As the basic building blocks of all living species, cells play a crucial role in sustaining life activities. Traditional bulk methods only give us molecular insight into tissue, organ, or/and individual. With the rapid development of sequencing technology, especially the 10× Genomics based on microfluidic omics, biological research has entered the era of single-cell level, which enables us to gain insight into life activities from more microscopic perspective. With this cutting-edge technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. However, the preparation of single-cell suspension (namely, protoplasm suspension) and the annotation of cell clusters are still two main obstacles faced by single-cell research. A few marker gene databases are currently available, including CellMarker, PanglaoDB, and SignatureDB (https://lymphochip.nih.gov/signaturedb/). Single-cell research in mouse and human is at the rapid development stage. However, in plant science, the research for the single-cell profiling is still in its infancy and quite limited resource were available, such as in Arabidopsis thaliana (Gala et al., 2021; Zhang and Chen, 2021b; Zhang et al., 2019), Zea mays (Marand et al., 2021; Xu et al., 2021), Oryza sativa (Liu, Liang, et al., 2021; Wang et al., 2021; Zhang et al., 2021a), Solanum lycopersicum (Tian et al., 2020), and Arachis hypogaea (Liu, Hu, et al., 2021). The existence of cell wall makes the preparation of plant single-cell suspension more difficult than that of animals. On the other hand, due to the lack of effective marker gene database, single-cell research in plant was usually time-consuming for performing large amount of in situ RNA hybridization or genetic transformation with reporter gene to provide reliable experimental evidence for the identification of tissue type of cell cluster. It is in urgent demands to establish a comprehensive database and exploit efficient tools to analyze such scattered over thousands marker genes of specific cell types from different plant species. Therefore, based on the Shiny and bootstrap frameworks, we developed the Plant Single Cell Transcriptome Hub (PsctH) (http://jinlab.hzau.edu.cn/PsctH/), aiming to provide a comprehensive and accurate resource of cell markers and web tool for various cell types in tissues of plant species (Figure 1a). All marker genes included in the PsctH must have been evidenced via RNA in situ hybridization or expression of GFP reporter. Based on this standard, we presented over 20 published plant single-cell reports including 98 cell markers from 51 cell types in 9 plant tissues/sub-tissues of five plant species (Arabidopsis thaliana, Zea mays, Oryza sativa, Arachis hypogaea, and Solanum lycopersicum), and the data were collected and deposited in PsctH (Figure 1b–d). There have been more than 2 marker genes for phloem parenchyma cells and veins from leaf, cortex, lateral root primordia, and stele from root; inflorescence meristem; and spikelet meristem from staminate primordia. Notably, the majority of the cell marker entries are derived from roots, involving 26 cell types, and the most abundant root cell type is lateral root primordia cell. All marker genes were displayed in page of ‘MarkerGeneDB’ page and can be searched via keywords (Figure 1e). Meanwhile, all experimental evidence derived from RNA in situ hybridization or GFP reporter were accompanied for each marker gene. In addition, all marker genes’ information can be download as a tab-delimited file on the ‘Download’ page and can be easily used in the command line.

Another challenge of applying single-cell analysis in plants is to prepare integrity and living single-cell suspension. The first step of preparing plant single-cell suspension usually starts with digestion of cell wall via cellulase and hemicellulase. In PsctH, we compared and evaluated different protocols used to prepare protoplasts and offer a feasible and efficient experimental pipeline for reference, which included preparation and isolation of tissue samples, enzyme digestion, purification, and the detection of the integrity of single cells (Figure 1f). To standardize and link the process of plant single-cell data mining, a flexible pipeline of plant single-cell transcriptome (scRNA-seq) analysis was also provided, including the process of quality control, normalization and scale, clustering, and marker genes identification (Figure 1g). Users can configure key parameters to obtain a dedicated R analysis script. In addition, we also provide configuration files (SingleCellCondaEnvironment.yml) that can easily reproduce the analysis environment of single-cell transcriptome through conda and run the R script obtained previously.

The scRNA-Seq data are particularly powerful in resolving progressions in gene expression. Single-cell sequencing data from different tissues of different species will help us to construct a complete plant single-cell landscape, dissecting cellular heterogeneity, identify the key regulatory genes involved in life activities, and insight into specific biological mechanisms. In this case, we collected all published high throughput sequencing data of plant single-cell and accessible in ‘SingleCellDB’ page, which were categorized by species and tissues. To best serve the community, we build a user interface that allows intuitive searching of plant single-cell literature and performed text mining using natural language processing (NLP) to highlight the most important keywords and explore their associations. The results are compiled by searching for keywords in abstracts and titles using data from PubMed.

In summary, PsctH will be a comprehensive and valuable resource for researchers applying single-cell experiments to plants. The isolation of protoplast, pipeline of data processing, and manually curated resource of cell markers collected from experimental researches will be expected to promote plant science research into a single-cell resolution. Indeed, the rapidly growing field of single-cell biology has given us an opportunity to constantly improve and enrich the database. Therefore, we will continue to track the single-cell sequencing studies and update the database by frequent additions of new cell markers across all plant species. Meanwhile, we hope researchers can contribute PsctH by submitting data to us by ‘Submit’ page.

作为所有生物物种的基本组成部分，细胞在维持生命活动中起着至关重要的作用。传统的批量方法只能让我们从分子角度了解组织、器官或/和个体。随着测序技术，特别是基于微流控组学的10倍基因组学技术的飞速发展，生物学研究进入了单细胞水平的时代，使我们能够从更微观的角度洞察生命活动。借助这项尖端技术，现在可以以前所未有的方式挖掘组织类型之间和细胞内的异质性。然而，单细胞悬液（即原生质悬液）的制备和细胞簇的注释仍然是单细胞研究面临的两大障碍。目前有一些标记基因数据库可用，包括 CellMarker、PanglaoDB 和 SignatureDB (https://lymphochip.nih.gov/signaturedb/)。小鼠和人类的单细胞研究正处于快速发展阶段。然而，在植物科学中，单细胞分析的研究仍处于起步阶段，可用的资源非常有限，例如拟南芥(Gala et al ., 2021 ; Zhang and Chen, 2021b ; Zhang et al ., 2019 ), Zea mays (Marand et al ., 2021 ; Xu et al ., 2021 ), Oryza sativa (Liu, Liang, et al., 2019) al ., 2021 ; Wang et al ., 2021 ; Zhang et al ., 2021a ), Solanum lycopersicum (Tian et al ., 2020 ) 和Arachis hypogaea (Liu, Hu,等人，2021 年）。细胞壁的存在使得植物单细胞悬液的制备比动物更困难。另一方面，由于缺乏有效的标记基因数据库，在植物中进行单细胞研究通常需要大量的原位研究。与报告基因进行RNA杂交或遗传转化，为细胞簇组织类型的鉴定提供可靠的实验依据。迫切需要建立一个全面的数据库并利用有效的工具来分析来自不同植物物种的特定细胞类型的数千个标记基因。因此，基于 Shiny 和 bootstrap 框架，我们开发了植物单细胞转录组中心 (PsctH) (http://jinlab.hzau.edu.cn/PsctH/)，旨在提供全面、准确的细胞标记资源和用于植物物种组织中各种细胞类型的网络工具（图 1a）。PsctH 中包含的所有标记基因必须已通过 RNA原位证明GFP报告基因的杂交或表达。基于此标准，我们发表了 20 多篇已发表的植物单细胞报告，包括来自 5 种植物（拟南芥、玉米、水稻、花生和茄属）的 9 个植物组织/亚组织中 51 种细胞类型的 98 个细胞标记物。番茄），并且数据被收集并存储在 PsctH 中（图 1b-d）。叶、皮层、侧根原基和根中石碑的韧皮薄壁组织细胞和叶脉已有2个以上的标记基因；花序分生组织；和来自雄蕊原基的小穗分生组织。值得注意的是，大多数细胞标记条目来自根，涉及 26 种细胞类型，其中最丰富的根细胞类型是侧根原基细胞。所有标记基因都显示在“MarkerGeneDB”页面的页面中，可以通过关键字进行搜索（图 1e）。同时，所有实验证据均来自原位RNA每个标记基因都伴随有杂交或 GFP 报告基因。此外，所有标记基因的信息都可以在“下载”页面上以制表符分隔文件的形式下载，并可在命令行中轻松使用。

在植物中应用单细胞分析的另一个挑战是制备完整性和活的单细胞悬浮液。制备植物单细胞悬液的第一步通常是通过纤维素酶和半纤维素酶消化细胞壁。在 PsctH 中，我们比较和评估了用于制备原生质体的不同方案，并提供了一个可行且高效的实验流程供参考，包括组织样本的制备和分离、酶消化、纯化和单细胞完整性检测（图 1f ）。为了规范和链接植物单细胞数据挖掘过程，还提供了一条灵活的植物单细胞转录组（scRNA-seq）分析流程，包括质量控制、标准化和规模化、聚类和标记基因识别的过程（图 1g）。用户可以配置关键参数，获得专用的R分析脚本。此外，我们还提供配置文件（SingleCellCondaEnvironment.yml），可以通过conda轻松复现单细胞转录组的分析环境，运行之前获得的R脚本。

scRNA-Seq 数据在解析基因表达进展方面特别强大。来自不同物种不同组织的单细胞测序数据将帮助我们构建完整的植物单细胞景观，剖析细胞异质性，识别参与生命活动的关键调控基因，深入了解具体的生物学机制。在这种情况下，我们收集了所有已发表的植物单细胞高通量测序数据，并可在“SingleCellDB”页面中访问，这些数据按物种和组织分类。为了最好地服务于社区，我们构建了一个用户界面，允许直观地搜索植物单细胞文献，并使用自然语言处理 (NLP) 执行文本挖掘，以突出最重要的关键字并探索它们的关联。

总之，对于将单细胞实验应用于植物的研究人员来说，PsctH 将是一个全面而有价值的资源。原生质体的分离、数据处理的管道以及从实验研究中收集的细胞标记物的人工管理资源将有望推动植物科学研究进入单细胞分辨率。事实上，快速发展的单细胞生物学领域为我们提供了不断改进和丰富数据库的机会。因此，我们将继续跟踪单细胞测序研究，并通过在所有植物物种中频繁添加新的细胞标记来更新数据库。同时，我们希望研究人员可以通过“提交”页面向我们提交数据来贡献 PsctH。