Genetically encoded assembly recorder temporally resolves cellular history

AI Summary4 min read

TL;DR

GEMINI is a genetically encoded protein assembly that records cellular history with hour-level accuracy, enabling retrospective imaging of dynamic processes like NFκB signaling and neuronal activity in tissues.

Key Takeaways

  • GEMINI uses a designed protein assembly to record cellular events as fluorescent patterns, similar to tree rings, for retrospective analysis.
  • It achieves hour-level chronological accuracy and can resolve fast dynamics down to 15 minutes, such as NFκB-mediated transcriptional changes.
  • The platform works in live cells and intact tissues, including mouse brains, with minimal impact on neuronal function, revealing spatial heterogeneity in signaling.

Tags

Biomaterials – proteinsProtein designSensors and probesScienceHumanities and Social Sciencesmultidisciplinary

Abstract

Cells constantly change their molecular state in response to internal and external cues1. Mapping cellular activity in tissues with spatiotemporal precision is essential for understanding organ physiology, pathology, and regenerative processes. Current cell-sensing modalities primarily rely on either endpoint analysis that takes static snapshots, or real-time sensing that monitors a small subset of cells3,4. Here, we introduce Granularly Expanding Memory for Intracellular Narrative Integration (GEMINI), an in cellulo recording platform that leverages a computationally designed protein assembly as an intracellular memory device to record the history of individual cells. GEMINI grows predictably within live cells, capturing cellular events as tree-ring-like fluorescent patterns for imaging-based retrospective readout. Absolute chronological information of activity histories is attainable with hour-level accuracy. GEMINI effectively maps differential NFκB-mediated transcriptional changes, resolving fast dynamics of 15 minutes and providing quantifiable signal amplitudes. In a xenograft model, GEMINI records inflammation-induced signaling dynamics across tissue, revealing spatial heterogeneity linked to vascular density. When expressed in the mouse brain, GEMINI minimally impacts neuronal functions and can resolve both transcriptional changes and activity patterns of neurons. Together, GEMINI provides a robust and generalizable means for spatiotemporal mapping of cell dynamics underlying physiological and pathological processes in both culture and intact tissues.

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Author information

Author notes
  1. Zhe Li

    Present address: Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China

  2. These authors contributed equally: Yuqing Yan, Jiaxi Lu, Zhe Li

Authors and Affiliations

  1. Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA

    Yuqing Yan, Jiaxi Lu, Zuohan Zhao, Yimei Wang, Hongru Yang, Aileen Qi, Yongzhi Sun & Dingchang Lin

  2. Institute for NanoBiotechnology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA

    Yuqing Yan, Jiaxi Lu, Zuohan Zhao, Yimei Wang, Hongru Yang, Yongzhi Sun & Dingchang Lin

  3. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA

    Yuqing Yan, Dwight E. Bergles & Dingchang Lin

  4. Institute for Protein Design, University of Washington, Seattle, WA, USA

    Zhe Li, Shunzhi Wang & David Baker

  5. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Timothy F. Shay & Yaping Lei

  6. Solomon H. Snyder Department of Neuroscience, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Wei Chen, Patrick Parker & Dwight E. Bergles

  7. Center for Cell Dynamics, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Dingchang Lin

Authors
  1. Yuqing Yan
  2. Jiaxi Lu
  3. Zhe Li
  4. Zuohan Zhao
  5. Timothy F. Shay
  6. Shunzhi Wang
  7. Yaping Lei
  8. Yimei Wang
  9. Wei Chen
  10. Patrick Parker
  11. Hongru Yang
  12. Aileen Qi
  13. Yongzhi Sun

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