Table_1_A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis.XLSX

Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants’ features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem’s solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells’ spatial localization in the initial plant organ—one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.

近十年来，单细胞技术（single-cell technology）作为一种新兴且极具前景的高分辨率转录组数据（transcriptomic data）获取手段，主要应用于动物研究领域。不过，已有多个科研团队针对部分植物组织开发并应用了相关实验流程。结合已日趋成熟的细胞分辨率成像技术（cell-resolution imaging techniques），这一突破为解析植物组织形态建成的复杂机制开辟了全新方向。尽管将转录组水平至形态发生水平的数据整合至统一分析框架仍存在诸多挑战，但植物组织具备诸多独特属性。植物的特征之一在于：通过胞间连丝（plasmodesmata）实现的细胞间通讯拓扑结构会随组织生长与形态发生过程逐步形成，并通过调控相邻细胞间的基因表达互作，影响组织内部进程与细胞域的发育。毋庸置疑，在分析单细胞转录组数据时，必须将这一特性纳入考量范畴。已成功应用于植物形态发生研究的基于细胞的计算建模方法（Cell-based computational modeling），有望成为整合这类新型多尺度数据的高效手段。基于真实组织模板构建的上述模型，其逆问题（inverse problem）求解结果可助力解析初始植物器官中单个细胞的空间定位信息——这也是单细胞转录组数据分析中最具歧义性与挑战性的环节之一。本综述总结了依托单细胞转录组数据实现的高级植物形态发生模型所带来的全新研究机遇。此外，本文还探讨了显微镜与细胞分辨率成像技术在解决单细胞转录组数据分析中的多项空间关联问题、并拓展混合建模框架应用潜力方面的发展前景。