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See detailA Systems biology approach to elucidate the contribution of alpha-synuclein to early in vitro phenotypes of Parkinson’s disease
Modamio Chamarro, Jennifer UL

Doctoral thesis (2020)

Although Parkinson's disease (PD was first described more than two hundred years ago, the clinical treatment options remain limited to symptom alleviation. Consequently, understanding the underlying ... [more ▼]

Although Parkinson's disease (PD was first described more than two hundred years ago, the clinical treatment options remain limited to symptom alleviation. Consequently, understanding the underlying molecular mechanisms is vital for the development of new therapeutic strategies. Most cases of PD are associated with toxic aggregations of the alpha-synuclein (α-syn) protein. However, the physiological and pathological mechanisms of α-syn aggregation are not entirely understood. One main reason for this knowledge gap is the lack of models that properly recapitulate the pathology in a human-midbrain-like context. Organoid models have emerged as an attractive model system that covers key aspects of in vivo tissue and organ complexity. Here, we present an optimized organoid protocol, which recapitulates features of the human midbrain. These human midbrain organoids (hMOs) present reduced levels of cell death in the core, while exhibiting reduced variability and increased viability. Their smaller size also allowed the implementation of a time-efficient image analysis technique. By using the protocol mentioned above, we generated hMOs from patient-derived induced pluripotent stem cells (iPSCs harboring a triplication of the SNCA gene (3xSNCA. 3xSNCAexhibited twice the levels of α-syn protein compared to wild type (WT) hMOs. Transcriptionalanalysis of 3xSNCA hMOs showed upregulation of PD- and SNCA-associated genes, as wellas transcriptional deregulations in neurogenesis, cell death, proliferation, and synapse formation. The analysis of cellular phenotypes in patient-specific hMOs supported these genetic observations. 3xSNCA hMOs presented reduced proliferation, cell death and reduced synapse count in mature organoids. Furthermore, 3xSNCA hMOs showed a reduced total number of neurons and impaired astrocytic differentiation. In addition, analysis of transcriptional and metabolomic data showed deregulation in metabolic pathways. To further analyze and explain our results, we used the latest human metabolic reconstruction (Recon3D) to generate an in silico model. The results presented here are a systematic analysis of patient-specific phenotypes in midbrain organoids from individuals with a triplication in the SNCA gene, which represent a starting point for further approaches to develop therapies. [less ▲]

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See detailReproducible generation of human midbrain organoids for in vitro modeling of Parkinson’s disease
Nickels, Sarah Louise UL; Modamio Chamarro, Jennifer UL; Mendes-Pinheiro, Barbara et al

in Stem Cell Research (2020)

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See detailPeptide-Imprinted Poly(hydroxymethyl 3,4-ethylenedioxythiophene) Nanotubes for Detection of Alpha Synuclein in Human Brain Organoids
Lee, Mei-Hwa; Liu, Kuan-Ting; Thomas, James Louis et al

in ACS Applied Nano Materials (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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See detailThe Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease
Noronha, Alberto UL; Modamio Chamarro, Jennifer UL; Jarosz, Yohan UL et al

in Nucleic Acids Research (2018)

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic ... [more ▼]

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community. [less ▲]

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