{
  "abstract": "Glioblastoma (GBM) exhibits marked spatial heterogeneity that is lost after tissue dissociation for single-cell RNA sequencing. Here, we developed a spatial-statistical framework to characterize tumor-microenvironment organization and derive molecularly predictable spatial phenotypes from CosMx data. Eight GBM specimens comprising 2,427,362 cells, 15 cell populations, and four malignant states were analyzed, revealing substantial inter-patient differences in cellular composition and density. Fine finite-element meshes best preserved continuous spatial intensity and supported whole-tissue Log-Gaussian Cox Process modelling. Immune populations, including macrophages, microglia, monocytes, cDCs, neutrophils, and T cells, showed spatial attraction toward malignant states. These models generated cell-level measures of conditional intensity and tumor-proximity probability. Transcript abundances and metabolic pathway scores served as the sole predictive features to model these spatial phenotypes. Transcriptomic representations outperformed metabolic pathway scores. Leave-one-patient-out validation reduced predictive performance, and patient-wise harmonization failed to restore cross-patient transportability. Spatial outcome distributions varied between specimens, indicating that molecular-spatial relationships are strongly conditioned by patient-specific tumor architecture. Predicted interaction scores stratified transcriptional subclusters within cell types in external non-spatial single-cell datasets. This framework links spatial point-process phenotypes to molecular states and provides a basis for inferring spatial context from non-spatial single-cell data. Generalizing these models requires larger and more diverse spatial cohorts.",
  "abstract_shown": true,
  "affiliations": [
    "Ribeirão Preto Medical School - University of São Paulo"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "F. P. Carcanholo"
    },
    {
      "name": "M. H. A. Cassiano"
    },
    {
      "name": "E. M. Panepucci"
    },
    {
      "name": "M. P. Laurini"
    },
    {
      "name": "T. M. Malta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/53d6e7fc5z0nf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.02.756019",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756019v1.full",
  "id": "53d6e7fc5z0nf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756019v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221344318",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756019v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/53d6e7fc5z0nf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:59Z",
      "native_id": "10.64898/2026.10.02.756019",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756019v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq",
  "title_text": "Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:59Z",
  "url": "https://research.draconitic.com/paper/53d6e7fc5z0nf",
  "venue": "bioRxiv",
  "versions": []
}