A Scalable Method to Study Neuronal Survival in Primary Neuronal Culture with Single-cell and Real-Time Resolution.

单细胞分析 神经科学 计算机科学 细胞 生物 电池类型 生物神经网络 神经突 神经元 细胞生物学
作者
Ángela Rodríguez-Prieto,Ana González-Manteiga,Yaiza Domínguez-Canterla,Carmen Navarro-González,Pietro Fazzari
出处
期刊:Journal of Visualized Experiments [MyJoVE Corporation]
卷期号: (173)
标识
DOI:10.3791/62759
摘要

Neuronal loss is at the core of many neuropathologies, including stroke, Alzheimer's disease, and Parkinson's disease. Different methods were developed to study the process of neuronal survival upon cytotoxic stress. Most methods are based on biochemical approaches that do not allow single-cell resolution or involve complex and costly methodologies. Presented here is a versatile, inexpensive, and effective experimental paradigm to study neuronal survival. This method takes advantage of sparse fluorescent labeling of the neurons followed by live imaging and automated quantification. To this aim, the neurons are electroporated to express fluorescent markers and co-cultured with non-electroporated neurons to easily regulate cell density and increase survival. Sparse labeling by electroporation allows a simple and robust automated quantification. In addition, fluorescent labeling can be combined with the co-expression of a gene of interest to study specific molecular pathways. Here, we present a model of stroke as a neurotoxic model, namely, the oxygen-glucose deprivation (OGD) assay, which was performed in an affordable and robust homemade hypoxic chamber. Finally, two different workflows are described using IN Cell Analyzer 2200 or the open-source ImageJ for image analysis for semi-automatic data processing. This workflow can be easily adapted to different experimental models of toxicity and scaled up for high-throughput screening. In conclusion, the described protocol provides an approachable, affordable, and effective in vitro model of neurotoxicity, which can be suitable for testing the roles of specific genes and pathways in live imaging and for high-throughput drug screening.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI5应助笨笨松采纳,获得10
1秒前
爆米花应助玥来玥好采纳,获得10
2秒前
bing完成签到,获得积分10
3秒前
3秒前
科研通AI5应助落后冬灵采纳,获得10
3秒前
3秒前
4秒前
4秒前
4秒前
wanghao婷完成签到,获得积分20
4秒前
ysxlybt2发布了新的文献求助30
4秒前
5秒前
skier完成签到,获得积分10
5秒前
俊秀的翼发布了新的文献求助10
5秒前
Lii完成签到 ,获得积分10
5秒前
脑洞疼应助lucifer0922采纳,获得10
5秒前
adorable完成签到,获得积分10
6秒前
6秒前
烟花应助科研猫采纳,获得10
7秒前
隐形曼青应助ww采纳,获得10
7秒前
7秒前
李爱国应助一千年以后采纳,获得10
7秒前
8秒前
kkkk发布了新的文献求助10
8秒前
小鱼完成签到,获得积分10
8秒前
含章完成签到,获得积分10
9秒前
麦田守望者完成签到,获得积分10
9秒前
skier发布了新的文献求助10
9秒前
清脆的夜云完成签到,获得积分10
9秒前
MXL完成签到,获得积分10
10秒前
10秒前
10秒前
11秒前
尉迟明风完成签到 ,获得积分10
11秒前
含章发布了新的文献求助10
11秒前
暗号发布了新的文献求助10
12秒前
Ava应助孤独的AD钙采纳,获得10
12秒前
CipherSage应助xmd采纳,获得10
12秒前
田様应助杨合霖采纳,获得10
13秒前
Orange应助筷子吃不了面采纳,获得10
13秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 610
Time Matters: On Theory and Method 500
Virulence Mechanisms of Plant-Pathogenic Bacteria 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3559249
求助须知:如何正确求助?哪些是违规求助? 3133915
关于积分的说明 9404473
捐赠科研通 2834019
什么是DOI,文献DOI怎么找? 1557787
邀请新用户注册赠送积分活动 727686
科研通“疑难数据库(出版商)”最低求助积分说明 716399