Gene expression and chromatin accessibility provide complementary insights into the regulatory mechanisms that define cell states. Although methods for jointly profiling these modalities exist, plant applications remain limited because of complex workflows, inconsistent performance, and prohibitive costs. Here, we present easy-Multiome, a streamlined single-cell multiomic workflow that integrates a single in situ reverse transcription step into the standard droplet-based scATAC-seq protocol. Using easy-Multiome, we profiled more than 20,000 nuclei from maize seedlings generating paired gene expression and chromatin accessibility data, with approximately 90% of nuclei containing both high-quality RNA and chromatin accessibility profiles. The resulting transcriptome data resolved 16 clusters corresponding to nine major maize seedling cell types and enabled direct characterization of cell-type-specific chromatin accessibility from the same nuclei. Furthermore, easy-Multiome simultaneously captured cell-type-specific transcription factor expression and the accessibility of their cognate DNA-binding motifs, providing direct links between transcriptional programs and regulatory landscapes. Together, these results demonstrate that easy-Multiome enables robust and efficient joint profiling of plant gene expression and chromatin accessibility while requiring only minimal modifications to existing droplet-based scATAC-seq workflows.
Xuan Zhang, M. Minow, Robert J. Schmitz· bioRxiv· 0 citations
Gene expression is governed by regulatory DNA and their associated trans factors acting in specific cell types, yet the sequences underlying this control remain poorly mapped in plants. Genome-pretrained DNA language models provide a route to interrogate regulatory sequence directly, but their attributions have largely been interpreted using bulk or whole-tissue data, and standard attribution pipelines can preferentially highlight sequences downstream of the transcription start (TSS) site rather than promoter-associated signals. Here, we train a celltype-resolved sequence-to-expression model from a single-cell soybean (Glycine max) atlas by coupling a soybean-adapted Genomic Pre-trained Network (GPN) to a shared sequence encoder with 66 cell-type-specific output heads. Across 38,339 protein-coding genes, the model achieves a mean per-cell-type, across-gene Pearson correlation of 0.683 and, recast as a highversus-low expression classification, reaches an area under the ROC curve of 0.92 to 0.97 across tissues, at or above dedicated plant sequence models. We then introduce ContextAware Significance of Cross-gene Attribution for Discovering Elements (CASCADE), a positionspecific statistical framework for identifying model-derived candidate regulatory elements from in silico saturation mutagenesis. Relative to the pooled null used by TF-MoDISco, CASCADE shifts motif recovery from downstream of the transcription start site toward promoter sequence, with 77% of CASCADE-exclusive motifs, compared with 12% of TF-MoDISco-exclusive motifs, falling within the promoter. Applied across the atlas, CASCADE identifies approximately 1.39 million candidate elements spanning broadly active, tissue-restricted and cell-type-restricted classes. Together, these analyses establish a position-aware approach for extracting promoterassociated regulatory hypotheses from sequence models and generate a cell-type-resolved map of candidate cis-regulatory elements.
Ali Farghadan, Robert J. Schmitz, Scott A. Jackson et al.· bioRxiv· 0 citations
