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<p>Formation of the NLRP3 inflammasome occurs in response to a wide range of triggers and leads to pyroptosis and release of IL-1β and IL-18. This has widely been considered an “all or nothing” response leading to similar cellular outcomes each time, once the inflammasome has formed, a view that has been challenged over the years. This assumption has largely been based on endpoint measurements at the population level, producing metrics that fail to adequately capture dynamic responses. Herein, we utilize live-cell imaging combined with an algorithmic approach to track individual ASC-GFP specks over time, formed in response to the triggers ATP, monosodium urate and nigericin. Using this approach, we report trigger-dependent differences in speck lifetime. The use of the proposed algorithm requires a relatively small dataset, while still providing insights into NLRP3 inflammasome dynamics downstream of inflammasome activation at the single-cell level.</p>

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Time-lapse image analysis reveals trigger-dependent differences in ASC speck lifetime in the NLRP3 inflammasome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • M. Herring,
  • A. Persson,
  • R. Karlsson,
  • D. Repsilber,
  • M. Ejdebäck,
  • E. Särndahl,
  • N. Nikaein,
  • O. Kotlyar

摘要

Formation of the NLRP3 inflammasome occurs in response to a wide range of triggers and leads to pyroptosis and release of IL-1β and IL-18. This has widely been considered an “all or nothing” response leading to similar cellular outcomes each time, once the inflammasome has formed, a view that has been challenged over the years. This assumption has largely been based on endpoint measurements at the population level, producing metrics that fail to adequately capture dynamic responses. Herein, we utilize live-cell imaging combined with an algorithmic approach to track individual ASC-GFP specks over time, formed in response to the triggers ATP, monosodium urate and nigericin. Using this approach, we report trigger-dependent differences in speck lifetime. The use of the proposed algorithm requires a relatively small dataset, while still providing insights into NLRP3 inflammasome dynamics downstream of inflammasome activation at the single-cell level.