累和分布进行调控, 为深入理解树木茎部初生和次生生长的细胞特征和分化过程提供了新见解, 有助于了解多年生树木茎生长过程中发生的细胞分化动态(Li et al., 2023b)。
植物从营养生长过渡到生殖生长后, 顶芽分生组织(shoot apical meristem, SAM)从产生叶原基转变为形成花。30年前, 调控花发育的基因被鉴定, 但由于当时缺乏在分子水平上以单细胞分辨率表征分化细胞的方法, 其精确分化轨迹未能完全阐释。近年来, scRNA-seq和snRNA-seq广泛应用于构建拟南芥花分生组织(Hou et al., 2021)以及水稻雌蕊和玉米穗(Xu et al., 2021; Li et al., 2021)的细胞图谱。需要特别指出的是, 将scRNA-seq与全基因组关联分析(genome wide association study, GWAS)整合鉴定出许多与玉米产量相关的候选基因, 这表明可通过scRNA-seq识别候选基因来提高作物产量(Xu et al., 2021)。在农作物陆地棉(G. hirsutum)中, 通过对野生型及天然无绒毛/无绒毛突变体棉花的胚珠进行scRNA-seq, 确定了包括纤维细胞类型在内的5个细胞群体, 并构建了纤维细胞的发育轨迹(Wang et al., 2023)。另外, 通过对木棉(Bombax ceiba)子房内壁组织进行scRNA-seq, 捕获了处于不同发育状态的细胞, 绘制出子房内壁单细胞图谱, 并跨物种综合分析了木棉纤维形成的分子机制(Ding et al., 2023)。
通过对拟南芥下胚轴诱导的愈伤组织进行scRNA- seq, 共得到10个细胞簇, 分为内细胞层、中间细胞层及外细胞层。在中间细胞层, WOX5直接与PLT1和PLT2互作, 促进TAA1的表达, 从而产生生长素; WOX5还与ARR12相互作用, 抑制A型ARRs, 解除细胞分裂素信号转导中的负反馈回路。综上, 促进生长素的产生和提高细胞分裂素的敏感性均为拟南芥愈伤组织中间细胞层促进器官再生所必需(Zhai and Xu, 2021)。基于Quartz-seq2的单细胞转录组进一步显示, WOX13促进拟南芥愈伤组织中非分生细胞的表达, 是新生芽形成的负调控因子(Ogura et al., 2023)。Liu等(2022)通过对在SIM (Hydrogen Sulfide Indole Motility Medium)培养基上分别培养0、2、4、6和10天的拟南芥愈伤组织进行scRNA-seq, 揭示了其愈伤组织从头再生早期的分化轨迹及关键调控基因。对龙眼(Dimocarpus longan)胚性愈伤组织进行scRNA-seq, 发现28 724个单细胞, 划分为12个细胞亚群, 鉴定到4种细胞类型, 包括增殖细胞(proliferating cell, PC)、分生细胞(meristematic cell, MC)、维管细胞(vascular cell, VC)及表皮细胞(epidermal cell, EPC) (张舒婷, 2022)。应用最新的空间转录组技术, 证明了番茄愈伤组织包含高度异质性的细胞; 根据空间基因表达和组织学观察鉴定到多种细胞类型, 包括表皮、芽原基、维管组织、内部愈伤组织和外生芽细胞; 并通过追踪从芽原基到生长芽的发育轨迹, 解析了其维管组织的发育过程(Song et al., 2023)。
当植物在胁迫条件下发生剧烈的转录组变化时, 研究人员仅能观察应激防御基因在器官水平上的变化趋势。然而, 单细胞研究表明, 植物在遭受生物/非生物胁迫(如热胁迫、盐胁迫、真菌或细菌)后, 不同类型细胞对胁迫的反应可能存在异质性, 而对于特定的细胞类型, 不同的胁迫方式通常会引起一组相似基因的转录变化。分别对正常生长及丁香假单胞菌DC3000处理后的拟南芥叶片进行scRNA-seq, 通过分离原生质体获得11 000个单细胞, 共聚类成18个细胞簇含8种细胞类型, 识别到免疫、过渡和易感状态下的病原体反应细胞簇, 拟时分析揭示了从免疫状态到易感状态的连续过程; 通过共聚焦成像进一步表明, 免疫簇内的细胞正是早期病原体入侵的部位, 而易感簇则表现出更广泛的分布, 并在感染的后期阶段被高度诱导。该研究揭示了受感染叶片内的细胞异质性, 在单细胞水平上解析了植物对生物胁迫的差异反应(Zhu et al., 2023)。通过对正常与缺氮培养基上生长的玉米幼苗根尖进行scRNA-seq, 鉴定到11种主要细胞类型, 发现85个基因以细胞类型特异性的方式响应和表达, 其中4个基因被证明与硝酸盐的吸收或利用有关, 从单细胞水平上揭示了玉米根尖发育过程及对硝酸盐响应的机制(Li et al., 2022)。另外, 玉米根部接种轮枝镰刀菌(Fusarium verticillioides, Fv)后进行scRNA-seq, 获得29 217个单细胞, 并鉴定到7种主要细胞类型。通过加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA), 从4 049个差异表达基因(DEGs)中鉴定出12个Fv响应调控模块, 在7种细胞类型中被Fv感染激活或抑制。该研究以单细胞分辨率深入解析了玉米根尖主要细胞类型的免疫调控网络, 为揭示玉米抗病性的分子机制奠定了基础(Cao et al., 2023)。对白菜型油菜叶片进行scRNA-seq分析, 揭示了其叶片发育、显性亚基因组表达和细胞水平上对热胁迫的转录变化(Sun et al., 2022)。草莓(Fragaria vesca)叶片在早期灰霉病感染过程中的单细胞图谱显示, 排水器、表皮和叶肉细胞中的抗病相关基因存在不同表达模式(Bai et al., 2022)。近期, 在木本植物橡胶树(Hevea brasiliensis)中, 通过对早期白粉病感染期间的叶片进行scRNA-seq, 确定了10种细胞类型。其中, 表皮细胞在白粉病感染下表现出不同的基因表达模式, 且大多数参与宿主-病原体相互作用的基因随着感染过程而上调; 进一步基于表皮细胞中的拟时轨迹, 发现CNL蛋白HbCNL2与HbHSP90相互作用并正向调节橡胶树叶片对白粉病的抗性(Liang et al., 2023)。
目前, 研究人员已在植物不同组织中进行了scRNA测序, 包括拟南芥的根、雌性配子体、茎尖、叶和愈伤组织, 水稻雌蕊、玉米穗、大豆根瘤、杨树茎及木棉花等, 为研究植物细胞发育轨迹并构建单细胞转录组图谱、分析物种细胞类型的保守性和多样性, 高分辨率研究植物细胞对生物和非生物胁迫的响应、探索代谢物生物合成的基因途径等方面提供了新见解。通过scRNA测序技术, 研究人员可以在单细胞水平上观察特定基因的表达模式, 了解细胞间的异质性和功能差异, 从而为育种提供更多的候选基因以及改善作物性状的策略。
尽管单细胞测序技术在植物研究中已取得巨大进展, 但非模式植物的单细胞转录组研究仍面临挑战。首先, 高质量原生质体或细胞核样品的制备对于scRNA-seq的成功至关重要, 然而, 由于细胞壁的存在, 从木本植物中分离原生质体和细胞核极具挑战性。未来的研究应优先考虑开发非模式植物(特别是木本植物)的原生质体分离方法。其次, 非模式植物中标记细胞类型的基因数量较少, 导致难以进行准确的细胞类型注释。为克服这些挑战, 研究人员可根据研究对象和内容的不同, 结合单细胞测序技术与其它生物技术, 如原位杂交和空间转录组技术(肖宇彬等, 2023), 获取更全面且准确的信息。同时, 采用跨物种搜索同源基因等方法, 从相关物种中获取更多的标记基因信息, 以辅助非模式植物中细胞类型的注释。scRNA-seq数据分析的另一个挑战是许多植物物种的参考基因组不完整或注释不够充分, 影响scRNA- seq数据的准确映射和注释。同时, 揭示细胞间异质性需要生成大型数据集和强大的生物信息学数据处理系统, 以准确识别不同的细胞类型(Cole et al., 2021)。然而, 目前尚缺乏使用来自不同植物物种的scRNA-seq数据对细胞类型同源性和多样化进行分类的计算方法(Shaw et al., 2021)。应对这一挑战的策略是建立标准化的植物单细胞基因组学数据库, 以标准化格式整合来自多个物种、不同发育阶段和环境条件的最新T2T基因组、基因注释和scRNA-seq数据(Yin et al., 2023)。
综上所述, 单细胞测序技术为植物研究提供了强大的工具, 有助于深入了解植物发育生物学和抗逆生物学等, 揭示细胞动态变化及调控网络。随着技术的不断发展和优化, 单细胞测序技术将为植物育种和农业生产做出更大的贡献。
王亚萍: 文献收集整理, 作图, 撰写和修改文章; 包文泉: 提供指导和建议, 修改文章; 白玉娥: 选题、构思和修改文章, 资金支持。
  • 国家自然科学基金(32360402)
  • 内蒙古自治区高等学校科学研究项目(B20231093Z)
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doi: 10.11983/CBB24048
  • 接收时间:2024-03-29
  • 首发时间:2026-10-27
  • 出版时间:2025-01-10
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  • 收稿日期:2024-03-29
  • 录用日期:2024-07-23
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内蒙古自治区高等学校科学研究项目(B20231093Z)
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    内蒙古农业大学林学院, 呼和浩特 010019

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* 白玉娥, 女, 内蒙古农业大学教授, 博士生导师, 长期从事林木种质资源收集评价及改良和林木生物技术研究, 重点对生态经济型林木沙地云杉、仁用杏、红枣及杨树等开展深入研究。发表学术论文50多篇, 获批8项国家发明专利, 审定良种7个, 新品种1个, 出版学术专著和教材3部。E-mail:
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2种不同金属材料的力学参数
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genus
种数
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species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
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species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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