Resources: Publications
Scalable Longitudinal Imaging and Transcriptomics of Cells in Dynamic Enclosures
May 5, 2026
BioRxiv
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
Scientific Summary
Cells are constantly changing as they respond to their environment, interact with neighboring cells, and transition between functional states. Many of these biological processes unfold over time, making them difficult to fully understand using approaches that capture only a single molecular or imaging snapshot.
This preprint introduces Cellanome’s approach to pairing longitudinal live-cell imaging with single-cell transcriptomics from the same cells. By enabling researchers to observe cellular behavior over time before measuring gene expression, the platform provides a way to connect what cells do with their underlying molecular state.
The study demonstrates this approach across a range of biological systems and applications, including drug response, immune cell interactions, neuronal biology, CRISPR perturbation screening, and cell differentiation. Throughout these examples, longitudinal phenotypic measurements provided biological context that complemented transcriptomic measurements and revealed cellular behaviors that may not have been apparent from endpoint measurements alone.
Together, these results establish a scalable framework for studying living cells over time while linking functional phenotypes with molecular state from the same cells. The work provides a foundation for investigating dynamic biological processes and exploring how longitudinal, multimodal measurements can expand our understanding of cellular biology.
Key Highlights
- Introduces a scalable workflow for pairing longitudinal live-cell imaging with single-cell transcriptomics from the same cells.
- Demonstrates how observing cellular behavior over time can provide biological context that complements transcriptomic measurements.
- Supports both adherent and suspension cells within the same experimental framework.
- Applies the approach across multiple biological applications, including drug response, immune cell interactions, neuronal biology, CRISPR screening, and cell differentiation.
- Shows how linking cellular behavior with molecular state enables researchers to investigate dynamic biological processes at single-cell resolution.
- Establishes a foundation for generating longitudinal, multimodal datasets for biological discovery.
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Posters & Presentations
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