氧氣供應(yīng)對處于浸浴狀態(tài)的海馬網(wǎng)絡(luò)活動的重要性

過去對腦切片的研究， 對神經(jīng)元和突觸的基本特征方面已取得了豐富資料。但在完整的腦，進行中的的網(wǎng)絡(luò)活動可能會嚴重影響這些特征。雖然，生理上更接近現(xiàn)實的網(wǎng)絡(luò)活動模式已能在保存于界面型培養(yǎng)容器中的腦切片中成功地誘導(dǎo)出來，但此類資料卻難以在浸浴型培養(yǎng)容器中取得，而浸浴型容器卻能提供更好的實驗優(yōu)勢，包括藥理試劑的快速交換，視覺輔助的膜片鉗記錄和成像技術(shù)。在這里，我們通過大鼠和小鼠制備的海馬浸浴切片，研究產(chǎn)生網(wǎng)絡(luò)振蕩(network oscillation) 的條件。研究發(fā)現(xiàn)，局部的氧氣量對 自發(fā)尖波波動振蕩 和膽堿能誘導(dǎo)快速振蕩的產(chǎn)生和傳播是至關(guān)重要。我們建議三種方式以改善在浸浴下腦切片的氧氣供應(yīng)： （一）優(yōu)化培養(yǎng)容器設(shè)計以利于灌流液的層流; （二）增加灌流液的流速;（ 三 ）灌流切片的兩個表面層。這些記錄條件的改進有利于研究處于更現(xiàn)實條件下的神經(jīng)元網(wǎng)絡(luò)活動，這些網(wǎng)絡(luò)活動對更好地了解整個神經(jīng)網(wǎng)絡(luò)活動是必不可少的。

Maintaining network activity in submerged hippocampal slices: importance of oxygen supply

Abstract：Studies in brain slices have provided a wealth of data on the basic features of neurons and synapses. In the intact brain, these properties may be strongly influenced by ongoing network activity. Although physiologically realistic patterns of network activity have been successfully induced in brain slices maintained in interface-type recording chambers, they have been harder to obtain in submerged-type chambers, which offer significant experimental advantages, including fast exchange of pharmacological agents, visually guided patch-clamp recordings, and imaging techniques. Here, we investigated conditions for the emergence of network oscillations in submerged slices prepared from the hippocampus of rats and mice. We found that the local oxygen level is critical for generation and propagation of both spontaneously occurring sharp wave–ripple oscillations and cholinergically induced fast oscillations. We suggest three ways to improve the oxygen supply to slices under submerged conditions: (i) optimizing chamber design for laminar flow of superfusion fluid; (ii) increasing the flow rate of superfusion fluid; and (iii) superfusing both surfaces of the slice. These improvements to the recording conditions enable detailed studies of neurons under more realistic conditions of network activity, which are essential for a better understanding of neuronal network operation.

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