Human biology is complex, and the complicated machinery behind many biological processes and diseases has not been fully uncovered. Because 2D models do not recapitulate this complexity, they can only help us scratch the surface. The lack of robust tools to predict the efficacy and safety of drugs is one of the reasons why over 90% of current drug candidates fail clinical trials (1). 3D imaging, by providing more relevant and accurate models, offers the potential to improve this – however, 3D imaging also brings with it its own unique challenges that must be overcome for optimum success.

In a new podcast with Drug Target Review, Dr. Hilary Sherman (Senior Applications Scientist at Corning Life Sciences) and Dr. Oksana Sirenko (Senior Manager of Assay Development at Molecular Devices) discuss how 3D imaging technologies can fill the gaps in complex cell model visualization and analysis.

Hilary and Oksana both have substantial experience developing and utilizing 3D cell models. They discuss the advantages that organoids have in emulating and predicting human biology and its responses, as well as the challenges that can come with imaging and analyzing these samples.

Why 3D over 2D?

Human tissues, organs, and tumors are not flat 2D structures but rather complex, 3D assemblages comprised of many different cell types. Additionally, vital biological processes, such as nutrient and drug uptake, pH gradients, signal transduction, waste disposal, and cell-cell interactions all occur in a 3D context. These phenomena can significantly impact cell biology and drug mechanisms of action, yet conventional 2D cell models barely, if at all, capture this complexity. Thus, drugs and treatments that may perform well in a 2D context don’t produce the same efficacy when used in vivo. This disconnect between the 2D and 3D environments partially explains why many drugs that seem promising in the lab fail in clinical trials. By constructing 3D systems, we can create more biologically relevant cell models that better mimic the effects of a drug or treatment in the 'real world'.

Advantages of organoids over other 3D cell models

Organoids also offer advantages over other commonly used 3D cell models.

Organoids differ from spheroids because they are more structured and sophisticated. Whereas spheroids comprise clusters of one or two cell types, organoids are derived from progenitor (i.e., stem) cells that differentiate into multiple cell types to create an actual organ model. The orientation of the different cell types in organoids better represents the organ in the body in terms of polarity or the lumen - the void-like center of the organ. Scientists often encapsulate organoids with extracellular matrix or collagen to better demonstrate cell-cell interactions and proper orientation. Thus, organoids are excellent for simulating the functionality of a given tissue or organ.

When it comes to representing complex human tissues and organs, organoids and organ-on-a-chip take different approaches although the end goal is similar. Organ-on-a-chip requires extensive knowledge of the organ and its environment being studied for a functional and controlled design. In contrast, organoids can be designed more easily due to the ability of stem cells to intrinsically self-organize and differentiate into functional cell types.

Can organoids be used in personalized medicine?

The features of organoids described above make them ideal candidates for personalized medicine, especially for diseases with multiple variants where there is no one medicine that works for all patients. Many cancer subtypes, such as triple-negative breast cancer, fall into this category, with the chemotherapeutic response varying considerably from patient to patient. By deriving organoids from patients’ biopsies, scientists can create Patient-Derived Organoids (PDOs), also known as tumoroids. This then enables them to evaluate the effect of various compounds upon that specific tumor type, ultimately enabling them to better predict the individual’s response to treatment.

Complexities of organoids

According to Hilary, despite standardized workflows for organoid development, many factors must be taken into consideration when working with organoids. “Working with spheroids is simple since you can utilize plates similarly to growing 2D cell cultures. When you start working with organoids, the workflow becomes much more complex due to various media formulations and the need for expensive reagents, such as growth factors.”

Another requirement is to employ Corning® Matrigel® matrix as the extracellular matrix (ECM), which is responsible for establishing the integrity or polarity of the organoid. However, it is a challenging material to work with due to its fluctuating physicochemical properties at different temperatures, which not only poses problems for liquid handling but also calls for additional optimization when imaging the 3D structure.

Each organoid type harbors unique additional complexities because of the variability of workflows. Although many workflows start by differentiating induced pluripotent cells (iPCSs) into precursor cells, the subsequent steps can differ depending on the precursor cell type (e.g., cardiac, neuronal, liver, or intestinal). For example, cerebral (brain) organoids require the transfer of iPCSs to a neuronal induction medium before transferring them to the Matrigel matrix droplets. Another example is the intestinal/rectal organoid workflow in which the rectal organoids are cultured in Gri3D® micropatterned U-bottom-shaped microwells in the hydrogel for increased complexity.

High magnification confocal images of human rectal organoids exposed to pro-inflammatory cytokines. 40X single plane images of F-actin (magenta) and DAPI (blue).

Challenges in Imaging and image analysis

In addition to the complexities of 3D cell biology, challenges can also arise when imaging these models. It can be more difficult for the staining reagents to penetrate the structure, and the lack of sufficient light infiltration and proper focus can affect accurate imaging

When working with complex 3D organoids, scientists need to account for the penetration of reagents into the cell model. That’s why additional considerations may be necessary, such as increasing the concentration of the permeabilization buffer, prolonging incubation time, or increasing antibody concentration.

Oksana emphasizes the importance of longer staining and fixing times, “While half an hour is enough for a 2D culture, we need to incubate organoids for at least two hours with the stain. Fixing also needs to be performed for at least two hours, ideally overnight.”

Imaging modality is another crucial factor, and both out of focus light and light penetration can present a challenge when imaging organoids. However, additional features can actually improve image quality. For example, water immersion technology helps increase both resolution and sensitivity at different magnification levels by placing a layer of water between the objective and the sample, ultimately producing sharper images.

Organoids are thick, so the light source needs to have sufficient illumination power to improve image resolution and minimize aberration. Yet, with standard LED light, achieving the desired brightness requires long acquisition times which can result in reduced image intensity and possible photobleaching. Advanced confocal imagers, such as ImageXpress® Confocal HT.ai High-Content Imaging System, utilizes a multichannel high-intensity laser source to increase assay sensitivity and minimize exposure times.

Lastly, reliable focus is a crucial aspect when imaging organoids to capture different levels of detail without sacrificing resolution. In addition, by combining confocal high-content imaging with robust image analysis software, researchers can generate comprehensive phenotypic profiles that include size, area, volume, density, intercellular distance, and subcellular structures.

Another significant challenge during the image analysis phase is the complexity and heterogeneity of organoid objects and phenotypes, that present challenges for analysis. Analysis methods allow characterization of objects using multiple read-outs including volumes, areas, intensities, also allow to count and characterize cells, and even sub-cellular structures inside organoids. Advanced analysis tools including machine-learning elements also to derive information about organoid phenotypes, and changes in phenotypes as a result of various treatments.

Future of organoid development and imaging

With the speed in which organoid protocols are development and imaging technologies are progressing, the next important step in organoid research is the integration of automation into labs to improve workflow speed and increase reproducibility. According to Oksana, end-to-end workflow automation solutions are in their infancy.

“We previously focused on imaging and image analysis automation, but the next step is the ability to automate the entire workflow. This involves automating every part of the entire cell journey: liquid handler, incubator, centrifuge, plate reader – all tied together by a robotic system that can navigate seamlessly between each component. The robotic arm can move plates from the incubator to the liquid handler for media addition, cell plating, and staining. It can then transfer the plates to the imaging system and analysis software for high-content analysis.”

An example of such an automated system is the Organoid Innovation Center built by Molecular Devices – an end-to-end solution that standardizes the organoid development process with cell culture, treatment, and incubation, through to imaging, analysis, and data processing.

Besides automation, initial optimization is another factor determining the success of the organoid study. Hilary states: “The more time spent optimizing the organoid research process upfront by understanding the biology and structure of the specific organoid type, the more robust the data collection and analysis will be, and the better quality the image-related data will have.”

When it comes to drug discovery and disease modeling, organoid research is fast-growing and holds a lot of promise. When exploring options for your lab, it’s important to invest proper investigation and resources to develop a streamlined workflow that will yield reproducible, reliable, and accurate results. When done optimally, researchers can benefit from increased productivity, improved efficiency, speed their time to market, and most importantly, improve the quality of life for patients.

Get in touch with one of our experts today to learn how we can help streamline your 3D biology or organoid workflow.

1. Mullard, Asher. "Parsing clinical success rates." Nature Reviews Drug Discovery 15.7 (2016): 447-448.

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