Summary

基于 IntelliCage 的阿尔茨海默病多基因模型认知功能的自动、长期行为检测

Published: August 04, 2018
doi:

Summary

本文介绍了一种基于 IntelliCage 系统的阿尔茨海默病遗传模型认知评估的协议, 它是一种具有操作性调节的高通量自动化行为监测系统。

Abstract

多种因素–如衰老和基因–往往与认知下降有关。基因改良的小鼠认知衰退模型, 如阿尔茨海默病 (AD), 已成为一个有希望的工具来阐明的基础机制和促进治疗进展。一个重要的步骤是验证和定性的预期行为异常的模型, 在广告, 认知下降的情况下。对实验动物进行长期行为调查, 研究衰老的影响需要研究人员的大量努力。IntelliCage 系统是一种高吞吐量和经济高效的小鼠测试电池, 它消除了日常人类处理的需要。在这里, 我们描述如何利用该系统的长期分型的基因阿尔茨海默病模型, 特别是关注的认知功能。实验采用重复的测试电池来评估空间学习和执行功能。这种具有成本效益的年龄依赖性分型使我们能够确定基因在各种认知方面的瞬态和/或永久性影响。

Introduction

过去十年中, 神经元疾病动物模型的发展提供了对其基础的机械理解, 以促进治疗进展1,2,3。高通量行为测试电池在遗传动物模型中的应用是研究人类疾病的基本机制和药物治疗鉴定的一种启发式的探索工具。用于长期观察衰老和/或痴呆模型的研究测试电池传统上迫使实验室消耗大量的专门人力和时间。家庭网箱监测系统将是一项成本效益高的战略, 因为这将降低人类行为观察的成本。一些研究小组开发了自动化的基于视觉的工具, 帮助单个个体在456的小型家庭笼子中的行为分型。然而, 这种方法限制了社会互动, 测试环境的大小, 以及包括认知功能在内的各种行为措施。IntelliCage 是第二代家庭网箱监测系统, 旨在在一个社会家庭笼中执行各种认知任务。重要的是, 这种方法可以消除日常处理, 使我们能够进行长期的行为监测和评估的认知功能, 它可以消除对专门的实际处理的要求, 并使高重现性数据采集7。在这里, 我们描述的长期分型和有效性的基因小鼠模型的阿尔茨海默病 (AD) 已产生最近8,9,10使用自动家庭网箱监测系统。一个测试电池, 包括空间学习和执行功能的评估, 反复执行在多个年龄点 (9–12和14–17月老)。这个年龄依赖性的分型使我们能够确定基因在不同认知方面的瞬态和/或永久性影响。我们发现, 一些广告模型显示了一些认知方面的瞬态和永久性表型, 在长期分析中, 使用了自动化的家庭笼监测系统10。因此, 利用家庭网箱监测系统进行的自动化研究对各种认知功能障碍模型的长期行为分型和验证具有很高的效益和成本效益。

Protocol

所有的程序都得到了机构动物护理和使用委员会的批准, 他们是根据山本理脑科学研究所的动物实验指南进行的。 1. 设置设备 注: 自动家庭笼监控系统概述见图 1。每个系统 (39 cm x 58 cm x 21 cm) 包含一个微处理器和四个角落室, 每个有两个水瓶和一个环形天线, 用于检测植入到动物体内的转发器的射频识别 (图 1A)。…

Representative Results

在我们以前的研究中, 通过10的自动化家庭网箱监测系统的实验, 检测出 AD 模型中年龄依赖性的认知缺陷。他们的广告模式在 PP 的表现是完整的年轻成人和老科目;然而, PPR 的表现明显和逐渐受损 (图 6)。在适应阶段观察一般行为或焦虑也很重要, 因为这些特质可能影响认知15。广告模型没有显示任何严重异常的访问,…

Discussion

本文介绍了在转基因 AD 模型中, 采用自动家庭笼监控系统进行长期认知行为分析的方法。最关键的一步是在适当位置植入转发器。在执行植入之前, 确保转发器的过期日期没有通过。第二个重要的问题是, 每天检查系统的运行情况, 特别是作为一个小课题, 在研究期间可能会变得更加严重 (i.、堆积门、跌落式转发器、不良电连接)。.).第三, 必须能够解决问题, 因为在整个实验计划中?…

Declarações

The authors have nothing to disclose.

Acknowledgements

我们感谢嫂子安藤在摄影材料方面的帮助。这项研究得到了援助资助的探索性研究 (jsp KAKENHI 赠款号 16K15196)。

Materials

IntelliCage TSE Systems Parchased in 2011 or later
PC Dell Inspiron 580s
Display Dell SI75T-WL
ALPHA-dri Shepherd Specialty Papers Standard bedding
Aron Alpha (Krasy Glue) 2g Toagosei (Krasy Glue) #04612 Cyanoacrylates for gluing magnet and blak arm
Handheld Transponder Reader BTS-ID R-560 Transponder reader, which reads both Trovan and DataMars
Transponder DataMars T-VA, T-VAS, or another series Basic package of transponders and implanters
Diamond Grip Plus Ansel Microflex DGP-INT-M Experimental glove
Isoflurane Pfizer 1119701G1092
Vaporizer for small animals DS Pharma Biomedical SF-B01 Facemask included
Neo-Medrol Pfizer 006472-001 Eye ointment
Ethanol (70%)
Excel Microsoft 00202-51382-15524-AA928 For data analysis

Referências

  1. Bryan, K. J., Lee, H., Perry, G., Smith, M. A., Casadesus, G. . Transgenic Mouse Models of Alzheimer’s Disease: Behavioral Testing and Considerations. Methods of Behavior Analysis in Neuroscience. , (2009).
  2. Nestler, E. J., Hyman, S. E. Animal models of neuropsychiatric disorders. Nature Neuroscience. 13 (10), 1161-1169 (2010).
  3. Crawley, J. N. Behavioral Phenotyping Strategies for Mutant Mice. Neuron. 57 (6), 809-818 (2008).
  4. Zarringhalam, K., Ka, M., et al. An open system for automatic home-cage behavioral analysis and its application to male and female mouse models of Huntington’s disease. Behavioural Brain Research. 229 (1), 216-225 (2012).
  5. Prusiner, S. B., Jackson, W. S., King, O. D., Lindquist, S. The power of automated high-resolution behavior analysis revealed by its application to mouse models of Huntington’s and prion diseases. Proceedings of the National Academy of Sciences of the United States of America. 95 (23), 13363-13383 (1998).
  6. Jhuang, H., Garrote, E., et al. Automated home-cage behavioural phenotyping of mice. Nature Communications. 1 (6), 1-9 (2010).
  7. Krackow, S., Vannoni, E., et al. Consistent behavioral phenotype differences between inbred mouse strains in the IntelliCage. Genes, brain, and behavior. 9 (7), 722-731 (2010).
  8. Nilsson, P., Saito, T., Saido, T. C. New mouse model of Alzheimer’s. ACS chemical. 5 (7), 499-502 (2014).
  9. Saito, T., Matsuba, Y., et al. Single App knock-in mouse models of Alzheimer’s disease. Nat Neurosci. 17 (5), 661-663 (2014).
  10. Masuda, A., Kobayashi, Y., Kogo, N., Saito, T., Saido, T. C., Itohara, S. Cognitive deficits in single App knock-in mouse models. Neurobiology of Learning and Memory. , (2016).
  11. Chan, R. C. K., Shum, D., Toulopoulou, T., Chen, E. Y. H. Assessment of executive functions: Review of instruments and identification of critical issues. Archives of Clinical Neuropsychology. 23 (2), 201-216 (2008).
  12. Jurado, M. B., Rosselli, M. The Elusive Nature of Executive Functions: A Review of our Current Understanding. Neuropsychology Review. 17 (3), 213-233 (2007).
  13. Diamond, A. Executive Functions. Annual Review of Psychology. 64 (1), 135-168 (2013).
  14. Kobayashi, Y., Sano, Y., et al. Genetic dissection of medial habenula-interpeduncular nucleus pathway function in mice. Frontiers in behavioral neuroscience. 7, 17 (2013).
  15. Robinson, O. J., Vytal, K., Cornwell, B. R., Grillon, C. The impact of anxiety upon cognition: perspectives from human threat of shock studies. Frontiers in human neuroscience. 7, 203 (2013).
  16. Robbins, T. The 5-choice serial reaction time task: behavioural pharmacology and functional neurochemistry. Psychopharmacology. (3-4), 362-380 (2002).
  17. Asinof, S. K., Paine, T. A. The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents. Journal of Visualized Experiments. (90), e51574 (2014).
  18. Codita, A., Gumucio, A., et al. Impaired behavior of female tg-ArcSwe APP mice in the IntelliCage: A longitudinal study. Behavioural brain research. 215 (1), 83-94 (2010).
  19. Blumstein, D. T. Habituation and sensitization: new thoughts about old ideas. Animal Behaviour. 120, 255-262 (2016).
  20. Endo, T., Maekawa, F., et al. Automated test of behavioral flexibility in mice using a behavioral sequencing task in IntelliCage. Behavioural brain research. 221 (1), 172-181 (2011).
  21. Voikar, V., Colacicco, G., Gruber, O., Vannoni, E., Lipp, H. -. P., Wolfer, D. P. Conditioned response suppression in the IntelliCage: assessment of mouse strain differences and effects of hippocampal and striatal lesions on acquisition and retention of memory. Behavioural brain research. 213 (2), 304-312 (2010).
  22. Puścian, A., Łęski, S., Górkiewicz, T., Meyza, K., Lipp, H. -. P., Knapska, E. A novel automated behavioral test battery assessing cognitive rigidity in two genetic mouse models of autism. Frontiers in Behavioral Neuroscience. 8, 140 (2014).
  23. Voikar, V., Colacicco, G., Gruber, O., Vannoni, E., Lipp, H. -. P., Wolfer, D. P. Conditioned response suppression in the IntelliCage: assessment of mouse strain differences and effects of hippocampal and striatal lesions on acquisition and retention of memory. Behavioural brain research. 213 (2), 304-312 (2010).
  24. Harda, Z., Dzik, J. M., et al. Autophosphorylation of αCaMKII affects social interactions in mice. Genes, Brain and Behavior. , e12457 (2018).
  25. Aarts, E., Maroteaux, G., et al. The light spot test: Measuring anxiety in mice in an automated home-cage environment. Behavioural Brain Research. 294, 123-130 (2015).
  26. Safi, K., Neuhäusser-Wespy, F., et al. Mouse anxiety models and an example of an experimental setup using unconditioned avoidance in an automated system -IntelliCage. Cognition Brain & Behavior. 10 (4), 475-488 (2006).
  27. Dzik, J. M., Puścian, A., Mijakowska, Z., Radwanska, K., Łęski, S. PyMICE: APython library for analysis of IntelliCage data. Behavior Research Methods. 50 (2), 804-815 (2018).
check_url/pt/58009?article_type=t

Play Video

Citar este artigo
Masuda, A., Kobayashi, Y., Itohara, S. Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer’s Disease, Using IntelliCage. J. Vis. Exp. (138), e58009, doi:10.3791/58009 (2018).

View Video