Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.
Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.
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DOI：
10.1113/jp275240
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发表时间：
2018-05-01
期刊：
影响因子：
--
通讯作者：
Prescott SA
中科院分区：
文献类型：
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作者：
Balachandar A;Prescott SA
不同的尖峰模式可能源于离子通道表达的定性差异（即，当不同的神经元表达不同的离子通道时）和/或当表达水平的定量差异定性地改变尖峰产生过程时。我们推测，脊髓浅层背角（SDH）神经元的尖峰模式反映了这两种机制。我们通过改变KV 1-和A-型钾电导的密度来再现SDH神经元尖峰模式。绘制从不同密度组合中出现的尖峰模式，显示由边界（分叉）分隔的尖峰模式区域。该图表明，某些尖峰模式组合发生时，钾通道密度的分布跨越边界，而其他尖峰模式反映不同的离子通道表达模式。前一种机制可以解释为什么某些尖峰模式同时出现在遗传识别的神经元类型中。我们还提出了算法来预测尖峰模式的比例从离子通道密度分布，反之亦然。神经元通常按尖峰模式分类。然而，一些神经元在微妙不同的测试条件下表现出不同的模式，这表明它们在突然的过渡或分叉附近运行。一组这样的神经元可能表现出异质尖峰模式，不是因为它们表达的离子通道的定性差异，而是因为表达水平的定量差异导致神经元在分叉的相对侧上操作。例如，脊髓背角中的神经元对体细胞电流注入的反应模式包括强直性、单一、间隙、延迟和不情愿的尖峰。目前尚不清楚这些模式是否反映了五个细胞群体（由不同的离子通道表达模式定义），单一群体内的异质性，或其某种组合。我们通过改变低阈值（KV 1型）钾电导和失活（A型）钾电导的密度，在计算模型中重现了所有五种尖峰模式，并发现当这些通道密度的联合概率分布跨越两个交叉分叉时，会出现单一，间隙，延迟和不情愿的尖峰，这些分叉将参数空间划分为象限，每个象限与不同的尖峰模式相关。紧张性尖峰可能来自钾通道密度的单独分布。这些结果主张赞成两个细胞群体，一个特征在于紧张尖峰和其他异质尖峰模式。我们提出的算法来预测尖峰模式的比例，离子通道密度分布的基础上，相反，估计离子通道密度分布的尖峰模式的比例。基于尖峰模式的细胞分类的影响进行了讨论。不同的尖峰模式可能源于离子通道表达的定性差异（即，当不同的神经元表达不同的离子通道时）和/或当表达水平的定量差异定性地改变尖峰产生过程时。我们推测，脊髓浅层背角（SDH）神经元的尖峰模式反映了这两种机制。我们通过改变KV 1-和A-型钾电导的密度来再现SDH神经元尖峰模式。绘制从不同密度组合中出现的尖峰模式，显示由边界（分叉）分隔的尖峰模式区域。该图表明，某些尖峰模式组合发生时，钾通道密度的分布跨越边界，而其他尖峰模式反映不同的离子通道表达模式。前一种机制可以解释为什么某些尖峰模式同时出现在遗传识别的神经元类型中。我们还提出了算法来预测尖峰模式的比例从离子通道密度分布，反之亦然。
Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of KV1‐ and A‐type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking‐pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co‐occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low‐threshold (KV1‐type) potassium conductance and an inactivating (A‐type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed. Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of KV1‐ and A‐type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking‐pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co‐occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa.