Abstract
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.
Highlights
Modern biology is going through the era of big data and omics technologies
This review aims to show how the combination of SC data, morphometric data, and cell-based models will expand our understanding of tissue and organ morphogenesis
The nature of the proposed framework is hybrid since it combines different mathematical formalisms and modules: (i) Ordinary Differential Equation (ODE)/Partial Differential Equations (PDE) equations for describing the dynamics of substances and morphogens inside the cell and the processes of intercellular transport, (ii) discrete events occurring during the onset of threshold conditions, (iii) the biophysical laws of mechanical interactions between cells
Summary
Modern biology is going through the era of big data and omics technologies. Single-cell sequencing (SCS) is one of the breakthroughs and rapidly developing technologies. McFaline-Figueroa et al (2020) provide currently available techniques for such kind of analysis Such a data-driven approach provides only a partial understanding of the developmental processes for different cell types since it includes only the molecular level. We believe that current methods for processing SC data should be coupled with morphological data on a tissue level and computational frameworks describing tissue development Such a systemicbiological cycle will allow researchers to find out the essential spatiotemporal regulators of morphogenetic processes and provide an in silico - in vivo verification of emerging hypotheses. We consider available tools for cell-based models and present our cell-based modeling framework for morphogenetic processes This algorithm is iterative and includes six main steps: (i) model formulation; (ii) design experiments to obtain microscopy and scRNA-seq data; (iii) obtaining experimental data; (iv) data analysis; (v) data integration into a hybrid (discrete-continuous) mathematical model of morphogenesis; (vi) model validation and verification
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