Jie Yao

Spatial omics technologies provide new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organized cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalize. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Summary: How cis -regulatory sequences and their genetic variation govern chromatin accessibility, regulate gene expression, and the establishment of plant cell identities, making them fundamental to development, environmental responses, and phenotypic diversity. Here we present PEAgent, a framework for training, evaluating and interpreting deep-learning models that predict single-cell chromatin accessibility directly from DNA sequence, packaged in an interactive web portal and toolkit. Models were trained on single-cell chromatin-accessibility atlases of soybean, maize and rice, together spanning over 355,000 cells and 320 cell types and ∼150 million years of evolution. We unraveled a lexicon of 243 cell-type-resolved regulatory patterns, half of them composite, with TCP and bHLH showing the greatest influence and strongest conservation across species. Co-occurrence and in silico synergy analyses, explicitly modeling motif orientation and spacing, revealed two distinct cooperative modes acting at short and nucleosome-scale distances. We further showed that model predictions distinguish grass-conserved from rice-specific regulatory sequences far more accurately than sequence conservation scores alone, and validated the model’s predicted variant effects against cell-type-level chromatin accessible QTLs. PEAgent provides a foundational resource for decoding cell-type-specific plant cis -regulatory logic and interpreting noncoding variation in plants. Highlights: PEAgent predicts single-cell chromatin accessibility directly from DNA sequence in soybean, rice and maize. Nearly half of cell-type-resolved patterns are composite, with TCP and bHLH motifs most influential and conserved across species. Co-occurrence and synergy analysis reveals distinct cooperative grammar at short and nucleosome-scale distances. PEAgent distinguishes grass-conserved from lineage-specific regulatory sequences better than sequence conservation scores alone.

Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for exploring cell heterogeneity and lineage dynamics, revealing complex mechanisms driving tissue function and development. However, plant cell’s rigid cell walls require a protoplasing step, an enzymatic process that can introduce biases. The impact of protoplasting on plant scRNA-seq data remains poorly understood. In this study, we analyzed gene expression patterns from bulk RNA-seq of various plant tissues before and after protoplasting. Cell-type specific protoplasting effects were discovered in different plant tissues. Protoplasting-related biases were found to distort clustering, marker gene identification, and multi-omics integration. By calculating protoplasted scores based on gene expression, we identified and mitigated protoplasting effects, enabling the accurate clustering and annotation of tobacco BY-2 cells and identification of cell-cycle-related genes. Our findings underscore the importance of assessing and correcting for protoplasting biases in plant scRNA-seq data analysis, offering new insights for more accurate data interpretation and biological discovery.