Today's guest post was contributed by Debraj Manna, a graduate student and science writer at the Indian Institute of Science, Bangalore, India. Besides his research in non-canonical translation, Debraj is interested in decoding complex scientific discoveries into compelling narratives. He is committed to sharing the stories behind scientific advancements while shedding light on researchers' lives. He is also a member of the GSA ECLP multimedia subcommittee. You can connect with Debraj on X (formerly Twitter) or LinkedIn.

Every multicellular organism starts life as a single-celled zygote that must grow and divide into the various tissues and organs present in the adult. Pluripotent stem cells differentiate into specialized cell types, and tracking the lineage of each cell in the embryo is an important aspect of studying development. The physical transparency and fixed number of adult cells of C. elegans have made the nematode a preferred system for lineage tracing; indeed, the 2002 Nobel Prize in Physiology and Medicine recognized John Sulston’s work in mapping the fate of each cell in the worm.

Two decades later, new work published in the October issue of GENETICS provides an exciting advance for researchers studying lineage tracing: an automated lineage-tracing pipeline called embGAN to track cells in C. elegans embryos to their respective lineages using label-free 3D time-lapse microscopy.

Lineage tracing relies on visually following cells through the course of development. Over the years, different combinations of light, electron, and immunofluorescence microscopy have been applied to the problem, both in label-free environments and through approaches that tag specific cells to make them easier to follow. Advances in microscopy and technology that allow for automated imaging have made larger-scale experiments much more feasible. However, they often still rely on fluorescently labeling cells through dyes, antibodies, or transgenesis. These methods can have significant drawbacks—inconsistent reliability of reagents, high costs, and time spent generating and validating new transgenic lines.

That’s where embGAN comes in.

Waliman, Johnson et al. developed the pipeline to give researchers a platform to automatically detect cells from light microscopy images without the need for cell labeling. They trained embGAN on both 3D fluorescence and DIC microscopy images of C. elegans throughout the nematode’s embryonic development, comparing the tool’s performance with the label-free images against an available automated pipeline for lineage tracing using fluorescent images for validation. They then used it to trace the embryonic lineage of cells in a laboratory strain of C. elegans, finding it a powerful tool for lineage tracing in early and late embryos, where nuclei size and packing of the cells vary and can present challenges for automated analysis. embGAN also performed well across different C. elegans strains and with images from different microscopes.

In addition to eliminating the requirement of fluorescent markers, embGAN dramatically reduces the time required to achieve results equivalent to existing fully manual methods by an incredible 95%—providing researchers the ability to scale up experiments and conduct cell lineage studies in unlabeled embryos in a high-throughput manner previously impossible.

embGAN provides an elegant, automated solution to experimental hurdles that make lineage tracing challenging—unavailability or unreliability of fluorescent markers, the limited number of spectral channels, and the inability to establish transgenic organisms—and massively speeds up the process. In the future, the pipeline can likely be applied to images beyond those of C. elegans embryos and could prove an essential advance in imaging analysis.

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