Abstract
Active, responsive, non-equilibrium materials–at the forefront of materials engineering–offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE: Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer, an open-access software that automates high throughput screening of microscopy video data to enable non-equilibrium material optimization and discovery. BARCODE produces a unique fingerprint or ‘barcode’ of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material-agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate BARCODE’s abilities to accelerate and streamline screening and analysis, reveal unexpected correlations and emergence, and enable broad non-expert data access, comparison, and sharing.
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Data availability
The source data for all plots shown in Figs. 2–5 have been deposited in the Dryad repository and can be accessed at: https://doi.org/10.5061/dryad.pc866t235. Source data are provided with this paper.
Code availability
BARCODE and supporting documentation are available on the GitHub repository58 at https://github.com/softmatterdb/barcode and can be accessed at: https://doi.org/10.5281/zenodo.17585069.
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Acknowledgements
BARCODE was developed through Hackathon events held at University of California, Santa Barbara and supported by the US National Science Foundation (NSF) Designing Materials to Revolutionize and Engineer our Future (DMREF) program with contributions from the following participants: Jonathan Michel, Mengyang Gu, Prashali Chauhan, Laura Morocho, Nimisha Krishnan, Anindya Chowdhury, Lauren Melcher, JJ Siu, Gregor Leech, Mehrzad Sasanpour, and Karthik Peddireddy. We acknowledge funding from the US National Science Foundation DMREF program through following grants: NSF DMR-2119663 (to RMRA), NSF DMR-2118403 (to JLR), NSF DMR-2118449 (to MD), NSF DMR-2118497 (to MTV), Research Corporation for Science Advancement award no. CS-PBP-2023-019 (to RMRA, MH), Arnold and Mabel Beckman Foundation Beckman Scholars Program (to RMRA, KM) and the NSF BioPACIFIC Materials Innovation Platform NSF DMR-1933487 for personnel support (QC) and access to research infrastructure. We thank Christopher Dunham, BioPACIFIC MIP, Emmie Kao and Christopher Tao for helpful discussions and preliminary research development, and Eric Feng for assistance in updating the graphical user interface. We thank Jose Alvarado, Gijsje Koenderink, and Yimin Luo for providing data10,34 and for helpful discussions. We thank Roberto Cerbino and Jasmin Di Franco for providing unpublished data and for helpful discussions.
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M.T.V. and R.M.R.A. conceived and designed the research. Q.C., A.S., A.D., K.M., M.H., K.T., R.J.M., R.M.R.A. and M.T.V. designed BARCODE algorithms, pipeline, and documentation. K.M. and M.H. performed experimental work and curated data. Q.C., A.S., A.D., R.M.R.A. and M.T.V. analyzed data. Q.C., A.S., R.M.R.A., and M.T.V. prepared figures and wrote the manuscript. M.D., R.J.M., R.M.R.A., and M.T.V. supervised the research. All authors interpreted data and edited the manuscript.
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Chen, Q., Sriram, A., Das, A. et al. BARCODE: high throughput screening and analysis of soft active materials. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67963-3
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DOI: https://doi.org/10.1038/s41467-025-67963-3
