{
  "abstract": "Cell-cell communication is essential for tissue homeostasis and disease-associated microenvironment remodeling. Spatial transcriptomics enables the study of communication events in situ, but single-cell-resolution inference from spot-based data remains challenging because of uncertain cell-to-spot mapping, limited use of local spatial information, and false positives from ligand-receptor co-expression. Existing methods increasingly emphasize spatial proximity in cell-cell communication inference, while integrating biologically informative signals remains important for improving the interpretability of candidate communication events. Here, we present SpaHCC, a heterogeneous graph learning framework that integrates single-cell transcriptomics, spatial transcriptomics, and prior ligand-receptor knowledge for spatial cell-cell communication inference. By jointly leveraging molecular features, spatial neighborhood information, and cellular functional states, SpaHCC prioritizes candidate communication events and supports their biological contextualization through pathway, transcription factor, and receptor-side response analyses. Benchmark and spatial consistency analyses showed that SpaHCC recovered spatially coherent candidate communication signals. In Alzheimer’s disease data, SpaHCC identified recurrent excitatory neuron-associated candidate patterns, including NRXN- and FLRT2-related modules associated with synaptic remodeling and inflammation-related transcriptional programs. Cross-patient analyses showed recurrence of selected AD-associated candidate patterns, while representative cSCC and PDAC analyses illustrated the spatial interpretability of the framework in additional disease contexts. Overall, SpaHCC provides an effective and interpretable framework for spatially resolved cell-cell communication analysis in complex diseases. Author summary: Cells in tissues constantly exchange signals, and these interactions help shape both normal tissue function and disease. Recent studies have increasingly used spatial transcriptomics to examine how the positions of cells influence potential communication patterns within tissues. However, spatial proximity alone is often not sufficient to understand whether two cells are likely to communicate in a biologically meaningful way, because being close in space does not necessarily mean that they are truly engaged in meaningful signaling interactions. We developed SpaHCC to combine spatial information with ligand-receptor evidence and cellular state information, so that candidate communication events can be interpreted in a more biologically informed manner. When applied to Alzheimer’s disease data, SpaHCC highlighted candidate communication patterns centered on excitatory neurons, which were associated with synaptic and inflammation-related programs. Our work provides a computational framework for studying not only where cell-cell communication may occur, but also what it may mean biologically in complex diseases.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information and Software Engineering, East China Jiaotong University, Nanchang, Jiangxi, China",
    "College of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, Hunan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiong Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Dongding Wu"
    },
    {
      "name": "Yuejin Zhang"
    },
    {
      "name": "Min Chen"
    },
    {
      "name": "Chong Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0y2wcse57d19q.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014835",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014835",
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  "issns": [
    "1553-7358"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014835",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/DNA-binding proteins/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Regulatory proteins/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cell physiology/Cell communication"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Neurons"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression/Gene regulation/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cellular neuroscience/Neurons"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Data visualization/Infographics/Graphs"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Inflammatory diseases"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Neurodegenerative diseases/Alzheimer's disease"
    ],
    [
      "plos",
      "/Medicine and health sciences/Mental health and psychiatry/Dementia/Alzheimer's disease"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Dementia/Alzheimer's disease"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Neurodegenerative diseases/Alzheimer's disease"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Communications"
    ]
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  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014835&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/0y2wcse57d19q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pcbi.1014835",
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      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014835"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "medicine",
      "origin": "plos:neurology",
      "subfield": "neurology"
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  ],
  "title": "Spatial cell-cell communication inference based on cell-spot-ligand-receptor heterogeneous graphs",
  "title_text": "Spatial cell-cell communication inference based on cell-spot-ligand-receptor heterogeneous graphs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/0y2wcse57d19q",
  "venue": "PLOS Computational Biology",
  "versions": []
}