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<p>Understanding how cells respond differently to perturbation is crucial in cell biology, but existing methods often fail to accurately quantify and interpret heterogeneous single-cell responses. Here we introduce the perturbation-response score (PS), a method to quantify diverse perturbation responses at a single-cell level. Applied to single-cell perturbation datasets such as Perturb-seq, PS outperforms existing methods in quantifying partial gene perturbations. PS further enables single-cell dosage analysis without needing to titrate perturbations, and identifies ‘buffered’ and ‘sensitive’ response patterns of essential genes, depending on whether their moderate perturbations lead to strong downstream effects. PS reveals differential cellular responses on perturbing key genes in contexts such as T cell stimulation, latent HIV-1 expression and pancreatic differentiation. Notably, we identified a previously unknown role for the coiled-coil domain containing 6 (<i>CCDC6</i>) in regulating liver and pancreatic cell fate decisions. PS provides a powerful method for dose-to-function analysis, offering deeper insights from single-cell perturbation data.</p>

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Decoding heterogeneous single-cell perturbation responses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月26日

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  • Bicna Song,
  • Dingyu Liu,
  • Weiwei Dai,
  • Natalie F. McMyn,
  • Qingyang Wang,
  • Dapeng Yang,
  • Adam Krejci,
  • Anatoly Vasilyev,
  • Nicole Untermoser,
  • Anke Loregger,
  • Dongyuan Song,
  • Breanna Williams,
  • Bess Rosen,
  • Xiaolong Cheng,
  • Lumen Chao,
  • Hanuman T. Kale,
  • Hao Zhang,
  • Yarui Diao,
  • Tilmann Bürckstümmer,
  • Janet D. Siliciano,
  • Jingyi Jessica Li,
  • Robert F. Siliciano,
  • Danwei Huangfu,
  • Wei Li

摘要

Understanding how cells respond differently to perturbation is crucial in cell biology, but existing methods often fail to accurately quantify and interpret heterogeneous single-cell responses. Here we introduce the perturbation-response score (PS), a method to quantify diverse perturbation responses at a single-cell level. Applied to single-cell perturbation datasets such as Perturb-seq, PS outperforms existing methods in quantifying partial gene perturbations. PS further enables single-cell dosage analysis without needing to titrate perturbations, and identifies ‘buffered’ and ‘sensitive’ response patterns of essential genes, depending on whether their moderate perturbations lead to strong downstream effects. PS reveals differential cellular responses on perturbing key genes in contexts such as T cell stimulation, latent HIV-1 expression and pancreatic differentiation. Notably, we identified a previously unknown role for the coiled-coil domain containing 6 (CCDC6) in regulating liver and pancreatic cell fate decisions. PS provides a powerful method for dose-to-function analysis, offering deeper insights from single-cell perturbation data.