Integrated experimental and computational workflows for single-cell transcriptomics in plants

Background: Single-cell transcriptomics is a powerful approach to resolve cellular heterogeneity, yet its application in plants is constrained by challenges in tissue preparation, nuclei isolation, and transcriptome quality. Optimized experimental and computational workflows are essential to achieve robust results in plant systems. Results: We systematically benchmarked bulk and single-cell transcriptomic workflows in maize and established an integrated, optimized framework. First, we developed an improved bulk RNA-seq protocol, providing higher consistency and serving as a reference for single-cell datasets. Second, we compared three input types, protoplasts, fresh nuclei, and frozen nuclei, across tissues, demonstrating overall comparability of their transcriptomic profiles and offering guidance for studies with limited material. Third, by leveraging bulk RNA-seq as a reference, these complementary data provide additional biological context that helps to interpret and validate findings derived from single-cell transcriptomic analyses. A combination of these strategies resulted in high transcriptome integrity and clear clustering resolution in the final dataset, supporting robust identification of plant cell types. While all experimental data are derived from maize, the principles and strategies described here provide practical guidance and inspiration for single-cell studies in other plant species. Conclusions: Our study establishes optimized experimental and computational workflows for plant single-cell transcriptomics. By validating input comparability and addressing the limitations of nuclear data, we provide methodological guidance that extends beyond maize and supports future single-cell investigations across diverse plant species.