{
  "abstract": "Single-cell transcriptomics has resolved cell-type-specific gene expression in plants, yet maize still lacks an integrated reference and species-specific foundation models. We present scMaize, combining scMaizeAtlas, an integrated atlas of 385,675 cells from 20 projects and 66 samples across seven tissues with hierarchical annotation, with two Transformer-based foundation models pretrained on this atlas. scMaizeExp serves as an expression-only baseline, while scMaizeGO incorporates Gene Ontology (GO) functional embeddings as an inductive bias. Although global expression-prediction accuracy was comparable, the GO prior improved rank-order prediction, strengthened attention toward functionally coherent gene modules, and enhanced embedding topology, with scMaizeGO achieving 86.0% cell-type and 97.1% tissue classification accuracy. Zero-shot evaluation demonstrated the cross-species generalizability of scMaizeGO representations, and few-shot fine-tuning enabled accurate cross-species classification with minimal labeled data. Root perturbation-condition analysis showed that the model encoded treatment-specific cellular states beyond cell-type identity, with the GO prior amplifying perturbation signals approximately threefold. Expression projection identified condition-responsive genes enriched for known stress pathways, and attention analysis revealed predominantly condition-specific changes in gene-gene attention that were weakly associated with expression-projection changes. An online platform (https://www.scmaize.com) provides atlas exploration, model access, and zero-code analysis tools. scMaize establishes a framework demonstrating that species-specific pretraining with functional priors enables transferable, perturbation-aware representations for crop single-cell genomics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Q. Cheng"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "H. T. Wu"
    },
    {
      "name": "A. Zhao"
    },
    {
      "name": "Q. M. Shang"
    },
    {
      "name": "F. X. Wang"
    },
    {
      "name": "J. Yan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3pzxe69mxe99f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.01.742180",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742180v2.full",
  "id": "3pzxe69mxe99f",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742180v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "plant biology"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7197026596",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742180v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3pzxe69mxe99f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:56Z",
      "native_id": "10.64898/2026.08.01.742180",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742180v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:plant biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "scMaize: A Single-Cell Foundation Model and Integrated Atlas for Maize",
  "title_text": "scMaize: A Single-Cell Foundation Model and Integrated Atlas for Maize",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:56Z",
  "url": "https://research.draconitic.com/paper/3pzxe69mxe99f",
  "venue": "bioRxiv",
  "versions": []
}