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相关概念视频

Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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Excitatory and Inhibitory Effects of Neurotransmitters01:29

Excitatory and Inhibitory Effects of Neurotransmitters

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When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
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Synaptic Signaling01:12

Synaptic Signaling

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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Synaptic Signaling01:09

Synaptic Signaling

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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
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The Synapse02:47

The Synapse

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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Updated: Jan 10, 2026

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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突触通信中的干扰的细胞类型特定推断与多神经元ChatChat.

Gianluca Volkmer1, Jens Hjerling-Leffler1, Lisa Bast1

  • 1Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17177 Stockholm, Sweden.

bioRxiv : the preprint server for biology
|November 26, 2025
PubMed
概括
此摘要是机器生成的。

多神经元聊天 (MultiNeuronChat) 从单核RNA测序数据增强了细胞通信分析. 这一框架精确地识别了细胞类型对水平上的改变的神经元-神经元和神经元-质沟通通路径,有助于疾病研究.

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Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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科学领域:

  • 神经科学是一个神经科学.
  • 基因组学就是基因组学.
  • 计算生物学 计算生物学

背景情况:

  • 单核RNA测序 (snRNA-seq) 能够研究细胞通信.
  • 目前的方法缺乏用于差异通信分析的细胞类型对分辨率.
  • 了解改变的细胞通信对于疾病和发育研究至关重要.

研究的目的:

  • 开发一个高分辨率的框架来推断差异性神经元-神经元和神经元-质沟通.
  • 以使用snRNA-seq数据在细胞类型对水平上进行分析.
  • 改进从通信通道变化中获得的生物和医学见解.

主要方法:

  • 多神经聊天框架集成snRNA-seq基因表达数据.
  • 使用细胞粘附分子,间隙结和突触传输的全面数据库.
  • 在细胞类型对水平上对细胞通信进行差异分析.

主要成果:

  • 精确和高效的预测细胞类型特定的干扰 in silico.
  • 在阿尔茨海默病患者数据中识别已知和新型的通信途径.
  • 在检测微妙的沟通变化方面表现出敏感性.

结论:

  • MultiNeuronChat提供了一种高分辨率的方法,用于从snRNA-seq数据中分析细胞通信.
  • 该框架增强了在疾病背景下对细胞类型特定通信变化的理解.
  • 捐赠者特定的沟通得分可以为患者分层和个性化医疗提供信息.