Spreading Lewy pathology, of which pS129-αsyn is a major constituent, is a histopathological hallmark of PD. Stopping or slowing down the accumulation of aggregated pS129-αsyn may slow down the degeneration of dopamine neurons and the progression of alpha-synucleinopathy. However, a mechanistic understanding of how pS129-αsyn aggregation contributes to the demise of dopamine neurons still has to be established. Evidence from human postmortem studies on brain samples from patients at different stages of the disease as well as observation of pS129-αsyn positive inclusion in transplanted fetal neurons strongly suggests the spreading of Lewy pathology between cells16,17,33. Consequently, prion-like spreading of pS129-αsyn was recently recapitulated by using α-synuclein PFFs9,10. Establishing a robust, cost-effective, and relatively high- or medium-throughput model of pS129-αsyn spreading and accumulation, specifically in dopamine neurons, can considerably speed up the search for novel treatments and compounds modifying this process.
Because loss of dopamine neurons is the main cause of motor symptoms in PD and these cells possess many unique properties2,34,35, modeling of prion-like spreading of pS129-αsyn in dopamine neurons is the most relevant type of model from the translational perspective. Protocols utilizing micro island cultures of embryonic midbrain neurons on 4 well plates and semiautomatic quantification have been described previously18. The protocol described here was adapted to 96 well plates and provides less laborious preparation of micro islands, allowing for the preparation of up to four plates containing 60 wells each by an experienced researcher during one workday. Culturing dopamine neurons in 96 well plates allows for testing drugs at lower amounts and enables high transduction rates with lentivirus vectors. It is also possible to combine different treatments to perform more complex experiments.
Before applying any treatments (including PFFs), the quality of the culturing should be checked with bright-field microscopy. If the microscopy system does not utilize a CO2 chamber with heating, the cells should not be kept outside of the incubator for more than couple of minutes, because primary mouse dopamine neurons are delicate and easily stressed. For the same reason, it is advised that the first imaging should be done after 24 h of incubation (between DIV1-DIV3). The cells should appear to be alive with present cell bodies and homogenously spread inside the micro island. Primary neurons would have settled on the PO-coated ground and started to establish neuronal projections. It is possible to observe small clump of cells (i.e., diameter smaller than 150–200 μm) that can be formed if the trituration process is not done properly or plating density is higher than recommended. These small clumps would not affect the experiment, unless they are more than a few per well and/or larger. Clumped cells make it very difficult to identify immunohistochemical markers and individual cells during the image analysis. It is essential to avoid such clumps by careful coating, triturating, and controlling plating density. If the uniformity of these conditions cannot be observed at certain wells, do not include these defective wells in the experiment. Such exclusion should be done before the execution of any treatments.
Moreover, utilization of 96 well plates allows for convenient multichannel pipette use during staining procedures and direct visualization with automatic plate microscopes, further increasing throughput. Utilization of automated image quantification is indispensable for the analysis of the data from high-content imaging platforms. In addition to the capability to process thousands of images obtained from each experiment, it ensures unbiased, identical quantification of all treatment groups. The workflow proposed for the image analysis is based on simple principles of segmenting dopamine neurons, filtering correctly segmented cells by supervised machine learning, and subsequently quantification of phenotypes (pS129-αsyn positive and pS129-αsyn negative), again by supervised machine learning. Although several different approaches for this task can be envisioned, we have found the combination of segmentation with machine learning to be the most robust for dopamine cultures due to high plating density, the diverse shapes of dopamine neurons, and the presence of strongly stained neurites. The proposed image analysis algorithm was implemented in CellProfiler and CellProfiler Analyst, open source, freely available high-content image analysis software26,27. The algorithm could also be implemented with other image analysis software, either open source (e.g., ImageJ/FIJI, KNIME) or proprietary. However, in our experience these often sacrifice customization capabilities for ease of use, and therefore might not perform well in complicated analyses. We have found that the CellProfiler and CellProfiler Analyst software packages give particularly reliable results by combining a substantial number of implemented algorithms, extreme flexibility in designing workflow, and simultaneously handling and efficiently processing of high-content imaging data.
The described protocol could also be adapted for quantification of other cellular phenotypes characterized by immunostaining with different antibodies, such as markers of other neuronal populations (e.g., DAT, GAD67, 5-HT etc.) and protein aggregates (e.g., phospo-Tau, ubiquitin). Multiple fluorescent markers could also be combined to distinguish multiple phenotypes (e.g., cells with inclusions at different stages of maturation). Automated classification of multiple phenotypes should also be easy to implement in the described image analysis pipelines by merely adding a channel containing immunofluorescent images of additional markers to measurement steps and sorting cells into multiple bins. Utilization of multiple markers at the same time would, however, require the optimization of immunostaining and imaging conditions. Additionally, for better quality in immunofluorescence imaging, the use of special black-walled 96 well plates explicitly designed for the fluorescent microscope is recommended. However, these can be considerably more expensive than standard cell culture plates, which are sufficient for the analysis described in our protocol.
The type and quality of utilized PFFs are critical for the outcome of the experiments. PFFs can both affect the robustness of the assay and the interpretation of results. Preparation conditions might affect the seeding efficiency of PFFs and, indeed, PFF "strains" with different physiological properties have been reported36. Nonetheless, the preparation and validation of PFFs are beyond the scope of this article and have been described in several publications11,28,29,30. In addition to the preparation protocol, the species of origin of α-synuclein in PFFs (e.g., mouse, human) and the usage of wild type or mutated protein (e.g., human A53T α-synuclein) should be considered, depending on the particular experimental conditions. Induction of pS129-αsyn accumulation by PFFs was shown to be dependent on age of the culture (i.e., days in vitro), with more mature cultures showing more pronounced induction11. This is probably due to the increased number of neuronal connections in more mature cultures, and increased α-synuclein protein levels. In our hands, treatment with PFFs at DIV8 gave the most robust results, with pronounced accumulation of pS129-αsyn in dopamine neuron soma, while not compromising neuronal survival. The described protocol is well suited to study treatments modifying early events leading to aggregation of endogenous α-synuclein because we quantify pS129-αsyn positive inclusions at a relatively early time point, 7 days after inoculation with PFFs. At this time point, intrasomal inclusions are present in a significant fraction of cells and can be easily distinguished by immunostaining while no PFF-induced cell death is observed, simplifying the interpretation of the results. Importantly, as the morphology and composition of PFF-induced inclusions can change over time12,13, the described protocol could, in principle, be modified to study more mature inclusions by fixation and immunostaining at later time points. However, keeping dopamine neurons in culture for longer than 15 days requires extreme care, and might induce additional variation because of cells failing to survive independently from PFF inoculation. Additionally, more extended cultures complicate drug treatment schedules. Many compounds have limited or not poorly characterized stability in the cell culture medium, and replenishment of a drug is not trivial because complete exchange of medium compromises the survival of the dopamine cultures.
Phosphorylation of α-synuclein at Ser129 is consistently reported in PFF-based models of α-synuclein aggregation and colocalizes with markers of misfolding and aggregation such as Thioflavin S, ubiquitin, or conformation-specific antibodies11,12. In our hands, immunostaining for pS129-αsyn also gives the strongest signal with the lowest background and is most straightforward to analyze, giving robust results when multiple treatments are screened. Importantly, immunostaining with pS129-αsyn antibody does not detect PFFs that remain outside of the cells, significantly reducing the background. However, it is important to remember that Ser129 phosphorylation is probably one of the earliest processes linked with the misfolding of α-synuclein and might be differently regulated under specific conditions. Therefore, any findings that show positive effects on pS129-αsyn should be confirmed by other markers.
Statistical analysis should be tailored correspondingly to experimental design. It is essential to perform experiments in at least three independent biological replicates (i.e., separate primary neuronal cultures). These replicates should be plated on different plates and treated independently. We analyze the data obtained from replicates on different plates with random block design ANOVA37 to take into account the pairing of data for different experimental plates.