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This study introduces a new method to accurately measure chemical messenger concentrations in cells by accounting for sensor binding kinetics. This approach improves biological signaling analysis and reveals distinct cellular events.

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Area of Science:

  • Cellular biology
  • Biophysics
  • Computational imaging

Background:

  • Designing fluorescent sensors is crucial for studying biological signaling.
  • Sensor binding kinetics are often overlooked, leading to interpretation artifacts in fluorescence measurements.
  • Accurate reconstruction of chemical messenger concentrations is essential for understanding cellular processes.

Purpose of the Study:

  • To develop a method for reconstructing spatiotemporal concentrations of chemical messengers, considering sensor binding kinetics.
  • To improve the interpretation of fluorescence measurements from biological sensors.
  • To validate the method using GCaMP calcium sensors and analyze calcium distribution in neurons.

Main Methods:

  • Developed a method to fit fluorescence data constrained by chemical reactions.
  • Integrated a deep neural network prior to enhance data fitting.
  • Applied the method to GCaMP calcium sensors to recover messenger concentrations.
  • Analyzed the spatiotemporal distribution of calcium in single neurons.

Main Results:

  • Recovered concentrations showed a common temporal waveform irrespective of sensor kinetics.
  • Ignoring binding kinetics and assuming equilibrium introduced artifacts in concentration measurements.
  • The method successfully revealed distinct spatiotemporal calcium events in single neurons.
  • The computational approach improved the accuracy of biological signaling studies.

Conclusions:

  • Incorporating physical constraints, specifically binding kinetics, is vital for accurate computational imaging of cellular signals.
  • The proposed method enhances the utility of current chemical sensors.
  • This work provides a more robust approach to analyzing biological signaling dynamics.