Raising the Bar with High Content Analysis

A closer look at future-proof solutions for high content biology projects

Imagine you can visualize, analyze, and quantify intracellular processes in complex models like organoids not only in 3D, but also over time. With an advanced software tool with integrated AI solutions, high content image analysis in 4D is now at our fingertips, keeping imaging-based research at the forefront of innovation. This technology supports drug discovery by providing better, more relevant understanding of efficacy and safety behavior of drug candidates in in vitro settings and has the potential to boost development of new alternative methods (NAMs).

Recently, Charles River deployed a new data-agnostic image analysis tool ZEISS Arivis, which specializes in in vitro assay development and high-throughput screening (HTS) for throughout different phases of the drug discovery pipeline.

Here to explain for readers of Eureka how this new analytical tool works is Marta da Silva, PhD, a neurobiologist and senior scientist.

How does the ZEISS Arivis tool work and is it one of the most advanced tools in high-content biology right now?

Marta: Yes, Arivis is one of the most advanced software tools that can be used to analyze and visualize complex biological data generated via high content biology techniques. Like other image analysis software tools, Arivis allows assessment and quantification of changes in intricate biological processes (such as cell morphology, protein expressions, protein localization and cellular interactions) over time or upon treatment with modalities. However, Arivis stands out from other high content imaging software due to several key features. It allows for analysis and visualization in four dimensions, enabling complex dynamic analysis in three dimensions over time.

Why do we need these new analysis capabilities?

Marta: Drug compounds are now more tailored to human disease-relevant targets. More than ever companies need to test their modalities in more physiologically relevant and more complex in vitro models. This is because these models mimic the human tissue-like structures and functions more effectively compared to monolayer cultures. But the use of these complex in vitro models (including co-culture models, 3D models, cell tracing over time and cell therapies) means we need to improve the way we capture and analyze data.

The complexity of image analysis read-outs is no longer manageable using the standard image analysis software. Creation of complex workflows (algorithms or pipelines) to analyze the data are needed. Software like Arivis allows for this.

Can you explain in more detail what capabilities drug developers gain from using these analytical tools?

Marta: Drug developers get better, more relevant understanding of efficacy and safety behavior of their drug modality. For instance, the new software has also been useful in analyzing our iPSC-derived gut organoids, miniaturized in vitro models that we developed to assess compounds for antiviral efficacy and toxicity. When assessing known antiviral compounds that lacked efficacy in late clinical trials with these gut-organoids, we discovered that these compounds did pass the tests for cytotoxicity and that viral genes were markedly decrease. With only these relatively simple read-outs the compounds would have moved on to the next phase of drug assessment. However, when we assessed the viral loads by immunocytochemistry and used the new software for segmentation of the cells and their viral loads, we observed that the compound was not effective in clearing the virus. It wasn't ready to move on at all! Extracting this last bit of data out of the models helped the developer make a better decision to further optimize their compound.

Figure 2. Assessment of viral loads in hIPSC-derived intestinal organoids (HIOs). Organoids infected with EV-A71 virus were labeled with nuclear marker DAPI (in blue), F-actin marker Phalloidin (in orange) and dsRNA marker (in green). Images were acquired with a high content imager and Arivis was used to color code segmented each organoid (center image). Quantitative analysis of EV-A71 infection levels, as determined by the number of dsRNA puctae per single organoid, were obtained (left image).

Another example reflects how this new analysis tool enhances capabilities within not only three-dimensional models but also in more complex, two-dimensional co-culture models. Hallmarks of pathogenesis of common neurodegenerative diseases such as multiple sclerosis (MS) and Alzheimer's disease (AD) are oligodendrocyte dysfunction and disruption of myelination of axons. Oligodendrocytes, the cells responsible for myelination, wrap their cell membrane around axons to support nerve impulse conduction. With Arivis, our biotherapeutics team in Leiden managed to quantify oligodendrocytes cells and myelination of neurites in a newly established co-culture model. This advanced capability provides our clients with a key tool to study one of the major hallmarks of two very common neurological disorders.

Figure 3. Myelination-Neurite/axonal wrapping in ioGlutamatergic Neuron and ioOligodendrocyte co-culture. Cocultures were stained with immunocytochemical technologies for relevant and specific neuronal markers Neurofilament H (NFH, in red) and oligodendrocyte marker MBP (in green). DAPI was used to identify cell nuclei. A representative image of a co-culture is shown (cropped) including the processes of segmentation with Arivis that are needed for assessment of axonal wrapping. Quantification of the intersection of MBP and NFH signals in day 14 (graph) show increased myelination (wrapping) upon treatment with a reference compound Tasin1, known to enhance oligodendrocyte function.

Our lab was also recently asked to study and optimize human embryonic stem cells differentiated in vitro to an immature neuronal phenotype before added to a cell delivery medical device. The device is used to mimic and replace a lost neuronal connection in brain tissue. This request was difficult as it involved implementation of image analysis read-outs in a three-dimensional setting of a dense network of neurites. With the conventional image analysis software, we were struggling to deliver a read-out.

However, with this new analysis tool we were able to provide a read-out by applying machine learning technology embedded in the Arivis image analysis software. We were able to train the program to identify, segment and measure the neurite structures within the device. Also, by staining these neurites for relevant and specific neuronal markers via immunocytochemistry technologies, the new analysis tool provided the client with a whole mount - a staining technique used to visualize the entire sample in its natural 3-D configuration-of the neurite outgrowth.

Figure 3. Innervated Scaffold - Neurite outgrowth within a medical device can be tracked via high content imaging in a live-cell setting by labeling the cells with a Calcein-AM probe. With Arivis, neurites can be identified, and features of these neurites (like length, density, and branching) can be analyzed.

Figure 1: Movie at top of story depicts 3D representation of neural outgrowth in a medical delivery device. Neurons were stained with immunocytochemical technologies for relevant and specific neuronal markers βIIITubulin (in red) and Tyrosine Hydroxylase (in green). Images were acquired with a high content imager and whole mount 3D rendering stack was obtained with Arivis to visualize the natural 3-D configuration of the neurite outgrowth.

So, these analytical tools are obviously exciting? Do they also help companies reduce their reliance on animals? If so, how?

Marta: Yes, complex in vitro models combined with an advanced imaging process tool can significantly contribute to reducing the use of animals. By increasing data efficiency, we can provide our clients with more detailed insights of data from fewer experiments. Advanced imaging technology, like Arivis, can enhance the use of more biologically relevant complex in vitro models. This increase in predictive models allow for better understanding of relevant biological processes without the need for and the reliance on animal testing.

Images and movie courtesy of Charles River's Leiden team

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