{
  "id": 5463566,
  "title": "Kintsugi maps nucleus-poor RNA compartments in subcellular spatial transcriptomics",
  "url": "https://urgent.news/2026/09/03/kintsugi-maps-nucleus-poor-rna-compartments-in-subcellular-spatial",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748061v1?rss=1"
  },
  "original_language": "en",
  "account": "Spatial transcriptomics enables the resolution of RNA within tissue structures that extend beyond nuclei. However, existing aggregation methods either impose fixed regions or use cells as anchors, making it challenging to directly measure process-rich and extracellular compartments. To address this, researchers have developed Kintsugi, a deterministic tessellation method that partitions sparse count matrices based on captured UMI density and assigns every bin within the tissue. By separating Pearson-residual gene composition from captured transcript density, Kintsugi preserves complete tissue representation without the need for histology or nuclear segmentation.\n\nIn mouse brain Visium HD data, Kintsugi successfully identified nucleus-poor neuropil compartments enriched with glial and dendritic transcripts. Additionally, Xenium molecule coordinates supported the localization of dendritic mRNA away from nuclei. The same tissue representation also identified nucleus-poor fibrotic scar regions in idiopathic pulmonary fibrosis and matrix structure in fetal cartilage. Furthermore, CODEX proteomics revealed that the captured density of RNA was not reducible to nuclear packing alone.\n\nThese findings demonstrate that nucleus-poor tissue spaces contain structured and interpretable RNA biology. Through complete tissue tessellation using Kintsugi, researchers can now access and study the RNA biology present in these previously challenging compartments.",
  "summary": "Subcellular spatial transcriptomics resolves RNA where tissue structures extend beyond nuclei, but current aggregation strategies either impose fixed areal units or use segmented cells as anchors. This leaves process-rich and extracellular compartments difficult to measure directly. Here we report Kintsugi, a deterministic tessellation method that partitions sparse count matrices by captured-UMI…",
  "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."
}