Legume Genomics and Genetics 2025, Vol.16, No.3, 143-152 http://cropscipublisher.com/index.php/lgg 147 4.5 Integration of scRNA-seq with other omics tools The combination of scRNA-seq with ATAC-seq and spatial transcriptomics and proteomics allows scientists to study root development at multiple levels of detail. The combination of different methods in multimodal approaches allows researchers to build gene regulatory networks and find gene expression locations and link transcriptomic data to protein functions and chromatin states (Serrano-Ron et al., 2021; Zheng et al., 2021). The combination of these approaches enables researchers to study root biology as a whole system which helps them understand how cells decide their fate and how they respond to their environment. 5 Case Study 5.1 scRNA-seq in Medicago truncatula root nodulation Scientists use single-nucleus RNA sequencing (sNucRNA-seq) technology to analyze cell-type-specific responses that occur during the initial nodulation process in Medicago truncatula. The researchers established a complete gene expression map through profiling of nuclei from roots that received mock inoculation and rhizobia inoculation which resulted in 25 distinct cell clusters that they identified using Medicago and Arabidopsis marker genes. The analysis showed that root hair cells together with cortex cells and endodermis cells and pericycle cells demonstrated the most significant changes in gene expression following rhizobial infection. The sNucRNA-seq method revealed existing genes and newly discovered signaling pathways which control symbiotic communication start-up and delivered detailed knowledge about nodule development and root cell transformation (Figure 2) (Cervantes-Pérez et al. 2022). 5.2 Application in Lotus japonicus Root Development While the provided search did not yield direct scRNA-seq studies in Lotus japonicus, the methodologies and insights from Medicago truncatula are highly relevant. The model legume Lotus japonicus exhibits identical root developmental patterns and symbiotic processes to Medicago and researchers can directly use established protocols for nuclei extraction and cell-type identification through cross-species marker gene analysis. The research of Lotus japonicus through single-cell methods will enhance our understanding of root and nodule development because it allows cell-type-specific gene expression analysis that extends the findings of Medicago research (Cervantes-Pérez et al., 2022). 5.3 Lessons learned and methodological improvements The research studies demonstrate multiple essential findings together with new methodological techniques. The technique sNucRNA-seq enables researchers to detect both frequent and infrequent cell types and their particular reactions to symbiotic signals. The research by Cervantes-Pérez et al. (2022) shows that Arabidopsis marker genes can effectively identify cell types in legumes even when there are limited cell-type-specific markers available. Discovery of Novel Pathways: Single-cell approaches uncover previously unrecognized genes and regulatory networks involved in nodulation and root development. Technical adaptations: the use of nuclei rather than whole cells overcomes challenges posed by plant cell walls, improving the feasibility and quality of single-cell transcriptomic studies in legumes. 6 Challenges and Future Directions in scRNA-Seq for Legume Root Development 6.1 Technical limitations: protoplasting, cell viability, and biases in cell capture The main technical hurdle in plant scRNA-seq analysis requires protoplasting to remove cell walls but this process triggers stress reactions that damage cells and produces false transcriptional data (Shaw et al., 2020; Sun et al., 2024). The dissociation process fails to properly break down cells with thick or lignified walls which results in uneven cell collection and incomplete cellular mapping (Shaw et al., 2020; Sun et al., 2024). The analysis of single-cell RNA sequencing data faces two major challenges which scientists need to solve by designing experiments correctly and implementing data normalization methods to remove technical artifacts and batch effects (Shaw et al., 2020; Sun et al., 2024).
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