Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures
Year: 2026
Type: preprint
Abstract: Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.
Authors: Tarun K Khurana, Liz Y Wu, Pier Federico Gherardini, Seyedsina Moeinzadeh, Mehdi Mohseni, Filiz Gorpe Yasar, Roger Dettloff, Justin Poelma, Shan Sabri, Annarita Scaramozza, Yunmin Li, Nirvan Rouzbeh, Nicholas Elder, Shreya Deshmukh, Alireza Majd, Ria Gupta, Sina Farahvashi, Isaac Thomas, Craig Betts, Frank Charbonier, David Roberts, Pei-Lin Hsiung, Eloise Zargari-Pariset, Richard Yau, Olaia F Vila, Molly Phillips, Carmen Xiao, Jie Wang, Yiqi Zhou, Punam Adhikari, Meng Taing, Elaheh Farjami, Behnam Javanmardi, Merek Siu, The Cellanome Development Team, Camilla Valente, Chris Cox, Kathryn Geiger-Schuller, Shannon J Turley, Orit Rozenblatt-Rosen, Faranak Fattahi, Joseph R Ecker, Jeffrey R Jones, Fred H Gage, Matthew H Spitzer, Jasmine Pritchard, Ilya Kupershmidt, Peter Lundberg, Shawn Levy, Omead Ostadan, Gary P Schroth, Mostafa Ronaghi
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7160706739