{
  "abstract": "Cell Painting is a popular high-content imaging assay that captures the shape and internal organization of 2D cell cultures at scale. While 3D cell cultures are more physiologically relevant models – especially for studying drug perturbations – they remain challenging to image because light scatters before it reaches the cultures’ interior. Here, we introduce a scalable method to adapt Cell Painting for 3D spheroids. We show that tissue-clearing, a technique that makes spheroids transparent, combined with a focusing routine that locates spheroids on the fly, enables Cell Painting at single-cell level inside intact spheroids. By profiling 52 clinically relevant drugs, we show that single-cell fingerprints clustered by biological pathway and resolved drug effects more robustly than those of flattened spheroid-wide views. Notably, the effect of two drugs, 5-fluorouracil and olaparib, nearly indistinguishable in 2D monolayers, diverged in spheroids. This finding was then corroborated by DNA-damage, cell-cycle, and gene-expression assays. The workflow runs on a spinning-disk confocal microscope in a standard 384-well plate format, making single-cell 3D profiling accessible to laboratories already performing Cell Painting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research support, non u.s. gov't",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Christa Ringers",
      "orcid": "0000-0002-0807-8481"
    },
    {
      "name": "David Holmberg"
    },
    {
      "name": "Anders Larsson"
    },
    {
      "name": "Bomi Jung"
    },
    {
      "name": "Malin Jarvius",
      "orcid": "0000-0002-1565-6155"
    },
    {
      "name": "Anna Luchniak"
    },
    {
      "name": "Dan Rosén"
    },
    {
      "name": "Martin Johansson"
    },
    {
      "name": "Polina Georgiev"
    },
    {
      "name": "Brinton Seashore-Ludlow",
      "orcid": "0000-0001-8658-5967"
    },
    {
      "name": "Torkild Visnes",
      "orcid": "0000-0003-1047-988X"
    },
    {
      "name": "Åsmund Flobak",
      "orcid": "0000-0002-3357-425X"
    },
    {
      "name": "Jordi Carreras-Puigvert"
    },
    {
      "name": "Ola Spjuth",
      "orcid": "0000-0002-8083-2864"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3qcxj02abp90k.bib",
  "cited_by": 0,
  "doi": "10.1038/s41467-026-78596-5",
  "fulltext_url": "https://europepmc.org/articles/PMC13686983",
  "id": "3qcxj02abp90k",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://www.nature.com/articles/s41467-026-78596-5",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7223114925",
  "pdf_url": "https://www.nature.com/articles/s41467-026-78596-5.pdf",
  "pmcid": "PMC13686983",
  "pmid": "42907469",
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3qcxj02abp90k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "10.1038/s41467-026-78596-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41467-026-78596-5"
    },
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42907469",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42907469"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Cell Image Analysis Techniques",
      "subfield": "biophysics"
    }
  ],
  "title": "High-content morphological profiling by Cell Painting in 3D spheroids",
  "title_text": "High-content morphological profiling by Cell Painting in 3D spheroids",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/3qcxj02abp90k",
  "venue": "Nature Communications",
  "versions": []
}