204 Chapter 7 of multiple peroxisomal and mitochondrial proteins in amino-acid restricted organoids. While these results are promising, they should be interpreted as a first step into the characterization of the biological effect of such compound and this should be further tested and validated in vivo. Another in vitro model developed during this thesis is introduced in chapter 6. Together with my colleague Ligia Kiyuna, we developed an iPSC-derived hepatobiliary model to study MCADD. To the best of our knowledge, this is the first patient-specific liver organoid model for the study of MCAD deficiency. Similar to primary organoids, iPSC-derived organoids allow us to understand different pathophysiological processes within a given disease. Moreover, they present a very valuable advantage as they are derived from patient tissue19,20. This offers the opportunity to understand the effect of different mutations on different factors such as development, cell fate, or cell metabolism. In the case of MCADD, it is not uncommon to see patients who are asymptomatic even if they carry the same mutations as other symptomatic patient21,22. In this first study, we have only used fibroblasts coming from patients with the classical mutation c.985A>G. However, the model offers the opportunity to generate organoids from fibroblasts from patients with different mutations to understand and determine the differences between symptomatic and asymptomatic patients. I developed two in silico models applied to the study of malnutrition in vitro and in vivo. In chapter 3, together with my colleague Asmaa Haja from the Perico ITN consortium, I contributed to the development of an automated tool to measure and track organoid size and morphology in brightfield images. As my contribution, I provided data to train the algorithm, defined the requirements of the system and validated the results. The need for this tool stemmed from the demand to quantify large sets of images containing, at times, hundreds of organoids. This deep-learning model was developed to speed up the quantification and characterization of the organoid work done in chapter 2, and to be applied for future experiments. In addition to minimizing the workload, another reason to develop this model was to reduce the intrinsic variability associated to organoid measuring and counting. The goal was to identify as many organoids as possible per image and track their individual growth rate in time. During the data analysis phase, distribution profiles were generated, avoiding solely relying on the average of all the data points. This model represents one of the many applications of artificial intelligence to in vitro research.
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