Department of Bioengineering, 254 Agricultural Engineering, Columbia, MO, USA; Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA.
Department of Bioengineering, 254 Agricultural Engineering, Columbia, MO, USA; Department of Medical Pharmacology and Physiology, 1 Hospital Dr., MO, USA; Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA.
J Neurosci Methods. 2019 Jan 1;311:360-368. doi: 10.1016/j.jneumeth.2018.09.018. Epub 2018 Sep 22.
Quantal exocytosis of oxidizable neurotransmitters can be detected as spikes of amperometric current using electrochemical microelectrodes. Measurements of spike parameters indicate the maximal transmitter flux, flux duration, and amount of transmitter released from individual vesicles. Automated analysis algorithms need to reject spikes that overlap in time. In addition, many spikes are preceded by small amplitude "foot" signals, attributed to slow release of transmitter through a fusion pore. Accurate pre-spike baseline determination is essential for estimating fusion-pore duration and the amount of transmitter released through the fusion pore.
We developed an estimation approach that is based on fitting a multi-exponential function to the data. Our previously described matched-filter algorithm is used to identify the sections of data to fit and provides seed values to facilitate convergence of the iterative fit. The new estimation algorithm includes overlap rejection, a two-step fitting procedure and a novel baseline estimation procedure.
Histograms of spike parameters demonstrate excellent agreement of the new approach with manually computed parameters.
Parameter estimates generated using the new approach are closer to blind manual estimates than commonly used existing methods. The improved performance is due to better detection of valid spikes and rejection of overlapping spikes. Moreover, since the complete time course of the spike is fit to a function, more complete information about the spike time course is captured.
The matched-filter seeded algorithm reliably rejects overlaps and estimates spike and foot signal parameters in a fully automated manner.
使用电化学微电极，可以将可氧化神经递质的量子胞吐作用检测为电流尖峰。尖峰参数的测量表明了单个囊泡释放的最大递质通量、通量持续时间和递质释放量。自动化分析算法需要拒绝在时间上重叠的尖峰。此外，许多尖峰之前都有小幅度的“足”信号，这归因于通过融合孔缓慢释放递质。准确的尖峰前基线确定对于估计融合孔持续时间和通过融合孔释放的递质量至关重要。
我们开发了一种基于拟合多指数函数的数据估计方法。我们之前描述的匹配滤波器算法用于识别要拟合的数据部分，并提供种子值以促进迭代拟合的收敛。新的估计算法包括重叠拒绝、两步拟合过程和新的基线估计过程。
尖峰参数的直方图表明，新方法与手动计算的参数具有极好的一致性。
使用新方法生成的参数估计比常用的现有方法更接近盲手动估计。性能的提高是由于更好地检测有效尖峰并拒绝重叠尖峰。此外，由于完整的尖峰时间过程拟合到一个函数，因此可以捕获更多关于尖峰时间过程的完整信息。
匹配滤波器种子算法可靠地拒绝重叠，并以全自动方式估计尖峰和足信号参数。
