{
  "id": 18027153,
  "title": "CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging",
  "url": "https://urgent.news/2026/11/03/cardioshift-resolving-cell-to-cell-functional-heterogeneity-in-hipsc",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763029v1?rss=1"
  },
  "original_language": "en",
  "account": "High-content screening of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is commonly employed for disease modeling. However, standard pipelines utilize whole-well averages, which fail to capture cell-to-cell heterogeneity. Transitioning from well-averages to single-cell analysis reveals these hidden variations, but may inadvertently introduce pseudoreplication bias if cells are treated as independent data points.\n\nThe researchers have developed CardioShift, an open-source bioimage informatics framework, to map single-cell distributions while properly accounting for the nested variance structure. By applying this framework to 32,818 Ca2+ transients and 10,719 contractility-lipid profiles, the team discovered that cell-to-cell variability is the primary source of variance for most functional parameters. This finding demonstrates how traditional well averages can obscure condition-dependent phenotypes.\n\nMoreover, unsupervised clustering using CardioShift identified condition-dependent shifts in functional subpopulations. One notable discovery was a distinct cellular state that accumulates lipids but maintains baseline contraction kinetics. This specific cellular state was not detected using either well- or single-cell averaging approaches. In summary, CardioShift offers a robust analytical approach to uncover hidden single-cell distributions, thereby enhancing our understanding of cellular heterogeneity in hiPSC-CM models.",
  "summary": "High-content screening of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is widely used for disease modeling, yet standard pipelines rely on whole-well averages that obscure cell-to-cell heterogeneity. Moving from well-averages to single-cell analysis reveals hidden variations, but may introduce a pseudoreplication bias if cells are analysed as independent data points. 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."
}