{
  "id": 17832930,
  "title": "SpaceBlocks: an expert-supervised workflow for cohort-level spatial transcriptomics analysis",
  "url": "https://urgent.news/2026/11/02/spaceblocks-an-expert-supervised-workflow-for-cohort-level-spatial",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-02T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.30.763100v1?rss=1"
  },
  "original_language": "en",
  "account": "Spatial transcriptomics (ST) has emerged as a pivotal tool in the realm of tissue biology, with various platforms each offering distinct approaches. However, numerous workflows have yet to address the need for reproducible, scalable, and cohort-level analyses while incorporating expert-supervised decisions to enhance computational and biological provenance. In response to this gap, we introduce SpaceBlocks, a Snakemake workflow designed for reproducible cohort-level analysis of single-cell resolution-ST data, emphasizing expert supervision over complete automation. SpaceBlocks harmonizes diverse platform outputs into a standardized data format, whose structure is rigorously validated to guarantee downstream processing integrity. The workflow incorporates comprehensive expert supervision, including per-sample quality control and cell type annotation diagnostic visualizations, which precede the execution of downstream analyses.\n\nFurthermore, SpaceBlocks accommodates the integration of technology-agnostic, externally formatted data and ensures interoperability with pre-existing tools and technologies. To illustrate the efficacy of SpaceBlocks, we employed it to conduct a reanalysis of 37 samples derived from public Visium HD, Xenium 5K, and MERSCOPE datasets. The utilization of SpaceBlocks facilitated processing scalability across the three platforms, while also uncovering previously overlooked cell types in the Visium HD data and heterogeneous marker gene expression patterns in the MERSCOPE dataset. These findings underscore the critical role of expert-supervised annotations in spatial transcriptomics analysis.\n\nMoreover, SpaceBlocks enabled the identification of differentially expressed genes associated with previously delineated multiple sclerosis (MS) spatial domains in the MERSCOPE dataset, as well as transcriptional distinctions between healthy and malignant colorectal tissues in the Visium HD data. These results collectively validate SpaceBlocks as a versatile and reproducible workflow tailored for expert-supervised spatial transcriptomics analysis across a range of technologies and biological cohorts.",
  "summary": "Spatial transcriptomics (ST) has become a central tool in tissue biology, with multiple technologies addressing the issue from a different angle. Each platform imposes its own raw data format conventions, and few workflows address reproducible, scalable, cohort-level analyses while integrating expert-supervised decisions to strengthen computational and biological provenance. In this work, we…",
  "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."
}