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Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis
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Estimating amperometric spike parameters resulting from quantal exocytosis using curve fitting seeded by a

Supriya Balaji Ramachandran1, Kevin D Gillis2

  • 1Department of Bioengineering, 254 Agricultural Engineering, Columbia, MO, USA; Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA.

Journal of Neuroscience Methods
|September 26, 2018
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Summary

This study introduces a new automated algorithm for analyzing neurotransmitter release. The method accurately estimates spike and foot signal parameters, improving upon existing techniques for electrochemical measurements.

Keywords:
Carbon-fiber amperometryExocytosisQuantalSignal processingSpike detection

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Area of Science:

  • Neuroscience
  • Electrochemistry
  • Biophysics

Background:

  • Quantal exocytosis releases oxidizable neurotransmitters, detectable as amperometric current spikes.
  • Spike parameters (flux, duration, amount) inform vesicle release dynamics.
  • Overlapping spikes and pre-spike foot signals complicate accurate analysis.

Purpose of the Study:

  • To develop an automated algorithm for analyzing amperometric current spikes from neurotransmitter exocytosis.
  • To accurately estimate spike and foot signal parameters, including fusion-pore characteristics.
  • To improve upon existing methods for analyzing overlapping spikes and baseline determination.

Main Methods:

  • Developed a novel estimation approach using multi-exponential function fitting.
  • Employed a matched-filter algorithm for data section identification and seed value generation.
  • Integrated overlap rejection, a two-step fitting procedure, and a new baseline estimation.

Main Results:

  • Histograms showed excellent agreement between the new approach and manual parameter computations.
  • Parameter estimates were closer to blind manual estimates than existing methods.
  • Improved performance attributed to better spike detection and overlap rejection.

Conclusions:

  • The matched-filter seeded algorithm automates the rejection of overlapping spikes.
  • It reliably estimates both spike and foot signal parameters.
  • This method captures more complete information about the spike time course.