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See detailINCREASING THE COMPLEXITY OF MIDBRAIN ORGANOID SYSTEMS FOR DEVELOPMENTAL STUDIES AND DISEASE MODELLING
Sabaté Soler, Sonia UL

Doctoral thesis (2022)

The discovery of iPSC technology revolutionized the biomedical field, allowing the development of translatable and complex 2D and 3D cell culture systems. Organoids are 3D models containing multiple cell ... [more ▼]

The discovery of iPSC technology revolutionized the biomedical field, allowing the development of translatable and complex 2D and 3D cell culture systems. Organoids are 3D models containing multiple cell types that mimic complex microenvironments. This is highly advantageous to understand human development, physiology and disease, especially in inaccessible areas such as the brain. Human midbrain-specific organoids have been developed to study the midbrain (abundant in dopaminergic neurons). In Parkinson’s Disease (PD), dopaminergic neurons in the substantia nigra of the midbrain degenerate, causing a broad spectrum of clinical features. Midbrain organoids (MO) are rich in dopaminergic neurons, and contain spatially organized groups of neural cells and progenitors. MO generated from PD patients’ cells recapitulate dopaminergic neuron degeneration. In this thesis, we first demonstrated that dopaminergic neuron PD phenotypes and drug rescue effects were similar between MO and mice. After, we identified different neuronal clusters, progenitor cells, radial glia and mesenchymal cells in MO by scRNA-Seq. As expected, due to the neuro-ectodermal patterning of the MO’ starting cell population, we confirmed the absence of mesoderm-derived cell types, such as microglia and endothelial cells. This represents a limitation for the system in terms of cellular and molecular complexity. Microglia in the human brain perform surveillance, defence and homeostasis functions; they phagocytose metabolic waste products and cell debris. We successfully developed a novel protocol to integrate functional microglia into our MO model. SnRNA-Seq analysis and electrophysiological results suggested a reduction of stress levels and higher maturation of neurons in the presence of microglia, respectively. We then aimed to vascularise MO, which would better recapitulate the brain environment and improve oxygen and nutrient supply into the organoid core (a common 3D culture limitation). We integrated an endothelial network into MO by fusion with vascular organoids, and observed the presence of blood vessel components like pericytes and basal lamina. Furthermore, vascularized assembloids showed decreased levels of cell death and hypoxia. Finally, by co-culturing microglia with vascularized assembloids, we modelled the neurovascular unit in 3D. Altogether, this work contributes to the development of advanced 3D region-specific organoids, which better recapitulate the complexity of the human brain. These novel MO systems represent one step further into modelling neuroinflammation and blood brain barrier disruption, typical from neurodegenerative disorders such as PD, which might lead to more reliable and personalized medical approaches. [less ▲]

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See detailGeneralising from Conventional Pipelines: A Case Study in Deep Learning-Based High-Throughput Screening
Garcia Santa Cruz, Beatriz UL; Sölter, Jan; Gomez Giro, Gemma UL et al

E-print/Working paper (2021)

The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality ... [more ▼]

The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25 % increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis. [less ▲]

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See detailSingle-cell transcriptomics reveals multiple neuronal cell types in human midbrain-specific organoids
Smits, Lisa UL; Magni, Stefano UL; Kinugawa, Kaoru et al

in Cell and Tissue Research (2020)

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See detailFrom tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Saraiva, Claudia UL et al

Poster (2019, November 29)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios. [less ▲]

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