Development and improvement of ‘functional neural cellomics’ to elucidate the structure‐function relationships of neural networks of Caenorhabditis elegans
Development and improvement of ‘functional neural cellomics’ to elucidate the structure‐function relationships of neural networks of Caenorhabditis elegans
复制标题
开发和改进“功能神经细胞组学”以阐明秀丽隐杆线虫神经网络的结构功能关系
DOI：
10.1096/fasebj.2020.34.s1.03223
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ueda Mitsuyoshi
中科院分区：
文献类型：
--
作者：
Yamauchi Yuji;Aoki Wataru;Ueda Mitsuyoshi
背景神经系统会产生复杂的行为。然而，神经计算和行为之间的关系几乎没有被阐明。光遗传学是研究神经计算机制的一项强有力的技术。然而，传统的光遗传学存在三个问题：(1)实验前需要一个假设；(2)吞吐量低，因为我们需要产生合适的转基因动物来检验每个假设；(3)单细胞分析很困难，因为已知的单细胞特异性启动子很少。为了解决这些问题，我们发展了一种新的方法学--功能神经细胞组学，它能够全面地阐明线虫行为与神经网络之间的关系。方法功能神经细胞组学的核心技术是视蛋白在OFC神经网络中的随机标记。优雅女装。应用CRE-lox系统实现了视蛋白的随机标记。我们设计了一个遗传回路，其中两组lox变异体lox2272和loxP序列交替插入泛神经元F25B3.3启动子下游。此外，mCherry和转录因子QF2w被插入到LoxSequence之间。在热激诱导Cre重组酶后，Cre-lox重组事件仅在lox2272序列之间或loxP序列之间发生。如果允许Cre作用于lox2272序列，则表达QF2w。然后，QF2w诱导ChR2：：GFP的表达。这样，我们就可以很容易地获得Ac。结果与讨论通过诱导Cre重组酶，我们证实了ChR2：：GFP在每种动物中以随机方式标记。为了验证功能神经细胞组学的可行性，我们试图证明神经元参与了FC的产卵行为。可以以高通量的方式鉴定出雅致病毒。The c.我们用蓝光照射ELEAN文库来激活ChR2，我们发现一些动物的产卵依赖于蓝光。我们用共聚焦显微镜鉴定了产生ChR2的神经元，并证实ChR2：：GFP在产卵个体的HSN(两性特异性神经元)中表达，而在非产卵个体中不表达[1]。如果视蛋白在太多的神经元中被标记，就很难区分单个神经元的功能。为了能够精确控制标记率，我们通过聚合酶链式反应生成了一个随机的LOX序列文库。我们用NGS评估了氧变异体的切割效率，并成功地鉴定了切割效率在0.01%到100%之间的氧变异体序列。使用这些变异体，我们将能够改进功能神经细胞学，以便对神经网络进行全面干预和高通量识别神经功能。支持或资助信息这项工作得到了Presto，JST(赠款编号：JPMJPR16F1)、JSPS KAKENHI(批准号：JP17K19452)，和京都大学实时成像中心。艾尔SCI Rep 8,10380(2018年)
BackgroundA nervous system generates complex behaviors. However, relationships between neural computation and behaviors have hardly been elucidated. Optogenetics is a powerful technique for investigating mechanisms of neural computation. However, conventional optogenetics has three problems: (1) a hypothesis is required before an experiment; (2) the throughput is low because we should generate appropriate transgenic animals to test each hypothesis; (3) single‐cell analysis is difficult because few single‐cell‐specific promoters are known. To solve these problems, we have developed a new methodology ‘functional neural cellomics’ which enables comprehensive elucidation of relationships between behaviors and neural networks ofCaenorhabditis elegans.MethodThe core technology of functional neural cellomics is the stochastic labeling of opsin in the neural network ofC. elegans. The stochastic labeling of opsin was implemented by applying the Cre‐loxsystem. We designed a genetics circuit in which two sets of lox variants,lox2272andloxPsequences, are inserted alternately downstream of pan‐neuronal F25B3.3 promoter. In addition, mCherry and a transcription factor, QF2w, are interposed between theseloxsequences. After the induction of Cre recombinase by heat shock, a Cre‐loxrecombination event occurs exclusively either betweenlox2272sequences or betweenloxPsequences. If Cre is allowed to act onlox2272sequences, QF2w is expressed. Then, QF2w induces expression of ChR2::GFP. In this way, we can easily acquire aC. eleganslibrary in which ChR2 is labeled in a stochastic manner in each animal.Results & DiscussionBy inducing Cre recombinase, we confirmed that ChR2::GFP was labeled in a stochastic manner in each animal. To verify the feasibility of functional neural cellomics, we tried to demonstrate that neurons involved in the egg‐laying behavior ofC. eleganscould be identified in a high‐throughput manner. TheC. eleganslibrary was irradiated with blue light to activate ChR2, and we found some animals laid eggs in a blue‐light‐dependent manner. We identified neurons producing ChR2 using a confocal microscope, and confirmed that ChR2::GFP was expressed in HSNs (hermaphrodite‐specific neurons) in the egg‐laying individuals, but was not in the non‐egg laying individuals [1].In the above experiment, we found that the labeling rate of opsin was about 30%. If opsin is labeled in too many neurons, it becomes difficult to distinguish the function of individual neurons. To enable precise control of the labeling rate, we generated a library of randomized lox sequences by PCR. We evaluated excision efficiencies of theloxvariants by using NGS, and successfully identifiedloxvariant sequences which showed excision efficiencies ranging from 0.01% to 100%. Using theseloxvariants, we will be able to improve functional neural cellomics for comprehensive intervention in neural networks and high‐throughput identification of neural functions.Support or Funding InformationThis work was supported by PRESTO, JST (grant No. JPMJPR16F1), JSPS KAKENHI (grant No. JP17K19452), and Kyoto University Live Imaging Center.[1]Aoki et. al. Sci Rep 8, 10380 ( 2018)