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Related Concept Videos

Microbial Biosensors01:17

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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications
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Deciphering cell signaling networks with massively multiplexed biosensor barcoding.

Jr-Ming Yang1, Wei-Yu Chi1, Jessica Liang2

  • 1Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21205, USA.

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|November 28, 2021
PubMed
Summary
This summary is machine-generated.

We developed a novel biosensor barcoding system to track over 100 signaling events simultaneously in live cells. This method overcomes spectral limitations, enabling deep analysis of complex cellular communication and signaling networks.

Keywords:
KRASadaptationbarcodecell non-autonomous effectfluorescent biosensorlive cell imagingmachine learningmultiplexingreceptor tyrosine kinasesignaling network

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Area of Science:

  • Cellular and Molecular Biology
  • Systems Biology
  • Biotechnology

Background:

  • Genetically encoded fluorescent biosensors are crucial for live-cell imaging of biochemical activities.
  • Current biosensor multiplexing is limited by spectral overlap, hindering simultaneous tracking of multiple signaling events.

Purpose of the Study:

  • To develop a scalable method for massively multiplexed biosensing in live cells.
  • To overcome spectral limitations in fluorescent biosensor applications.
  • To enable deep analysis of complex cellular signaling networks.

Main Methods:

  • Development of a barcoding protein system generating over 100 spectrally separable barcodes.
  • Simultaneous imaging of barcoded cells expressing different biosensors.
  • Application of deep learning models for analyzing multiplexed signaling events.

Main Results:

  • Demonstrated generation of over 100 spectrally separable barcodes.
  • Achieved massively multiplexed tracking of signaling events in cell mixtures.
  • Revealed coordinated activities and temporal relationships between different biosensors.
  • Uncovered distinct mechanisms in receptor tyrosine kinase signaling, including KRAS mutation effects.

Conclusions:

  • Biosensor barcoding significantly expands multiplexing capabilities for live-cell studies.
  • This scalable method facilitates deciphering complex signaling networks and intercellular interactions.
  • Enables advanced research into cellular communication and disease mechanisms.