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High‐throughput Functional Annotation of the Caenorhabditis elegans Neural Network
Author(s) -
Aoki Wataru,
Yokoyama Haruki,
Matsukura Hidenori,
Ueda Mitsuyoshi
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.936.4
Subject(s) - optogenetics , computer science , artificial neural network , neuroscience , biological neural network , throughput , caenorhabditis elegans , neuron , artificial intelligence , biology , machine learning , telecommunications , biochemistry , gene , wireless
In order to investigate the relationship between behavior and neural networks, such a technology that can artificially control the activity of the particular neuron is required. Once we artificially manipulate the activity of neurons, we will be able to examine in detail how the neural networks function by evaluating how the artificial manipulation influences the behavior. Optogenetics is a technology that is able to freely control the activity of neurons with opsin by light irradiation. Optogenetics provides a methodology that can precisely analyze the relationship between behavior and neural network, and has become a trigger of rapid progress of brain research in the circuit‐centric approaches. However, there are some weaknesses in the optogenetics. 1. Optogenetics requires a hypothesis, because we have to consider in advance which group of neurons is related to particular behavior when choosing promoter regulating opsin expression. For this reason, although it is a powerful method for validation and development of existing hypothesis, a completely new discovery is hard to be achieved. 2. Throughput is low. We need to create different transgenic animals depending on the hypothesis to be tested. 3. Single cell level analysis is difficult. Cell‐specific promoter does not usually exist. In addition, it is difficult to converge light on the single neuron of moving animals. In order to overcome these weaknesses, we propose a new method that does not need any hypothesis, can be high throughput and can analyze in single cell resolution (Aoki et al . submitted). A key technology for this methodology is Cre/lox system. By applying Cre/lox system, we have established a methodology to randomize single neuron that expresses opsin, among the all neurons that construct the neural network. In this methodology, we conduct behavior experiment after opsin expressions are randomized, and identify the neuron that influences the behavior “later”. Therefore, it does not require the hypothesis in advance. In addition, since we can create a large number of individuals that have randomized opsin expression pattern, from single individual, our method is feasible for high throughput analysis. Since expression of opsin is determined in each cell at random, single cell level analysis is feasible. Here we present stochastic expression of opsin and high‐throughput annotation of phenotype using C. elegans as an experimental model.