Visual barcodes for clonal-multiplexing of live microscopy-based assays

Tom Kaufman, Erez Nitzan, Nir Firestein, Miriam Bracha Ginzberg, Seshu Iyengar, Nish Patel, Rotem Ben-Hamo, Ziv Porat, Jaryd Hunter, Andreas Hilfinger, Varda Rotter, Ran Kafri () and Ravid Straussman ()
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Tom Kaufman: Weizmann Institute of Science
Erez Nitzan: Weizmann Institute of Science
Nir Firestein: Weizmann Institute of Science
Miriam Bracha Ginzberg: The Hospital for Sick Children
Seshu Iyengar: University of Toronto
Nish Patel: The Hospital for Sick Children
Rotem Ben-Hamo: Weizmann Institute of Science
Ziv Porat: Weizmann Institute of Science
Jaryd Hunter: The Hospital for Sick Children
Andreas Hilfinger: University of Toronto
Varda Rotter: Weizmann Institute of Science
Ran Kafri: The Hospital for Sick Children
Ravid Straussman: Weizmann Institute of Science

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation, we develop visual barcodes that discriminate the clonal identity of single cells by different fluorescent proteins that are targeted to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then use visual barcodes to generate ‘Signalome’ cell-lines by mixing 12 clones of different live reporters into a single population, allowing simultaneous monitoring of the activity in 12 branches of signaling, at clonal resolution, over time. Using the ‘Signalome’ we identify two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological systems.

Date: 2022
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DOI: 10.1038/s41467-022-30008-0

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