{
  "id": 10531909,
  "title": "NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics",
  "url": "https://urgent.news/2026/09/28/nexust-a-hierarchical-foundation-model-for-spatial-transcriptomics",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-28T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753590v1?rss=1"
  },
  "original_language": "en",
  "account": "Spatial transcriptomics offers a way to study the molecular states of individual cells within tissue organization. However, existing foundation models struggle to integrate fine-grained gene data with spatial context on a large scale. Researchers have now introduced NexuST, a hierarchical foundation model designed to tackle this challenge.\n\nThe NexuST model operates on a two-level approach, repeatedly combining gene-level molecular modeling with cell-level spatial modeling. These two levels can refine one another during the end-to-end pretraining process. To develop the pretraining data, the team curated a dataset called HumanST-46M, which includes 45.7 million human cells from 72 datasets covering 11 organs and three imaging-based platforms.\n\nWhen evaluated on four held-out datasets totaling about 2.6 million cells, NexuST demonstrated state-of-the-art or competitive performance in various tasks. These tasks include cell-type annotation, region prediction, gene recovery, and neighborhood-composition prediction. The study also revealed that cell-intrinsic expression data remains valuable even for spatial tasks, as evidenced by an expression-only PCA baseline.\n\nNotably, NexuST showed particularly strong improvements in scenarios where spatial context is crucial. By establishing a hierarchical framework, NexuST sets the stage for future spatial transcriptomics foundation models to build upon.",
  "summary": "Spatial transcriptomics captures molecular states within cells and their organisation in tissue. However, integrating fine-grained gene information with spatial context at scale remains challenging for existing foundation models. Here we present NexuST, a hierarchical foundation model that repeatedly interleaves gene-level molecular modelling with cell-level spatial modelling, allowing the two…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}