Identifying secondary & tertiary objects

Estimated time: 25 minutes

Overview

Questions

• How can we detect whole cells once we have identified nuclei?
• What is the differences between detecting primary, secondary, and tertiary objects?

Objectives

• Use IdentifySecondaryObjects to segment whole cells from an actin image.
• Understand how secondary objects depend on primary objects.
• Learn how propagation-based segmentation expands from nuclei to cell edges.
• Create a cell object set suitable for per-cell measurements.
• Use IdentifyTertiaryObjects to create cytoplasm masks.

From nuclei to whole cells

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In the previous section, we identified nuclei as primary objects. This gives us “seed” objects: one nucleus per cell.

However, many biologically interesting measurements (e.g. cell area, shape and cytoplasmic fluorescence) require us to segment the whole cell. This is often more challenging than nuclei segmentation because:

• cytoplasm and boundaries can be fainter than nuclei,
• neighboring cells may touch or overlap,
• staining can be uneven across the cell body.

To tackle this, CellProfiler provides IdentifySecondaryObjects, which grows secondary objects outward from nuclei using information from another image (here an actin channel). This approach helps prevent ambiguous assignments of boundary pixels by ensuring each cell is linked to exactly one starting nucleus.

The IdentifySecondaryObjects module

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Add a new module via + Add → Object Processing → IdentifySecondaryObjects.

You should now see a module where you need to specify:

1. which primary objects act as “seeds” (nuclei),
2. which image contains cell boundary information (actin),
3. how to determine where each cell ends (thresholding + method),
4. how to handle cells touching the image border.

Step 1: choose primary input objects

Set Select the input objects (or similarly named setting) to Nuclei or the name you set in the previous lesson. This tells CellProfiler that each cell object should be grown outward from one nucleus.

Step 2: choose the correct input image (actin)

Set Select the input image to your actin (or cytoplasmic) channel, e.g. Actin (or whatever name you assigned in NamesAndTypes).

The channel should contain relatively strong signal across the cell body and/or along the cell boundary.

Challenge

Checking the actin image

Using Test Mode, inspect a few images:

• Can you see whole cell bodies?
• Are neighboring cells separable?
• Is the background reasonably dark?

If not, what issues do you observe?

Callout

If you find the contrast too dim to see the channel well, you can increase the contrast. You can do so either by right clicking > Adjust Contrast, or by selecting Subplots > (Object name) outlines > Adjust Contrast. Then, select Log normalized and a normalization factor that you deem suitable, the click Apply to all.

Show me the solution

Compared to nuclei, cell boundaries are often less easily distinguished. We can see that the actin channel does increase at cell junctions, which should help in segmenting the cells in later steps. But it is important to keep in mind that any segmentation will not be perfect here: after all, where would you draw the boundaries by hand?

Step 3: choose a method to identify secondary objects

Now we will use IdentifySecondaryObjects to segment cells. Many of the options are the same as in IdentifyPrimaryObjects, but the most important difference is the presence of Select the method to identify the secondary objects option. We encourage you to read their descriptions in CellProfiler by clicking the ? symbol, but most often it is set to Propagation. To see why, let’s see what happens when we try segmenting cells with either method!

Challenge

Optional challenge: explore methods

Try two different methods (e.g. propagation vs watershed gradient). How do the resulting cell boundaries differ?

Comparing methods

As with the segmentation of nuclei, getting cell segmentation right can be tricky. Often, starting with propagation as method is a good starting point, because watershed can expand into neighboring cells (see below). But you can certainly find areas of the image where the reverse is true. This means that, once again, choices should be made carefully.

Step 4: choose a threshold strategy and threshold method

Much like when segmenting nuclei, the IdentifySecondaryObjects module allows us to fine-tune our segmentation by changing the thresholding strategy and method. As before, the best choice depends on illumination and staining consistency. While Minimum Cross-Entropy (right) is the default thresholding method, Otsu (left) can often also yield good masks and makes a bit fewer assumptions about your image. For this dataset they perform very similarly, except that Minimum Cross-Entropy does slightly better at detecting protrusions such as in the below image.

Challenge

Challenge: biological pitfalls

Think about the assumptions CellProfiler is making in its identification of secondary objects. Can you think of biological samples where these assumptions may not be met? Discuss with your neighbor.

Show me the solution

CellProfiler identifies cells by expanding outwards from a nucleus. This assumes that each cell only has exactly one nucleus in the same plane. When imaging hepatocytes, for example, this can prove problematic: they often contain more than one nucleus. Equally, if we were imaging cells in suspension, we would have to make sure than we capture the nucleus accurately in 3D and expand the cell in 3D space too. And lastly, red blood cells do not even have a nucleus, so this method would not work for them either!

Other pitfalls include mitotic/meiotic cells: from when on do we term a splitting cell as two cells? When the two nuclei have budded off, or when the membrane is fully split? CellProfiler cannot answer these questions for us, instead, we must consider these biological complexities when designing our analysis pipeline to not yield nonsensical data down the line.

Once you are happy with the result, make sure to check the following options:

1. Fill holes in identified objects: Yes
2. Discard secondary objects touching the border of the image: Yes
3. Discard the associated primary objects: Yes

And finally, name the new primary objects thus filtered, e.g. Nuclei_Filtered.

Identifying cytoplasm

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You have now created whole-cell objects from nuclei seeds and an actin image. Well done! 🎉

With nuclei and cells in hand, we can create one last object: the cytoplasm. Fortunately, this one is easy:

Cytoplasm = Cell - Nucleus

To do this, add the IdentifyTertiaryObjects module and select what you think are the correct options.

Suggested settings

• Larger objects: cells identified with IdentifySecondaryObjects
• Smaller objects: nuclei filtered in IdentifySecondaryObjects
• Name: e.g. Cytoplasm
• Shrink smaller object prior to subtraction: Yes (default)

Conclusions

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Finally, we have created all masks we need and can move on to measure things!

Help

Did you get stuck with one of the steps? Download a working version of the pipeline here:

Download pipeline

Callout

If you are using Firefox, you have to right click the button and select Save Link As....

After downloading the pipeline, you can compare it to yours for troubleshooting. To do so, first open a new CellProfiler window. Then, import this pipeline in CellProfiler by clicking on File > Import > Pipeline from File.

Key Points

• Secondary objects (cells) are typically grown from primary objects (nuclei) using a cytoplasmic/cell-boundary stain (here: actin).
• Filtering border-touching nuclei helps avoid partial cells and misleading measurements.
• The most important settings in IdentifySecondaryObjects are the identification method and thresholding choices, which strongly affect whether cells merge or fragment.
• Tertiary objects (cytoplasm) are a subtraction of nuclei from the cell mask.