Name: Image Processing of Fluorescently-Stained Human iPSC-Derived mDA Neurons for Phenotypic Profile Generation
Uploaded: 2023-07-07T14:00:00
Description: Watch this Scientific Journal Video about Image Processing of Fluorescently-Stained Human iPSC-Derived mDA Neurons for Phenotypic Profile Generation at JoVE.com

image processing of fluorescently-stained human ipsc-derived mda neurons for phenotypic profile generation

Culturing Human Midbrain Dopaminergic Neurons for Phenotypic Profiling

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>

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.

Watch this Scientific Journal Video about Image Processing of Fluorescently-Stained Human iPSC-Derived mDA Neurons for Phenotypic Profile Generation at JoVE.com

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.

Research

JoVE Journal

Neuroscience

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 ...