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<p>High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the study of cellular morphology and transcriptomics, dynamic cellular processes remain challenging to phenotypically profile. To address this, we developed Plexus, a self-supervised model designed to capture and quantify network-level neuronal activity. Unlike existing tools that focus on static readouts, Plexus leverages a network-level cell encoding method, efficiently encoding dynamic neuronal activity into rich representational embeddings. In turn, Plexus achieves state-of-the-art performance in detecting phenotypic changes in neuronal activity. Here we validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its ability to classify distinct phenotypes compared with traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system using human induced pluripotent stem cell-derived neurons expressing the GCaMP6m calcium indicator and CRISPR interference machinery. This platform successfully identified nearly 17 times as many phenotypic changes in response to genetic perturbations compared with conventional methods, as demonstrated in a 52-gene CRISPR interference screen across multiple induced pluripotent stem cell lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders.</p>

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Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Parker Grosjean,
  • Kaivalya Shevade,
  • Cuong Nguyen,
  • Sarah Ancheta,
  • Karl Mader,
  • Ivan Franco,
  • Seok-Jin Heo,
  • Greyson Lewis,
  • Dehua Zhao,
  • Bhairavi Tolani,
  • Steven Boggess,
  • Angelique Di Domenico,
  • Erik Ullian,
  • Shawn Shafer,
  • Adam Litterman,
  • Laralynne Przybyla,
  • Michael J. Keiser,
  • Jamie Ifkovits,
  • Adam Yala,
  • Martin Kampmann

摘要

High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the study of cellular morphology and transcriptomics, dynamic cellular processes remain challenging to phenotypically profile. To address this, we developed Plexus, a self-supervised model designed to capture and quantify network-level neuronal activity. Unlike existing tools that focus on static readouts, Plexus leverages a network-level cell encoding method, efficiently encoding dynamic neuronal activity into rich representational embeddings. In turn, Plexus achieves state-of-the-art performance in detecting phenotypic changes in neuronal activity. Here we validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its ability to classify distinct phenotypes compared with traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system using human induced pluripotent stem cell-derived neurons expressing the GCaMP6m calcium indicator and CRISPR interference machinery. This platform successfully identified nearly 17 times as many phenotypic changes in response to genetic perturbations compared with conventional methods, as demonstrated in a 52-gene CRISPR interference screen across multiple induced pluripotent stem cell lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders.