Genetically encoded assembly recorder temporally resolves cellular history

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Authors: Yuqing Yan, Jiaxi Lu, Zhe Li, Zuohan Zhao, Timothy F. Shay, Shunzhi Wang, Yaping Lei, Yimei Wang, Wei Chen (23863), Patrick Parker, Hongru Yang, Aileen Qi
+4 more Yongzhi Sun, Dwight E. Bergles, David Baker, Dingchang Lin
Cited by: 4
References: 49

Abstract

Abstract Cells constantly change their molecular state in response to internal and external cues 1 . 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 end point analysis that takes static snapshots 2 or real-time sensing that monitors a small subset of cells 3,4 . 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 NF-κB-mediated transcriptional changes, resolving fast dynamics of 15 min and providing quantifiable signal amplitudes. In a xenograft model, GEMINI records inflammation-induced signalling 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.

Fields of Study & Subjects

Genomics and Chromatin Dynamics Single-cell and spatial transcriptomics Gene Regulatory Network Analysis

Publication details

Journal / source
Nature
Publisher
Springer Science and Business Media LLC
Volume
652
Issue
8111
Pages
1049–1059
Publication date
3 March 2026
Language
EN