From Image to Results | Organoid Analysis

In this new series "From Image to Results", explore various case studies explaining how to reach results from your demanding samples and acquired images in an efficient way. For each case study, we highlight different samples, imaging systems, and research questions.

In this third episode, we showcase a simple imaging experiment performed on intestinal organoids treated with and without a Wnt-inhibiting drug, with the experimental goal to study the role of Wnt signaling in organoid formation.​

Key Learnings:

• How to study the role of Wnt signaling in organoid formation
• How to use machine learning to segment outer organoid cell layers
• How to perform nucleus and cell body segmentation
  

Next, we checked the validity and quality of the different segmentations applied during the analysis. The organoid cell layer and organoid lumen were segmented with the machine learning segmenter. Employing machine learning leads to superior segmentation results compared to conventional threshold-based segmentation. It allowed discrimination between cells in the cell layer (included in the objects) and lumen (excluded from the objects) based on complex image texture (Figure 3A).

Cell nuclei were segmented with blob finder segmentation. This allowed high-quality separation of nuclei despite them being densely packed in 3D and despite intensity variations. By setting up relationships between the organoid cell layer and lumen object, nuclei were then further separated into cell layer nuclei and luminal nuclei (Figure 3B). Cell bodies were segmented by region-growing from cell layer nuclei. By object filtering, they are nicely restricted to the organoid cell layer (Figure 3C).

Size and Roundness of Organoids

Having segmented and validated all relevant objects of interest, we now can jump into the analysis. Because of natural variation within spheroids and to allow for a simple statistical analysis, all analyses are prepared as box-whisker plots (depicting single data points, and their mean and standard deviations), comparing organoids from both experimental groups, where each dot is the data from one organoid.

First, the volumes of the full organoid (Figure 4A), the outer organoid cell layer (Figure 4B) and the organoid lumen (Figure 4C) were analyzed. There is a trend for larger volumes and particularly a larger spread of volumes in the control group, suggesting that Wnt inhibition interferes with proper growth of the spheroids. However, none of these trends were significant in a statistical t-test. Longer incubation times or including more organoids in the analysis would likely help to obtain more conclusive results.

We also observed in the initial overview image (Figure 2A) that control-treated organoids formed more amorph shapes while organoids treated with Wnt inhibitor remained spherical. ZEISS arivis Pro offers several morphological parameters to analyze such observations. Figure 4D shows a significant drop of “roundness” in control-treated samples, thus, Wnt inhibition indeed interferes with the formation of amorph organoid shapes.

Cell Numbers in Different Organoid Compartments

Next, as another marker of organoid growth, we evaluated the number of cells in the different organoid compartments based on nucleus object counts. Quantification was performed for all nuclei, with cell layer nuclei and luminal nuclei processed independently. These two classes have completely different fates within the growing organoid. Luminal cells only act as a transient scaffold and eventually die off by apoptosis, while the outer layer cells form the functional epithelial tissue of the organoid.

Cell numbers are shown for the total organoid (Figure 5A), the outer organoid cell layer (Figure 5B) and the organoid lumen (Figure 5C). For all three groups, there is a significant increase in cell numbers for control-treated organoids compared to organoids exposed to Wnt inhibition (p<0.05 each in statistical t-tests). This again indicates that Wnt inhibition interferes with proper organoid outgrowth. As for organoid volumes, the spread of values (the standard deviation) is considerable. Analysis would therefore likely benefit from analyzing larger sample sizes (more spheroids in the analysis).

Aldolase B Expression as a Marker for Enterocyte Differentiation​

Having described the basic morphological organoid features in the previous sections, we now turn to Aldolase B expression analysis. Aldolase B is a marker for enterocyte differentiation. During organoid maturation, intestinal stem cells should progressively transform into differentiated cells. As a result, increased Aldolase B expression is an indicator for successful organoid maturation.

Two of our object groups could theoretically be suitable for the analysis: (1) the cell layer nuclei and (2) the cell layer cell bodies. As shown in Figure 6A, Aldolase B mainly localizes to the cytosol, making the cell body objects the best suited for analysis. ZEISS arivis Pro allows extracting channel intensities from different hierarchical layers. Here, we show here the sum of Aldolase B expression for the complete organoid (Figure 6B) and the single-cell mean Aldolase B intensities measured independently on every cell (Figure 6C). In both cases, there is a strong and significant increase (p<0.001 in statistical T-tests) in organoids that were mock-treated compared to organoids treated with Wnt inhibitor. This adds further evidence that Wnt inhibition interferes with organoid maturation.

Determining Aldolase B-positive Cells as an Alternative Read-out​

In the last section, we focused on measuring a continuous spectrum of Aldolase B expression; however, this has a serious drawback. It doesn’t consider that, more realistically, cells are either “positive” or “negative” for Aldolase B, as can be observed nicely in a typical organoid cross section (Figure 7A). Therefore, a more suitable analysis strategy stratifies cells into Aldolase B-positive and -negative groups, then evaluates the fraction of positive cells within an organoid.

Using this approach, a threshold must be defined that separates positive and negative populations. Figure 7B shows the bimodal distribution of Aldolase B intensity over all cells in the data set, with maxima at mean pixel intensities of 10 and 30, respectively. We selected a mean pixel intensity of 15 as a threshold for Aldolase B-positive cells. Applying this threshold generates positive and negative cells that match well with the visual impression of Aldolase B distribution in the example cross section (Figure 7A). Results are shown as total positive cells per organoid (Figure 7C) and as the percentage of positive cells per organoid (Figure 7D). Again, control-treated organoids had significantly more Aldolase B-positive cells, indicating better organoid maturation.

Additional Note - Scaling Up Analysis​

Once a pipeline has been created and optimized in ZEISS arivis Pro by testing on a sample set of images, it is then possible to scale up the analysis by use of the server-based VisionHub platform. As arivis VisionHub is accessed over the web, users can easily upload pipelines to their datasets, run batch analysis and then have the results readily available within the web-based user interface.​

In this study, 60 organoid samples were analyzed but it would be possible to analyze many more. arivis VisionHub watches for newly acquired images being exported from the microscope. Once the dataset is fully exported, the thumbnail becomes visible in the user interface (Figure 8A). Users can upload their pipelines and simply select datasets for analysis (Figure 8B). When the analysis is complete, the results are available as object mark-ups in the viewer (Figure 8C) and data tables are also available for download.​

In this application story we have showcased an approach to an organoid study employing a ZEISS Celldiscoverer 7 and ZEISS arivis Pro for image analysis.
Organoids place high demand on analytical techniques because of their rather complex tissue architecture (3D data sets consisting of an outer cell layer and an inner lumen) and for the multitude of differentiation markers that may be applied (organoid volume and shape, cell numbers and single-cell expression analysis of tissue). We have highlighted how all this can be accomplished with the built-in functionality of ZEISS arivis Pro.

In this experiment, we found several results that could highlight the role of Wnt signaling in intestinal organogenesis. We could validate that organoids with inhibited Wnt signaling would have decreased organoid volumes, a more immature spherical shape, fewer cell numbers and less Aldolase B expression. Aldolase B as a marker for enterocyte differentiation was reduced both in terms of total expression levels and in terms of Aldolase B-positive cell fractions.

It should be noted that only 30 organoids per sample were analyzed for the purpose of demonstrating image analysis with our software. This certainly doesn’t meet the high standards that would be required for a professional study and statistically relevant conclusions. We still believe that having this kind of “real-world” use case helps you to learn about image analysis strategies that can be applied to your own data. As always, the data set and analysis pipeline are provided here to give you the opportunity to test ZEISS arivis Pro for yourself.