The authors would like to thank all colleagues at Ksilink for their valuable help and discussions that lead to the design of the presented protocol.

1. Preparation of medium and plates for neuron seeding (Day 1)
<ol>
	<li>To prepare the plates for neuron seeding on Day-1, warm Laminin to room temperature (RT) just before use. Prepare the Laminin solution by diluting the Laminin stock solution (0.1 mg/mL) 1/10 in cold PBS+/+ (with Ca2+ and Mg2+). 
	NOTE: All reagents are listed in the Table of Materials. The compositions of solutions and buffers are described in Tables 1-4.</li>
	<li>Then,...

Culturing Human Midbrain Dopaminergic Neurons for Phenotypic Profiling

<ol>
	<li>Panicker, N., Ge, P., Dawson, V. L., Dawson, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cell+biology+of+Parkinson's+disease.">The cell biology of Parkinson's disease.</a> The Journal of Cell Biology. 220 (4), 202012095 (2021).</li><li>Caicedo, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data-analysis+strategies+for+image-based+cell+profiling.">Data-analysis strategies for image-based cell profiling.</a> Nature Methods. 14 (9), 849-863 (2017).</li><li>Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image-based+profiling+for+drug+discovery:+due+for+a+machine-learning+upgrade.">Image-based profiling for drug discovery: due for a machine-learning upgrade.</a> Nature Reviews Drug Discovery. 20 (2), 145-159 (2021).</li><li>Bray, M. -. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+Painting,+a+high-content+image-based+assay+for+morphological+profiling+using+multiplexed+fluorescent+dyes.">Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes.</a> Nature Protocols. 11 (9), 1757-1774 (2016).</li><li>Schiff, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+deep+learning+and+unbiased+automated+high-content+screening+to+identify+complex+disease+signatures+in+human+fibroblasts.">Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.</a> Nature Communications. 13 (1), 1590 (2022).</li><li>Ziegler, S., Sievers, S., Waldmann, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphological+profiling+of+small+molecules.">Morphological profiling of small molecules.</a> Cell Chemical Biology. 28 (3), 300-319 (2021).</li><li>Cobb, M. M., Ravisankar, A., Skibinski, G., Finkbeiner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=iPS+cells+in+the+study+of+PD+molecular+pathogenesis.">iPS cells in the study of PD molecular pathogenesis.</a> Cell and Tissue Research. 373 (1), 61-77 (2018).</li><li>Elitt, M. S., Barbar, L., Tesar, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+screening+for+human+genetic+diseases+using+iPSC+models.">Drug screening for human genetic diseases using iPSC models.</a> Human Molecular Genetics. 27 (R2), 89-98 (2018).</li><li>Vuidel, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-content+phenotyping+of+Parkinson's+disease+patient+stem+cell-derived+midbrain+dopaminergic+neurons+using+machine+learning+classification.">High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification.</a> Stem Cell Reports. 17 (10), 2349-2364 (2022).</li><li>Stirling, D. R., Swain-Bowden, M. J., Lucas, A. M., Carpenter, A. E., Cimini, B. A., Goodman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellProfiler+4:+improvements+in+speed,+utility+and+usability.">CellProfiler 4: improvements in speed, utility and usability.</a> BMC Bioinformatics. 22 (1), 433 (2021).</li><li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Sofroniew, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> napari: a multi-dimensional image viewer for Python. , (2022).</li><li>Berthold, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KNIME:+The+Konstanz+Information+Miner.">KNIME: The Konstanz Information Miner.</a> Data Analysis, Machine Learning and Applications. , 319-326 (2008).</li><li>Fathi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diverging+Parkinson's+Disease+Pathology+between+patient-derived+GBAN370S,+LRRK2G2019S+and+engineered+SNCAA53T+iPSC-derived+Dopaminergic+Neurons.">Diverging Parkinson's Disease Pathology between patient-derived GBAN370S, LRRK2G2019S and engineered SNCAA53T iPSC-derived Dopaminergic Neurons.</a> bioRxiv. , (2023).</li><li>Wang, Y., Huang, H., Rudin, C., Shaposhnik, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+How+Dimension+Reduction+Tools+Work:+An+Empirical+Approach+to+Deciphering+t-SNE,+UMAP,+TriMap,+and+PaCMAP+for+Data+Visualization.">Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization.</a> Journal of Machine Learning Research. 22 (201), 1-73 (2021).</li><li>Ke, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LightGBM:+A+highly+efficient+gradient+boosting+decision+tree.">LightGBM: A highly efficient gradient boosting decision tree.</a> Advances in Neural Information Processing Systems. 30, (2017).</li><li>Avazzadeh, S., Baena, J. M., Keighron, C., Feller-Sanchez, Y., Quinlan, L. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+Parkinson's+Disease:+iPSCs+towards+Better+Understanding+of+Human+Pathology.">Modelling Parkinson's Disease: iPSCs towards Better Understanding of Human Pathology.</a> Brain Sciences. 11 (3), 373 (2021).</li><li>S&#225;nchez-Dan&#233;s, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disease-specific+phenotypes+in+dopamine+neurons+from+human+iPS-based+models+of+genetic+and+sporadic+Parkinson's+disease.">Disease-specific phenotypes in dopamine neurons from human iPS-based models of genetic and sporadic Parkinson's disease.</a> EMBO Molecular Medicine. 4 (5), 380-395 (2012).</li><li>Oosterveen, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pluripotent+stem+cell+derived+dopaminergic+subpopulations+model+the+selective+neuron+degeneration+in+Parkinson's+disease.">Pluripotent stem cell derived dopaminergic subpopulations model the selective neuron degeneration in Parkinson's disease.</a> Stem Cell Reports. 16 (11), 2718-2735 (2021).</li><li>Hughes, R. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiparametric+high-content+cell+painting+identifies+copper+ionophores+as+selective+modulators+of+esophageal+cancer+phenotypes.">Multiparametric high-content cell painting identifies copper ionophores as selective modulators of esophageal cancer phenotypes.</a> ACS Chemical Biology. 17 (7), 1876-1889 (2022).</li><li>Akbarzadeh, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphological+profiling+by+means+of+the+Cell+Painting+assay+enables+identification+of+tubulin-targeting+compounds.">Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds.</a> Cell Chemical Biology. 29 (6), 1053-1064 (2022).</li><li>Schiff, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+deep+learning+and+unbiased+automated+high-content+screening+to+identify+complex+disease+signatures+in+human+fibroblasts.">Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.</a> Nature Communications. 13 (1), 1590 (2022).</li><li>Way, G. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Morphology+and+gene+expression+profiling+provide+complementary+information+for+mapping+cell+state.">Morphology and gene expression profiling provide complementary information for mapping cell state.</a> Cell Systems. 13 (11), 911-923 (2022).</li><li>Feng, Y., Mitchison, T. J., Bender, A., Young, D. W., Tallarico, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-parameter+phenotypic+profiling:+using+cellular+effects+to+characterize+small-molecule+compounds.">Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds.</a> Nature Reviews Drug Discovery. 8 (7), 567-578 (2009).</li><li>Antonov, S. A., Novosadova, E. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+state-of-the-art+and+unresolved+problems+in+using+human+induced+pluripotent+stem+cell-derived+dopamine+neurons+for+parkinson's+disease+drug+development.">Current state-of-the-art and unresolved problems in using human induced pluripotent stem cell-derived dopamine neurons for parkinson's disease drug development.</a> International Journal of Molecular Sciences. 22 (7), 3381 (2021).</li><li>Miller, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+iPSC-based+modeling+of+late-onset+disease+via+progerin-induced+aging.">Human iPSC-based modeling of late-onset disease via progerin-induced aging.</a> Cell Stem Cell. 13 (6), 691-705 (2013).</li><li>Bezard, E., Gross, C. E., Brotchie, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Presymptomatic+compensation+in+Parkinson's+disease+is+not+dopamine-mediated.">Presymptomatic compensation in Parkinson's disease is not dopamine-mediated.</a> Trends in Neurosciences. 26 (4), 215-221 (2003).</li><li>Wu, Y., Le, W., Jankovic, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preclinical+Biomarkers+of+parkinson+disease.">Preclinical Biomarkers of parkinson disease.</a> Archives of Neurology. 68 (1), 22-30 (2011).</li><li>Verstraelen, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+quantification+of+synapses+in+primary+neuronal+culture.">Systematic quantification of synapses in primary neuronal culture.</a> iScience. 23 (9), 101542 (2020).</li><li>Liu-Yesucevitz, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ALS-Linked+mutations+enlarge+TDP-43-enriched+neuronal+RNA+granules+in+the+dendritic+arbor.">ALS-Linked mutations enlarge TDP-43-enriched neuronal RNA granules in the dendritic arbor.</a> The Journal of Neuroscience. 34 (12), 4167-4174 (2014).</li></ol>

All authors are employed by Ksilink.

Phenotypic profiling is a technique to measure a large number of phenotypes in cells by applying fluorescent stainings, microscopy, and image analysis3. Phenotypic profiles can be obtained and compared across cell lines or other experimental conditions to understand complex changes in cellular biology that might go unnoticed when using a single readout. Here we describe the application of phenotypic profiling to human iPSC-derived mDA neurons, a cell type frequently used to model PD cellular biolo...

A variety of cell biological processes are disturbed in Parkinson&#39;s disease (PD). For example, mitochondrial dysfunction, oxidative stress, protein degradation defects, disruption of vesicular trafficking and endolysosomal function have been associated with midbrain dopaminergic (mDA) neuron loss, are commonly observed in PD1. Therefore, PD appears to involve multiple disease mechanisms that can interact with and worsen each other. One useful way to investigate this mechanistic interplay is the creation of a comprehensive phenotypic fingerprint or profile of midbrain dopaminergic (mDA) neurons.
Phenotypic profiling is an approach that involves creating a profile of a sample based on a collection of measurable characteristics, and second, it involves making predictions about a sample based on this profile2,3. The goal of profiling is to capture a diverse range of features, some of which may not have been previously associated with a disease or treatment3. As a result, profiling can reveal unexpected biological processes. Phenotypic profiling typically relies on fluorescently stained cells, and standardized assays, such as Cell Painting, have been developed to create phenotypic profiles4. Recently, phenotypic profiling has, for example, been applied for the characterization of small molecules or the accurate prediction of PD-subtypes solely based on patient-derived fibroblasts5,6. Despite these advances, phenotypic profiling has rarely been applied to post-mitotic differentiated cells, such as human induced pluripotent stem cell (iPSC)-derived mDA neurons that express PD-linked mutations such as LRRK2 G2019S. Significant challenges of iPSC-derived models include the presence of subtle or variable pathological features across differentiation batches or genotypes, and the fact that isolated PD phenotypes do not capture the full complexity of the disease. Furthermore, while iPSC neuronal models are physiologically relevant, they are rarely used in PD drug discovery processes due to concerns about technical complexity7,8.
We previously developed a robust methodology to measure multiple PD-related pathophysiological phenotypes in human mDA neurons that are both sensitive to genetic and chemical compound-induced phenotypic changes9. This article describes in detail a further optimized version of this methodology to create phenotypic profiles from mDA neurons (Figure 1). This protocol has several advantages over the previously described phenotypic profiling approaches, such as the use of high-quality mDA neurons and technical reproducibility. For the first time, this protocol enables phenotypic profiling in physiologically relevant post-mitotic mDA neurons after chemical perturbations in a highly scalable fashion. Fully differentiated and cryopreserved mDA neurons are commercially available, significantly decreasing batch-to-batch differentiation variability. Secondly, technical variability can be further reduced by using a well-defined experimental design (i.e., culture duration or avoiding edge wells), automated liquid handling and automated microscopy. Additionally, the initial steps of phenotypic profile analysis using unsupervised clustering or supervised classification approaches are outlined here, indicating how phenotypic profiling data can be analyzed. This protocol will be of use for researchers interested in phenotypic changes of mDA neurons induced by genetic or chemical perturbations, specifically when a highly scalable study setup is required, for example, during screening campaigns or when the effects of a smaller number of compounds are to be studied, for example, to determine toxic effects. In summary, it is anticipated that the application of phenotypic profiling of human neurons is a valuable technique to study complex disease-related phenotypes and characterize the cellular effects of drug candidates.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65570/65570fig01.jpg" /> 
Figure 1: Schematic depiction of the experimental protocol to generate image-based phenotypic profiles from human iPSC-derived mDA neurons. <a href="https://www.jove.com/files/ftp_upload/65570/65570fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</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

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 PD cellular models lack complexity and do not take multiple phenotypes into account. Phenotypic profiling in human induced pluripotent stem cell (iPSC)-derived mDA neurons can address these shortcomings by simultaneously measuring a range of neuronal phenotypes in a PD-relevant cell type in parallel. Here, we describe a protocol to obtain and analyze phenotypic profiles from commercially available human mDA neurons. A neuron-specific fluorescent staining panel is used to visualize the nuclear, &#945;-synuclein, Tyrosine hydroxylase (TH), and Microtubule-associated protein 2 (MAP2) related phenotypes. The described phenotypic profiling protocol is scalable as it uses 384-well plates, automatic liquid handling and high-throughput microscopy. The utility of the protocol is exemplified using healthy donor mDA neurons and mDA neurons carrying the PD-linked G2019S mutation in the Leucine-rich repeat kinase 2 (LRRK2) gene. Both cell lines were treated with the LRRK2 kinase inhibitor PFE-360 and phenotypic changes were measured. Additionally, we demonstrate how multidimensional phenotypic profiles can be analyzed using clustering or machine learning-driven supervised classification methods. The described protocol will particularly interest researchers working on neuronal disease modeling or studying chemical compound effects in human neurons.

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons 

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

We construct phenotypic profiles of differentiated human cells such as neurons. Our goal is to better understand the overall effects of chemical compound treatments on the cells. Phenotypic profiles can suggest less biased entry points for further mechanistic studies.The quality of protocols for differentiating human dopaminergic neurons has significantly improved, enabling the production of large batches of homogeneous neurons. Additionally, there is a rise in the use of lab automation for handling differentiated cells'reproducibility over extended periods, and of course, the exploitation of imaging-based data has become faster and more detailed. A big challenge is to ensure that phenotypic profiles are both quantifiable and reproducible.To achieve this, it is crucial to produce homogeneous neuron batches and identify an automation-suited protocol. However, with an automated protocols, there is always a trade-off between technical feasibility and physiological relevance. Here, we found a good trade-off.We developed a ready-to-use solution to thoroughly characterize human dopaminergic neurons using an imaging approach. The automated protocol, along with the image and data analysis workflow, will equip more scientists to create neuronal phenotypic profiles. We would like to better understand how phenotypic profiles differ between monogenetic and idiopathic forms of Parkinson's disease.We would also like to explore the changes in phenotypic aspects when dopaminergic neurons interact with other brain cell types such as microglia, for example.

Phenotypic profiling in mDA neurons is an efficient way to quantify multiple aspects of cellular biology and their changes during the experimental modulation. To exemplify this methodology, this study made use of cryopreserved LRRK2 G2019S and healthy donor mDA neurons. These neurons have been differentiated for approximately 37 days, are post-mitotic and express neuronal markers (TUBB3 and MAP2) and dopaminergic neuron markers, including tyrosine hydroxylase (TH) in combination with FOXA2, while the glial marker Glial F...

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

This protocol describes the cell culturing of human midbrain dopaminergic neurons, followed by immunological staining and the generation of neuronal phenotypic profiles from acquired microscopic high-content images allowing the identification of phenotypic variations due to genetic or chemical modulations.

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 ...

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Neuroscience

Thawing Human iPSC-Derived mDA Neurons for Seeding

To thaw the neurons on the day of seeding, prewarm the water bath to 37 degrees Celsius. Also, equilibrate the complete maintenance media to room temperature while protecting it from light. Meanwhile, remove the vial containing the commercially-obtained frozen neurons from the liquid nitrogen tank and transfer it to dry ice.Then place the vial in the water bath at 37 degrees Celsius for two minutes to ensure complete thawing. Following which, disinfect the vial using 70%ethanol. Using a P1000 pipette, transfer the approximately 370 microliters of thawed neurons from the vial to a 50-milliliter centrifuge tube.Rinse the empty vial with 630 microliters of complete maintenance medium. And using a pipette, dispense the medium dropwise into the 50-milliliter tube at a 45-degree angle. In a similar manner, using a P1000 pipette, add one milliliter of complete maintenance medium to the 50-milliliter centrifuge tube.Then slowly add an additional two milliliters of complete maintenance medium into the tube. Next, mix 10 microliters of trypan blue with 10 microliters of the cell suspension in a micro tube. Then add 10 microliters of the mixture to a counting chamber slide for counting cells.Following cell counting and transferring the neurons to a 15-milliliter tube, centrifuge at 400g for five minutes at room temperature. Subsequently, remove the supernatant. Using a P1000 pipette, carefully resuspend the neuron pellet in one milliliter of complete maintenance medium.Finally, add the necessary volume of medium to achieve the desired concentration for seeding the neurons on the same day.

Human iPSC-Derived mDA Neurons Seeding and Compound Treatment for Phenotypic Profiling

To begin, take the Poly-D-Lysine pre-coated 384-well plate containing 25 microliters of laminin per well out of the refrigerator and equilibrate the plate to room temperature for about 30 minutes under the cell culture hood. Just before seeding, aspirate 15 microliters of coating solution from each well using an automated liquid handler or a 16-channel pipette. Then dispense 50 microliters of cell solution containing 300, 000 neurons per milliliter into each well.Avoid filling cells in columns 1, 2, 23, and 24, and rows A, B, O, and P to minimize possible edge effects. Fill those unused wells with 80 microliters of phosphate-buffered saline. Incubate the plate at 37 degrees Celsius under 5%carbon dioxide.Prewarm an appropriate volume of complete maintenance medium at room temperature and away from light. Prepare a 1.5 times concentrated compound solution for all desired concentrations to be tested, using complete maintenance medium for dilutions. Using an automatic pipetting system, discard 40 microliters of medium per well from the neuron-containing plate, leaving 20 microliters of medium per well.Then add 40 microliters of the 1.5 times concentrated compound solution per well to achieve the desired final concentration. Following the desired protocol, healthy donor, or LRRK2 G2019S mutation-carrying midbrain dopaminergic neurons were successfully cultured for six days, and treated with either dimethyl sulfoxide or a LRRK2 kinase inhibitor. The neurons were stained with nuclear stain and antibodies against alpha-synuclein, tyrosine hydroxylase, and MAP2.

Image Processing of Fluorescently-Stained Human iPSC-Derived mDA Neurons for Phenotypic Profile Generation

To process the images of the fluorescently stained neurons acquired using a confocal fluorescence microscope use phenol link software for image segmentation and feature extraction. In the software load the plate of choice to access the data and select a desired image. To perform image segmentation on the illumination corrected raw images, adjust the fluorescent intensities to visualize the different channels.Empirically determine the respective channel intensity thresholds per plate to ensure the desired segmented signal corresponds to the signal in the raw image and minimize the background signal. To separate living from dead cells, define the nucleus size and intensity thresholds. Measure the size of the nucleus by double clicking and visualize the displayed intensity.When using 40 X images, maintain default parameters and run the processing. With the resulting tabular quantitative data construct phenotypic profiles to compare different cell lines or treatment conditions. Execute the Jupyter notebook cell by cell, using the Jupyter software for phenotypic profile generation and visualization.Images were segmented and phenotypic features were extracted. A total of 126 quantitative features that could be aggregated into well-based phenotypic profiles were determined. Some features showed changes upon compound treatment.For example, the alpha synuclein fluorescence intensity in tyrosine hydroxylase, or TH positive cells, decreased upon treatment with the LARK2 kinase inhibitor PFE-360. Other features, like the MAP2 neurite network length, or the ratio of TH positive neurons, presented differences only between the tested cell lines, but not upon compound treatment.

976 Views.  Ksilink.  This protocol describes the cell culturing of human midbrain dopaminergic neurons, followed by immunological staining and the generation of neuronal phenotypic profiles from acquired microscopic high-content images allowing the identification of phenotypic variations due to genetic or chemical modulations.

Culturing Human Midbrain Dopaminergic Neurons for Phenotypic Profiling

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