AI-Powered Cell Division Tracking in C. elegans

This C. elegans line was created by C. W. Okafornta, Dresden University of Technology, Germany. It is equipped with fluorescent marker proteins for cell membranes (PH::mKate2), nuclei (H2B::mCherry) and centrosomes (gammaTub::GFP). This allows for visualization of cellular structures to analyze cell division cycles and cell compartmentalization during live embryonic development.

Image data for this use case was captured with ZEISS Lattice Lightsheet 7. This system is designed for gentle, volumetric live cell imaging, making it the ideal instrument for observing delicate structures over long periods of time. The C. elegans embryos were imaged using the green channel (centrosomes) and red channel (cell membranes and nuclei). Live acquisition was performed for almost 2 hours with one volume every 30 sec, 476 planes per volume for a total of 120,000 images.

When using lattice light sheet microscopy, the sample is imaged at an angle, due to the geometry inherent to this technology. Consequently, the raw data must be processed (deconvolved and deskewed) in ZEN (Figure 2) before further analyses can be performed.

The processed .czi file from ZEN was directly imported into ZEISS arivis Pro (formerly Vision4D) and converted into the arivis .sis file format, a format specifically designed for fast handling of large data sets. We then reduced the data size by using only every second time point, resampling the data set to 50% in z dimensions and converted to data set to 8-bit. The final data set has 2 channels, 79 slices, 60 time points, and a size of ~0.9 GB.

We proceeded toupload image data from 6 individual upload image data from 6 individual time points onto the arivis Cloud platform in order to train deep learning models using the arivis AI toolkit. From these, 76 random slices were annotated for cell bodies and nuclei (Figure 3). From the resulting labelled dataset, two distinct Deep Learning segmentation models were trained, one for cell bodies and one for nuclei.

For image analysis in ZEISS arivis Pro we aimed at segmenting cell bodies, nuclei and centrosomes.

Centrosomes were segmented conventionally with watershed segmentation, requiring some pre-processing (background subtraction and denoising) to clean up the raw image. For nuclei and cell bodies, we embedded Deep Learning models imported from ZEISS arivis Cloud directly into the arivis Pro analysis pipeline with the newly introduced “Deep Learning Segmenter” function.

After segmentation, all resulting object classes were further optimized and filtered using morphological operations and filter functions. For example., raw cell body segmentations still contained too many merged cells and these could be separated by applying morphological opening, splitting and closing filters.

Next, object tracking was performed based on cell bodies. Parameters were adjusted to also detect (binary) cell divisions. This allowed for tracking complete cell lineages from a few initial cells. Furthermore, compartments were created to study cell bodies along with the respective nuclei and centrosomes contained in them.

Finally, segmented cell bodies, nuclei and centrosomes were grouped per time point to extract global average measurements of numbers, volumes and intensities.
The sketch summarizes the image analysis procedure. The arivis Pro pipeline to perform these operations will be available in detail following the conclusion of this case study.

To validate the quality of centrosome segmentation, we overlayed segmented centrosomes with the raw image (Figure 5). In this video clip, objects are color-coded based on their time of appearance. Centrosomes vary in size and intensity throughout the cell cycle. All centrosomes, including the small interphase centrosomes, were successfully segmented.

Figure 7 shows the results from Deep Learning segmentation of nuclei. Nuclei segmentations were robust, although not as precise as cell segmentations. Particularly during mitosis, the Deep Learning model struggled to identify all nuclei with sufficient precision.

Notice how cells descending from a particular initial cell occupy coherent patches of the embryo at later stages, a phenomenon known as mosaic development².

Figure 14 shows the global measurement of average centrosome intensities. Because there are distinct collective waves of cell division in the embryo, we can detect peak centrosome intensities that coincide with these waves. But what happens if there are no collective waves of cell division? Then, any global measurement of centrosome properties would become diluted and potentially undetectable (compare red box in Figure 14).

The results show that centrosome intensities strongly increase in the 10 minutes prior to cell division and then steeply drop after it. What we observe here is the formation of spindle poles at a late mitotic stage that actively pull chromosomes apart just before the cell splits into its two daughter cells.