Deciphering cell signaling networks with massively multiplexed biosensor barcoding

Jr Ming Yang, Wei Yu Chi, Jessica Liang, Saki Takayanagi, Pablo A. Iglesias, Chuan Hsiang Huang

Research output: Contribution to journalArticlepeer-review


Genetically encoded fluorescent biosensors are powerful tools for monitoring biochemical activities in live cells, but their multiplexing capacity is limited by the available spectral space. We overcome this problem by developing a set of barcoding proteins that can generate over 100 barcodes and are spectrally separable from commonly used biosensors. Mixtures of barcoded cells expressing different biosensors are simultaneously imaged and analyzed by deep learning models to achieve massively multiplexed tracking of signaling events. Importantly, different biosensors in cell mixtures show highly coordinated activities, thus facilitating the delineation of their temporal relationship. Simultaneous tracking of multiple biosensors in the receptor tyrosine kinase signaling network reveals distinct mechanisms of effector adaptation, cell autonomous and non-autonomous effects of KRAS mutations, as well as complex interactions in the network. Biosensor barcoding presents a scalable method to expand multiplexing capabilities for deciphering the complexity of signaling networks and their interactions between cells.

Original languageEnglish (US)
Pages (from-to)6193-6206.e14
Issue number25
StatePublished - Dec 9 2021


  • KRAS
  • adaptation
  • barcode
  • cell non-autonomous effect
  • fluorescent biosensor
  • live cell imaging
  • machine learning
  • multiplexing
  • receptor tyrosine kinase
  • signaling network

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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