Name: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons (Video) | JoVE
Uploaded: 2023-07-07T14:00:00
Description: Watch this Scientific Journal Video about Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons at JoVE.com

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。

1. 准备用于神经元接种的培养基和培养板（第 1 天）
<ol><li>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。</li>
	<li>然后，向聚-D-赖氨?...

Culturing Human Midbrain Dopaminergic Neurons for Phenotypic Profiling

<ol>
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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...

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。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65570/65570fig01.jpg"> 图 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。 <a href="https://www.jove.com/files/ftp_upload/65570/65570fig01large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Anti- chicken &#8211; Alexa 647</td>
			<td>Jackson ImmunoRearch</td>
			<td>703-605-155</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Anaconda</td>
			<td></td>
			<td>https://www.anaconda.com/download</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-Map2</td>
			<td>Novus</td>
			<td>NB300-213</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Anti-mouse - Alexa 488</td>
			<td>Thermo Fisher</td>
			<td>A11001</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Anti-rabbit - Alexa 555</td>
			<td>Thermo Fisher</td>
			<td>A21429</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Anti-Tyrosine Hydroxylase</td>
			<td>Merck</td>
			<td>T2928</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Anti-&#945;-synuclein</td>
			<td>Abcam</td>
			<td>138501</td>
			<td>Immunofluorescence</td>
		</tr>
		<tr>
			<td>Bravo Automated Liquid Handling Platform with 384ST head</td>
			<td>Agilent</td>
			<td></td>
			<td>If no liquid handler is available, the use of an electronic multichannel pipette is recommended.</td>
		</tr>
		<tr>
			<td>Confocal microscope&#160;</td>
			<td>Yokogawa</td>
			<td>CV7000</td>
			<td>The use of an automated confocal fluorescence microscope is recommended to ensure image quality consistency.</td>
		</tr>
		<tr>
			<td>Countess Automated cell counter</td>
			<td>Invitrogen</td>
			<td></td>
			<td>Cell counting before seeding. Can also be done using a manual counting chamber.</td>
		</tr>
		<tr>
			<td>DPBS +/+</td>
			<td>Gibco</td>
			<td>14040-133</td>
			<td>Buffer for washing</td>
		</tr>
		<tr>
			<td>EL406 Washer Dispenser&#160;</td>
			<td>BioTek (Agilent)&#160;</td>
			<td></td>
			<td>If no liquid handler is available, the use of an electronic multichannel pipette is recommended.</td>
		</tr>
		<tr>
			<td>Formaldehyde Solution (PFA 16 %)</td>
			<td>Euromedex</td>
			<td>EM-15710-S</td>
			<td>Fixation before staining</td>
		</tr>
		<tr>
			<td>Hoechst 33342</td>
			<td>Invitrogen</td>
			<td>H3570</td>
			<td>Nuclear staining</td>
		</tr>
		<tr>
			<td>iCell Base Medium 1</td>
			<td>Fujifilm</td>
			<td>M1010</td>
			<td>Base medium for neurons</td>
		</tr>
		<tr>
			<td>iCell DPN, Donor#01279, Phenotype AHN, lot#106339, 1M</td>
			<td>Fujifilm</td>
			<td>C1087</td>
			<td>Apparently healthy donor</td>
		</tr>
		<tr>
			<td>iCell DPN, Donor#11299, Phenotype LRRK2 G2019S, phenotype PD lot#106139</td>
			<td>Fujifilm</td>
			<td>C1149</td>
			<td>Donor carrying LRRK2 G2019S mutation&#160;</td>
		</tr>
		<tr>
			<td>iCell Nervous System Supplement</td>
			<td>Fujifilm</td>
			<td>M1031</td>
			<td>Supplement for base medium</td>
		</tr>
		<tr>
			<td>iCell Neural Supplement B</td>
			<td>Fujifilm</td>
			<td>M1029</td>
			<td>Supplement for base medium</td>
		</tr>
		<tr>
			<td>Jupyter Python Notebook</td>
			<td>In-house development</td>
			<td>https://github.com/Ksilink/Notebooks/tree/main/Neuro/DopaNeuronProfiling</td>
			<td>Notebook to perform phenotypic profile visualization and classification from raw data.</td>
		</tr>
		<tr>
			<td>Laminin</td>
			<td>Biolamina</td>
			<td>LN521</td>
			<td>Plate coating</td>
		</tr>
		<tr>
			<td>PFE-360</td>
			<td>MedChemExpress</td>
			<td>HY-120085</td>
			<td>LRRK2 kinase inhibitor</td>
		</tr>
		<tr>
			<td>PhenoLink</td>
			<td>In-house development</td>
			<td>https://github.com/Ksilink/PhenoLink</td>
			<td>Software for image analysis</td>
		</tr>
		<tr>
			<td>PhenoPlate 384w, PDL coated</td>
			<td>Perkin Elmer</td>
			<td>6057500</td>
			<td>Pre-coated plate for cell culture and imaging. This plate allows imaging of all wells using all objectives of the Yokogawa CV7000 microscope.</td>
		</tr>
		<tr>
			<td>Storage plates Abgene 120 &#181;L</td>
			<td>Thermo Scientific</td>
			<td>AB-0781</td>
			<td>Necessary for compound dispensing using the Vprep pipetting system. If not available, the use of an electronic multichannel pipette is recommended.</td>
		</tr>
		<tr>
			<td>Triton</td>
			<td>Sigma</td>
			<td>T9284</td>
			<td>Permeabilization before lysis</td>
		</tr>
		<tr>
			<td>Trypan Blue</td>
			<td>Sigma</td>
			<td>T8154-20ML</td>
			<td>Determination of living cells</td>
		</tr>
		<tr>
			<td>Vprep Pipetting System&#160;</td>
			<td>Agilent</td>
			<td></td>
			<td>Medium change and compound dispensing. Alternatively, an electronic multichannel pipette can be used.</td>
		</tr>
	</tbody>
</table>

phenotypic profiling of human stem cell-derived midbrain dopaminergic neurons

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。

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

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...

Watch this Scientific Journal Video about Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons at JoVE.com

Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons (Video) | JoVE

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。

人类干细胞来源的中脑多巴胺能神经元的表型分析

Parkinson&#39;s disease (PD) is linked to a range of cell biological processes that cause midbrain dopaminergic (mDA) neuron loss. Many current in vitro ...

我们构建了分化的人类细胞（如神经元）的表型图谱。我们的目标是更好地了解化合物处理对细胞的整体影响。表型谱可以为进一步的机理研究提供较少的偏倚切入点。用于区分人类多巴胺能神经元的方案的质量已显着提高，从而能够产生大批量的均质神经元。此外，使用实验室自动化来处理分化细胞在较长时间内的可重复性的情况也在增加，当然，基于成像的数据的利用也变得更快、更详细。一个很大的挑战是确保表型图谱既可量化又可重复。为了实现这一点，生产均匀的神经元批次并确定适合自动化的方案至关重要。然而，对于自动化协议，总是需要在技术可行性和生理相关性之间进行权衡。在这里，我们找到了一个很好的权衡。我们开发了一种即用型解决方案，使用成像方法彻底表征人类多巴胺能神经元。自动化协议以及图像和数据分析工作流程将使更多的科学家能够创建神经元表型图谱。我们想更好地了解帕金森病的单基因和特发性形式的表型特征有何不同。我们还想探索当多巴胺能神经元与其他脑细胞类型（例如小胶质细胞）相互作用时表型方面的变化。