Legume Genomics and Genetics 2025, Vol.16, No.3, 143-152 http://cropscipublisher.com/index.php/lgg 144 research presents recent breakthroughs and current obstacles to create a complete guide for scientists who want to use single-cell methods to study legume root biology and its effects on agriculture. 2 Single-Cell RNA Sequencing Technology: Principles, Platforms, and Challenges in Plant Systems 2.1 Principles of scRNA-seq: from tissue dissociation to sequencing Single-cell RNA sequencing (scRNA-seq) allows researchers to study individual cell transcriptomes which reveals hidden cell diversity and rare cell populations that bulk sequencing methods cannot detect. The process begins with tissue dissociation to generate single-cell suspensions followed by cell capture and lysis and then RNA reverse transcription to cDNA followed by amplification and library preparation and high-throughput sequencing. The application of Unique molecular identifiers (UMIs) and cell barcodes by researchers allows them to detect particular cell-derived transcripts and correct amplification bias for accurate gene expression analysis (Liu and Trapnell, 2016; AlJanahi et al., 2018; Rich-Griffin et al., 2019). 2.2 Technical platforms and their application in plant systems Multiple scRNA-seq platforms exist for plant research because they provide different benefits for scientific applications. The Drop-seq system uses microfluidics to create droplets that contain single cells and barcoded beads which allows researchers to analyze thousands of cells simultaneously. It is cost-effective but has lower capture efficiency compared to other platforms (AlJanahi et al., 2018). The 10x Genomics Chromium system operates as a commercial droplet-based platform which achieves high capture efficiency and sensitivity levels for working with scarce tissue samples and detecting rare transcripts (Baran-Gale et al., 2017; Rich-Griffin et al., 2019). The SMART-seq/SMART-seq2 method provides a well-based solution to obtain full-length transcripts with high sensitivity which works best for analyzing rare tissues and small cell numbers. It is often combined with fluorescence-activated cell sorting (FACS) (Baran-Gale et al., 2017). Plant systems have proven the effectiveness of these platforms through their successful application to model species Arabidopsis thaliana, maize and rice which generated complete root cell type and developmental pattern maps (Jovic et al., 2022). 2.3 Challenges in applying scRNA-seq to plant tissues compared to animal systems The application of scRNA-seq to plants requires overcoming specific technical challenges. The process of removing cell walls through enzymatic digestion (protoplasting) for single cell release causes stress responses and cell recovery bias particularly in lignified and suberized tissues (Bawa et al., 2022). The process of cell viability and RNA quality assessment proves more difficult in plants because it affects the final data quality (Rich-Griffin et al., 2019). The complexity of tissue structure together with its cell wall diversity leads to poor dissociation outcomes which results in missing specific cell types (Hazarika et al., 2025). The plant scRNA-seq method operates with lower detection sensitivity and reduced throughput compared to animal systems which need particular optimization protocols (Liu and Trapnell, 2016). 2.4 Data analysis pipelines for plant scRNA-seq The computational process for plant scRNA-seq data analysis consists of multiple stages which begin with Quality Control. The process of filtering out low-quality cells and doublets and empty droplets relies on gene/UMI counts and mitochondrial RNA content. The normalization process enables researchers to achieve proper cell-to-cell comparison through the adjustment of sequencing depth and technical variation (Bacher and Kendziorski, 2016; Andrews et al., 2020). The process of cell type and state identification involves running PCA t-SNE or UMAP algorithms for dimensionality reduction followed by clustering analysis (Andrews et al., 2020). The analysis of marker genes and developmental lineage reconstruction or stimulus response tracking is described in. Specialized Tools: The development of plant-specific pipelines and adaptations continues to address particular difficulties in plant data analysis which include problems caused by protoplast-induced artifacts and cell-type annotation.
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