Collaborator stories: Nicole Repina
From Optogenetics to 3D organoids: A researcher's perspective
Nicole Repina is a postdoctoral researcher in the Liberali’s group at the Friedrich Miescher Institute for Biomedical Research (FMI). Nicole works with 3D small intestinal organoids to study tissue regeneration and self-organization.
Can you tell us about your scientific journey and the project you are currently working on?
Throughout my academic journey, I have been fortunate to learn from and combine diverse research disciplines. In particular, the complexity of biological systems viewed through the lens of microscopy and quantitative analysis has always been fascinating to me. During my PhD at UC Berkeley, I worked at the intersection of cell biology and optics to develop optogenetic methods and study the role of cell signaling heterogeneity in embryonic stem cells. This experience not only honed my technical skills in microscopy and cell systems but also sparked a deep interest in image analysis for quantifying multicellular interactions.
For my postdoctoral research, I shifted focus to more complex multicellular systems. Joining Prisca Liberali’s group at the Friedrich Miescher Institute for Biomedical Research (FMI), I now work with 3D organoids derived from the mouse small intestine. These organoids serve as an excellent model system for studying the self-organized emergence of tissue shape and patterning. These organoids develop from a single cell, which divides and differentiates to eventually form a 3D multicellular tissue with a complex shape and cell organization that mimics the crypts and villi of the small intestine.
The key scientific aim of my project is to understand how single-cell properties and interactions can give rise to tissue-level emergent phenomena such as shape and spatial patterning. Using advanced microscopy and analysis methods, I quantify organoid cell composition and interactions in 3D, enabling me to study these phenomena at a multiscale and system-wide level. By treating organoid development as a dynamic, complex system, I seek to unravel the intricate coordination and communication between cells that leads to the formation of organized, functional tissues. My research sheds light on the fundamental mechanisms that govern the self-organization of multicellular systems during regeneration and development.
How has using Fractal improved or simplified your image analysis workflow?
Fractal has been incredibly useful to apply my image analysis workflows to large datasets of thousands of organoids, ranging from 10 to 50 terabytes in size. I typically analyze 3D confocal fluorescence microscopy images, often from multiple rounds of iterative multiplexed staining. With Fractal, I can parallelize my analysis workflows over multiple wells, iterative imaging rounds, and time points in our institute’s computing cluster. I can also specify the memory usage requirements and generate log reports, which is useful for catching any unexpected behavior during the analysis. In addition, Fractal’s features for tracking task versions and workflow parameters ensure my analysis is reproducible and transparent, a critical advantage for complex workflows. The OME-Zarr format also allows me to visualize and explore these large, 3D images more efficiently, using Napari.
I had first developed our image analysis workflows for 3D and multiplexed analysis of cell expression, tissue shape, and patterning in an open-source package called scMultipleX. This was a collaborative effort that included contributions from the FMI image analysts and IT team, as well as collaboration with the lab of Florian Jug at the Human Technopole. It was initially intended for processing of images in the tiff format, but thanks to the collaboration between FMI and the BioVisionCenter, I have now implemented the scMultipleX workflows as Fractal tasks. It allows me to run these tasks on large datasets and benefit from the OME-Zarr file format, which makes data loading more efficient during processing and has an organized metadata structure, which is particularly useful for my multiplexed and multiscale experiments.
In addition, which features of Fractal were most helpful to you?
In addition to the mentioned technical benefits, I really appreciate the open-source and collaborative vision of the Fractal project. It makes it easier to share my image analysis workflows and apply them to different datasets or even different biological systems, which benefits both our team and the broader research community tackling similar challenges.
The community behind the project is also invaluable. Connections fostered by the BioVisionCenter allowed me to develop new workflows and algorithms, including an image stitching task in collaboration with Marvin Albert at the Institut Pasteur.
What would you tell other researchers or labs about your experience with Fractal?
Fractal is a wonderful platform supported by a passionate community of researchers with shared interests. It offers a structured and modern approach to image analysis, and I would say that the investment in converting our analysis workflows into Fractal tasks has already paid off by enabling efficient, scalable, and reproducible analyses. I encourage researchers to take the initiative to adopt Fractal and contribute their workflows, as this collective effort will expand its image analysis capabilities and broaden its utility to diverse research applications.
If you would like to reach out to Nicole to find more about her research, please contact her at nicole.repina@fmi.ch