Summary

人类干细胞来源的中脑多巴胺能神经元的表型分析

Published: July 07, 2023
doi:

Summary

该方案描述了人中脑多巴胺能神经元的细胞培养,然后进行免疫染色和从获得的微观高内涵图像生成神经元表型图谱,从而可以识别由于遗传或化学调节引起的表型变异。

Abstract

帕金森病 (PD) 与一系列导致中脑多巴胺能 (mDA) 神经元丢失的细胞生物学过程有关。许多目前的 体外 PD细胞模型缺乏复杂性,没有考虑多种表型。人诱导多能干细胞 (iPSC) 衍生的 mDA 神经元的表型分析可以通过同时测量 PD 相关细胞类型中的一系列神经元表型来解决这些缺点。在这里,我们描述了一种从市售的人类mDA神经元中获取和分析表型谱的协议。神经元特异性荧光染色面板用于可视化细胞核、α-突触核蛋白、酪氨酸羟化酶 (TH) 和微管相关蛋白 2 (MAP2) 相关表型。所描述的表型分析方案具有可扩展性,因为它使用 384 孔板、自动液体处理和高通量显微镜。使用健康供体 mDA 神经元和携带富含亮氨酸的重复激酶 2 (LRRK2) 基因中 PD 连接的 G2019S 突变的 mDA 神经元来举例说明该协议的效用。两种细胞系均用LRRK2激酶抑制剂PFE-360处理,并测量表型变化。此外,我们还演示了如何使用聚类或机器学习驱动的监督分类方法分析多维表型图谱。所描述的协议将特别引起从事神经元疾病建模或研究人类神经元中化合物效应的研究人员的兴趣。

Introduction

帕金森病 (PD) 中的多种细胞生物过程受到干扰。例如,线粒体功能障碍、氧化应激、蛋白质降解缺陷、囊泡运输和内溶酶体功能的破坏与中脑多巴胺能 (mDA) 神经元丢失有关,常见于 PD1。因此,帕金森病似乎涉及多种疾病机制,这些机制可以相互作用并相互恶化。研究这种机制相互作用的一种有用方法是创建中脑多巴胺能 (mDA) 神经元的综合表型指纹或图谱。

表型分析是一种方法,涉及根据一组可测量的特征创建样本的图谱,其次,它涉及根据该图谱对样本进行预测 2,3。分析的目标是捕获各种特征,其中一些特征以前可能与疾病或治疗无关3.因此,分析可以揭示意想不到的生物过程。表型分析通常依赖于荧光染色的细胞,并且已经开发了标准化检测方法,例如细胞绘画,以创建表型图谱 4。例如,最近,表型分析已被应用于小分子的表征或仅基于患者来源的成纤维细胞 5,6 的 PD 亚型的准确预测。尽管取得了这些进展,但表型分析很少应用于有丝分裂后分化细胞,例如表达PD相关突变(如LRRK2 G2019S)的人诱导多能干细胞(iPSC)衍生的mDA神经元。iPSC衍生模型的重大挑战包括分化批次或基因型之间存在细微或可变的病理特征,以及孤立的PD表型不能捕捉到疾病的全部复杂性。此外,虽然 iPSC 神经元模型具有生理相关性,但由于担心技术复杂性,它们很少用于 PD 药物发现过程 7,8

我们之前开发了一种强大的方法来测量人类 mDA 神经元中多种与 PD 相关的病理生理表型表型,这些神经元对遗传和化合物诱导的表型变化都很敏感9。本文详细描述了该方法的进一步优化版本,以从mDA神经元创建表型图谱(图1)。与先前描述的表型分析方法相比,该协议具有几个优点,例如使用高质量的mDA神经元和技术可重复性。该协议首次以高度可扩展的方式在化学扰动后实现生理相关的有丝分裂后mDA神经元的表型分析。完全分化和冷冻保存的 mDA 神经元是市售的,可显著降低批次间的分化变异性。其次,通过使用明确定义的实验设计(即培养持续时间或避免边缘孔)、自动液体处理和自动显微镜,可以进一步减少技术变异性。此外,本文还概述了使用无监督聚类或监督分类方法进行表型谱分析的初始步骤,指出了如何分析表型谱数据。该协议将用于对遗传或化学扰动诱导的mDA神经元的表型变化感兴趣的研究人员,特别是当需要高度可扩展的研究设置时,例如,在筛选活动期间或当要研究较少数量的化合物的影响时,例如,确定毒性作用。总之,预计人类神经元表型分析的应用是研究复杂疾病相关表型和表征候选药物细胞效应的宝贵技术。

Figure 1
1:从人类 iPSC 衍生的 mDA 神经元生成基于图像的表型图谱的实验方案示意图。 请点击这里查看此图的较大版本.

Protocol

1. 准备用于神经元接种的培养基和培养板(第 1 天) 为了在第 1 天准备用于神经元接种的板,请在使用前将层粘连蛋白加热至室温 (RT)。通过将层粘连蛋白储备溶液(0.1mg / mL)稀释在冷PBS + / +(含Ca2+ 和Mg2 +)中1/10来制备层粘连蛋白溶液。注:所有试剂均列在 材料表中。溶液和缓冲液的组成见 表1-4。 然后,向聚-D-赖氨?…

Representative Results

mDA神经元中的表型分析是量化细胞生物学的多个方面及其在实验调节过程中的变化的有效方法。为了举例说明这种方法,本研究使用了冷冻保存的 LRRK2 G2019S 和健康的供体 mDA 神经元。这些神经元已经分化了大约 37 天,是有丝分裂后和表达神经元标志物(TUBB3 和 MAP2)和多巴胺能神经元标志物,包括酪氨酸羟化酶 (TH) 与 FOXA2 联合使用,而神经胶质标志物胶质纤维酸性蛋白 (GFAP) 缺失<sup class="x…

Discussion

表型分析是一种通过应用荧光染色、显微镜和图像分析来测量细胞中大量表型的技术3。可以跨细胞系或其他实验条件获得和比较表型图谱,以了解细胞生物学中的复杂变化,这些变化在使用单次读数时可能会被忽视。在这里我们描述了表型分析在人类 iPSC 衍生的 mDA 神经元中的应用,这是一种经常用于模拟 PD 细胞生物学的细胞类型17,18,19。<sup class="xr…

Declarações

The authors have nothing to disclose.

Acknowledgements

作者要感谢Ksilink的所有同事,感谢他们为所提出的方案设计提供的宝贵帮助和讨论。

Materials

Anti- chicken – Alexa 647 Jackson ImmunoRearch 703-605-155 Immunofluorescence
Anaconda https://www.anaconda.com/download
Anti-Map2 Novus NB300-213 Immunofluorescence
Anti-mouse – Alexa 488 Thermo Fisher A11001 Immunofluorescence
Anti-rabbit – Alexa 555 Thermo Fisher A21429 Immunofluorescence
Anti-Tyrosine Hydroxylase Merck T2928 Immunofluorescence
Anti-α-synuclein Abcam 138501 Immunofluorescence
Bravo Automated Liquid Handling Platform with 384ST head Agilent If no liquid handler is available, the use of an electronic multichannel pipette is recommended.
Confocal microscope  Yokogawa CV7000 The use of an automated confocal fluorescence microscope is recommended to ensure image quality consistency.
Countess Automated cell counter Invitrogen Cell counting before seeding. Can also be done using a manual counting chamber.
DPBS +/+ Gibco 14040-133 Buffer for washing
EL406 Washer Dispenser  BioTek (Agilent)  If no liquid handler is available, the use of an electronic multichannel pipette is recommended.
Formaldehyde Solution (PFA 16 %) Euromedex EM-15710-S Fixation before staining
Hoechst 33342 Invitrogen H3570 Nuclear staining
iCell Base Medium 1 Fujifilm M1010 Base medium for neurons
iCell DPN, Donor#01279, Phenotype AHN, lot#106339, 1M Fujifilm C1087 Apparently healthy donor
iCell DPN, Donor#11299, Phenotype LRRK2 G2019S, phenotype PD lot#106139 Fujifilm C1149 Donor carrying LRRK2 G2019S mutation 
iCell Nervous System Supplement Fujifilm M1031 Supplement for base medium
iCell Neural Supplement B Fujifilm M1029 Supplement for base medium
Jupyter Python Notebook In-house development https://github.com/Ksilink/Notebooks/tree/main/Neuro/DopaNeuronProfiling Notebook to perform phenotypic profile visualization and classification from raw data.
Laminin Biolamina LN521 Plate coating
PFE-360 MedChemExpress HY-120085 LRRK2 kinase inhibitor
PhenoLink In-house development https://github.com/Ksilink/PhenoLink Software for image analysis
PhenoPlate 384w, PDL coated Perkin Elmer 6057500 Pre-coated plate for cell culture and imaging. This plate allows imaging of all wells using all objectives of the Yokogawa CV7000 microscope.
Storage plates Abgene 120 µL Thermo Scientific AB-0781 Necessary for compound dispensing using the Vprep pipetting system. If not available, the use of an electronic multichannel pipette is recommended.
Triton Sigma T9284 Permeabilization before lysis
Trypan Blue Sigma T8154-20ML Determination of living cells
Vprep Pipetting System  Agilent Medium change and compound dispensing. Alternatively, an electronic multichannel pipette can be used.

Referências

  1. Panicker, N., Ge, P., Dawson, V. L., Dawson, T. M. The cell biology of Parkinson’s disease. The Journal of Cell Biology. 220 (4), 202012095 (2021).
  2. Caicedo, J. C., et al. Data-analysis strategies for image-based cell profiling. Nature Methods. 14 (9), 849-863 (2017).
  3. Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., Carpenter, A. E. Image-based profiling for drug discovery: due for a machine-learning upgrade. Nature Reviews Drug Discovery. 20 (2), 145-159 (2021).
  4. Bray, M. -. A., et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nature Protocols. 11 (9), 1757-1774 (2016).
  5. Schiff, L., et al. Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts. Nature Communications. 13 (1), 1590 (2022).
  6. Ziegler, S., Sievers, S., Waldmann, H. Morphological profiling of small molecules. Cell Chemical Biology. 28 (3), 300-319 (2021).
  7. Cobb, M. M., Ravisankar, A., Skibinski, G., Finkbeiner, S. iPS cells in the study of PD molecular pathogenesis. Cell and Tissue Research. 373 (1), 61-77 (2018).
  8. Elitt, M. S., Barbar, L., Tesar, P. J. Drug screening for human genetic diseases using iPSC models. Human Molecular Genetics. 27 (R2), 89-98 (2018).
  9. Vuidel, A., et al. High-content phenotyping of Parkinson’s disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification. Stem Cell Reports. 17 (10), 2349-2364 (2022).
  10. Stirling, D. R., Swain-Bowden, M. J., Lucas, A. M., Carpenter, A. E., Cimini, B. A., Goodman, A. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics. 22 (1), 433 (2021).
  11. Schindelin, J., et al. Fiji: an open-source platform for biological-image analysis. Nature Methods. 9 (7), 676-682 (2012).
  12. Sofroniew, N., et al. . napari: a multi-dimensional image viewer for Python. , (2022).
  13. Berthold, M. R., et al. KNIME: The Konstanz Information Miner. Data Analysis, Machine Learning and Applications. , 319-326 (2008).
  14. Fathi, A., et al. Diverging Parkinson’s Disease Pathology between patient-derived GBAN370S, LRRK2G2019S and engineered SNCAA53T iPSC-derived Dopaminergic Neurons. bioRxiv. , (2023).
  15. Wang, Y., Huang, H., Rudin, C., Shaposhnik, Y. Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. Journal of Machine Learning Research. 22 (201), 1-73 (2021).
  16. Ke, G., et al. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems. 30, (2017).
  17. Avazzadeh, S., Baena, J. M., Keighron, C., Feller-Sanchez, Y., Quinlan, L. R. Modelling Parkinson’s Disease: iPSCs towards Better Understanding of Human Pathology. Brain Sciences. 11 (3), 373 (2021).
  18. Sánchez-Danés, A., et al. Disease-specific phenotypes in dopamine neurons from human iPS-based models of genetic and sporadic Parkinson’s disease. EMBO Molecular Medicine. 4 (5), 380-395 (2012).
  19. Oosterveen, T., et al. Pluripotent stem cell derived dopaminergic subpopulations model the selective neuron degeneration in Parkinson’s disease. Stem Cell Reports. 16 (11), 2718-2735 (2021).
  20. Hughes, R. E., et al. Multiparametric high-content cell painting identifies copper ionophores as selective modulators of esophageal cancer phenotypes. ACS Chemical Biology. 17 (7), 1876-1889 (2022).
  21. Akbarzadeh, M., et al. Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds. Cell Chemical Biology. 29 (6), 1053-1064 (2022).
  22. Schiff, L., et al. Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts. Nature Communications. 13 (1), 1590 (2022).
  23. Way, G. P., et al. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Systems. 13 (11), 911-923 (2022).
  24. Feng, Y., Mitchison, T. J., Bender, A., Young, D. W., Tallarico, J. A. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nature Reviews Drug Discovery. 8 (7), 567-578 (2009).
  25. Antonov, S. A., Novosadova, E. V. Current state-of-the-art and unresolved problems in using human induced pluripotent stem cell-derived dopamine neurons for parkinson’s disease drug development. International Journal of Molecular Sciences. 22 (7), 3381 (2021).
  26. Miller, J. D., et al. Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell. 13 (6), 691-705 (2013).
  27. Bezard, E., Gross, C. E., Brotchie, J. M. Presymptomatic compensation in Parkinson’s disease is not dopamine-mediated. Trends in Neurosciences. 26 (4), 215-221 (2003).
  28. Wu, Y., Le, W., Jankovic, J. Preclinical Biomarkers of parkinson disease. Archives of Neurology. 68 (1), 22-30 (2011).
  29. Verstraelen, P., et al. Systematic quantification of synapses in primary neuronal culture. iScience. 23 (9), 101542 (2020).
  30. Liu-Yesucevitz, L., et al. ALS-Linked mutations enlarge TDP-43-enriched neuronal RNA granules in the dendritic arbor. The Journal of Neuroscience. 34 (12), 4167-4174 (2014).

Play Video

Citar este artigo
Weiss, A., Sommer, P., Wilbertz, J. H. Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons. J. Vis. Exp. (197), e65570, doi:10.3791/65570 (2023).

View Video