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Biomedical researchers at the University of Münster study the motion of the embryonic cells that later become sperm or egg cells – the founder cells of every organism that reproduces sexually. These cells move (a process called cell migration) from their birth place to where they form the gonad, the organ of reproduction. Insights into this fundamental process can lead to important clinical advancements, such as in the fight against cancer. 

Cell migration is a complex process that requires the coordination of many cellular compartments, such as the nucleus, which contains the DNA of the cell, and the cytosketon, which gives shape and motile function to these cells. Researchers need to accurately detect and track these complex structures in videos of moving cells, to interpret how the behaviour of an individual compartment is coordinated with the others’, and to understand how pharmacological and genetic treatments affect the motility and function of these cells. 

The partnership between the research lab at the University of Münster and DeepMirror is allowing researchers to tap into our powerful AI solutions. We adjusted our CytoButler AI solution by adding a module that can track cells over time, and also added several custom analytics modules to classify the behaviours of the different cell compartments. 

Our clients are now able to obtain detailed quantifications of the motility of these cells for dozens of different experiments, to understand how the different compartments behave individually and collectively, and to test the effects of experimental perturbations that could lead to effective advancements in our battle against cancer.

To understand how organs develop during pregnancies and to obtain insights into conditions related to premature birth and into the possibility of therapeutic regeneration, biomedical researchers at the University of Cambridge use 3D organoids as a system that can replicate human organs at a scale that is amenable to pharmacological and gene-targeting experiments. 

The function of biomedical tissues is often linked to their morphology, and thus researchers strive to detect the outline of all the individual cells that make up these organoids (a process called instance segmentation in machine learning) to evaluate the efficacy of different treatments. This poses several challenges, as instance segmentation of three-dimensional biomedical data as well as the interpretation of the effect of geometry of the cells on their function are still open problems. 

To solve this, DeepMirror combined two approaches: i) extending the 2D AI cell segmentation tool CytoButler to work with 3D data by training a personalised AI model, and ii) implementing custom analytics for our clients to analyse the effect of different treatments on 3D geometrical parameters and thus, their function. 

Our clients are now using our 3D CytoButler AI solution to detect the morphological parameters of each of the hundreds of cells that make up the organoids, obtaining meaningful insights that will help them study the development of organs and finding new therapeutics for harmful conditions.