de la Torre-Ubieta, L. et al. The dynamic landscape of open chromatin during human cortical neurogenesis. Cell 172, 289–304.e18 (2018).
Dixon, J. R. et al. Topological domains in mammalian genomes identified by ***ysis of chromatin interactions. Nature 485, 376–380 (2012).
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Guillotin, B. et al. A pan-grblock transcriptome reveals patterns of cellular divergence in crops. Nature 617, 785–791 (2023).
Liu, Q. et al. Transcriptional landscape of rice roots at the single-cell resolution. Mol. Plant 14, 384–394 (2021).
Sun, Z. M. et al. Single-cell RNA-seq of Lotus japonicus provide insights into identification and function of root cell types of legume. J. Integr. Plant Biol. 65, 1147–1152 (2023).
Feng, D. et al. Chromatin accessibility illuminates single-cell regulatory dynamics of rice root tips. BMC Biol. 20, 274 (2022).
Marand, A. P., Chen, Z. L., Gallavotti, A. & Schmitz, R. J. A cis-regulatory atlas in maize at single-cell resolution. Cell 184, 3041–3055 (2021).
Dorrity, M. W. et al. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat. Commun. 12, 3334 (2021).
Farmer, A., Thibivilliers, S., Ryu, K. H., Schiefelbein, J. & Libault, M. Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant 14, 372–383 (2021).
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).
Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).
Yang, J. H., Han, S. J., Yoon, E. K. & Lee, W. S. Evidence of an auxin signal pathway, microRNA167-ARF8-GH3, and its response to exogenous auxin in cultured rice cells. Nucleic Acids Res. 34, 1892–1899 (2006).
Fu, F. F. & Xue, H. W. Coexpression ***ysis identifies Rice Starch Regulator1, a rice AP2/EREBP family transcription factor, as a novel rice starch biosynthesis regulator. Plant Physiol. 154, 927–938 (2010).
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).
Ma, L. et al. Essential role of sugar transporter OsSWEET11 during the early stage of rice grain filling. Plant Cell Physiol. 58, 863–873 (2017).
Nunes-Nesi, A., Fernie, A. R. & Stitt, M. Metabolic and signaling aspects underpinning the regulation of plant carbon nitrogen interactions. Mol. Plant 3, 973–996 (2010).
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).
Dai, Z. Y. et al. The OsmiR396–OsGRF8–OsF3H–flavonoid pathway mediates resistance to the brown planthopper in rice (Oryza sativa). Plant Biotechnol. J. 17, 1657–1669 (2019).
Liu, K. et al. Superoxide, hydrogen peroxide and hydroxyl radical in D1/D2/cytochrome b-559 photosystem II reaction center complex. Photosynth. Res. 81, 41–47 (2004).
Zhang, H. et al. A genetic module at one locus in rice protects chloroplasts to enhance thermotolerance. Science 376, 1293–1300 (2022).
Huang, X. et al. Genome-wide blockociation studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961–967 (2010).
Wei, X. et al. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat. Genet. 53, 243–253 (2021).
Bian, J. et al. Comparative ***ysis on grain quality and yield of different panicle weight indica–japonica hybrid rice (Oryza sativa L.) cultivars. J. Integr. Agr. 19, 999–1009 (2020).
Ren, D., Ding, C. & Qian, Q. Molecular bases of rice grain size and quality for optimized productivity. Sci. Bull. 68, 314–350 (2023).
Zong, J. et al. A rice single cell transcriptomic atlas defines the developmental trajectories of rice floret and inflorescence meristems. New Phytol. 234, 494–512 (2022).
Zhang, L. et al. Identification of an apoplastic protein involved in the initial phase of salt stress response in rice root by two-dimensional electrophoresis. Plant Physiol. 149, 916–928 (2009).
Paul, P. et al. MADS78 and MADS79 are essential regulators of early seed development in rice. Plant Physiol. 182, 933–948 (2019).
Rodriguez-Leal, D., Lemmon, Z. H., Man, J., Bartlett, M. E. & Lippman, Z. B. Engineering quantitative trait variation for crop improvement by genome editing. Cell 171, 470–480 (2017).
Liang, Z., Brown, R. C., Fletcher, J. C. & Opsahl-Sorteberg, H. G. Calpain-mediated positional information directs cell wall orientation to sustain plant stem cell activity, growth and development. Plant Cell Physiol. 56, 1855–1866 (2015).
Ma, X. et al. A robust CRISPR/Cas9 system for convenient, high-efficiency multiplex genome editing in monocot and dicot plants. Mol. Plant 8, 1274–1284 (2015).
Busch, F. A. Photosynthetic gas exchange in land plants at the leaf level. Methods Mol. Biol. 1770, 25–44 (2018).
Wu, K. et al. Enhanced sustainable green revolution yield via nitrogen-responsive chromatin modulation in rice. Science 367, eaaz2046 (2020).
Triantaphylidès, C. et al. Singlet oxygen is the major reactive oxygen species involved in photooxidative damage to plants. Plant Physiol. 148, 960–968 (2008).
Yu, Q. et al. RNA demethylation increases the yield and biomblock of rice and potato plants in field trials. Nat. Biotechnol. 39, 1581–1588 (2021).
Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. Doubletfinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state ***ysis with Signac. Nat. Methods 18, 1333–1341 (2021).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Jin, J. et al. Planttfdb 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res. 45, D1040–D1045 (2017).
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Yu, G., Wang, L. G. & He, Q. Y. Chipseeker: an R/Bioconductor package for Chip peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
Huang, C. et al. Multi-omics ***ysis for transcriptional regulation of immune-related targets using epigenetic data: a new research direction. Front. Immunol. 12, 741634 (2022).
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
Kleino, I., Frolovaitė, P., Suomi, T. & Elo, L. L. Computational solutions for spatial transcriptomics. Comput. Struct. Biotechnol. J. 20, 4870–4884 (2022).
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Wei, D. dongwei-2023/Rice-root-cell-type-prediction-tool: v1.0 (v1.0). Zenodo (2025).
Wei, D. dongwei-2023/Single_cell_multiomics_in_rice: v1.0 (v1.0). Zenodo (2025).
