Investigating the role of Hedgehog/GLI1 signaling in glioblastoma cell response to temozolomide.

Resistance to chemotherapy substantially hinders successful glioblastoma (GBM) treatment, contributing to an almost 100% mortality rate. Resistance to the frontline chemotherapy, temozolomide (TMZ), arises from numerous signaling pathways that are deregulated in GBM, including Hedgehog (Hh) signaling. Here, we investigate suppression of Hh signaling as an adjuvant to TMZ using U87-MG and T98G cell lines as in vitro models of GBM. We found that silencing GLI1 with siRNA reduces cell metabolic activity by up to 30% in combination with TMZ and reduces multidrug efflux activity by 2.5-fold. Additionally, pharmacological GLI inhibition modulates nuclear p53 levels and decreases MGMT expression in combination with TMZ. While we surprisingly found that silencing GLI1 does not induce apoptosis in the absence of TMZ co-treatment, we discovered silencing GLI1 without TMZ co-treatment induces senescence as evidenced by a significant 2.3-fold increase in senescence associated β-galactosidase staining, and this occurs in a loss of PTEN-dependent manner. Finally, we show that GLI inhibition increases apoptosis in glioma stem-like cells by up to 6.8-fold in combination with TMZ, and this reduces the size and number of neurospheres grown from glioma stem-like cells. In aggregate, our data warrant the continued investigation of Hh pathway inhibitors as adjuvants to TMZ chemotherapy and highlight the importance of identifying signaling pathways that determine whether co-treatment will be successful

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation

Background and objectives: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. Methods: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. Results: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. Conclusions: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays

Defined Geldrop Cultures Maintain Neural Precursor Cells

Distinct micro-environmental properties have been reported to be essential for maintenance of neural precursor cells (NPCs) within the adult brain. Due to high complexity and technical limitations, the natural niche can barely be studied systematically in vivo. By reconstituting selected environmental properties (adhesiveness, proteolytic degradability, and elasticity) in geldrop cultures, we show that NPCs can be maintained stably at high density over an extended period of time (up to 8 days). In both conventional systems, neurospheres and monolayer cultures, they would expand and (in the case of neurospheres) differentiate rapidly. Further, we report a critical dualism between matrix adhesiveness and degradability. Only if both features are functional NPCs stay proliferative. Lastly, Rho-associated protein kinase was identified as part of a pivotal intracellular signaling cascade controlling cell morphology in response to environmental cues inside geldrop cultures. Our findings demonstrate that simple manipulations of the microenvironment in vitro result in an important preservation of stemness features in the cultured precursor cells

Development of Circadian Oscillators in Neurosphere Cultures during Adult Neurogenesis

Circadian rhythms are common in many cell types but are reported to be lacking in embryonic stem cells. Recent studies have described possible interactions between the molecular mechanism of circadian clocks and the signaling pathways that regulate stem cell differentiation. Circadian rhythms have not been examined well in neural stem cells and progenitor cells that produce new neurons and glial cells during adult neurogenesis. To evaluate circadian timing abilities of cells undergoing neural differentiation, neurospheres were prepared from the mouse subventricular zone (SVZ), a rich source of adult neural stem cells. Circadian rhythms in mPer1 gene expression were recorded in individual spheres, and cell types were characterized by confocal immunofluorescence microscopy at early and late developmental stages in vitro. Circadian rhythms were observed in neurospheres induced to differentiate into neurons or glia, and rhythms emerged within 3–4 days as differentiation proceeded, suggesting that the neural stem cell state suppresses the functioning of the circadian clock. Evidence was also provided that neural stem progenitor cells derived from the SVZ of adult mice are self-sufficient clock cells capable of producing a circadian rhythm without input from known circadian pacemakers of the organism. Expression of mPer1 occurred in high frequency oscillations before circadian rhythms were detected, which may represent a role for this circadian clock gene in the fast cycling of gene expression responsible for early cell differentiation

Zika Virus Disrupts Molecular Fingerprinting Of Human Neurospheres

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Zika virus (ZIKV) has been associated with microcephaly and other brain abnormalities; however, the molecular consequences of ZIKV to human brain development are still not fully understood. Here we describe alterations in human neurospheres derived from induced pluripotent stem (iPS) cells infected with the strain of Zika virus that is circulating in Brazil. Combining proteomics and mRNA transcriptional profiling, over 500 proteins and genes associated with the Brazilian ZIKV infection were found to be differentially expressed. These genes and proteins provide an interactome map, which indicates that ZIKV controls the expression of RNA processing bodies, miRNA biogenesis and splicing factors required for self-replication. It also suggests that impairments in the molecular pathways underpinning cell cycle and neuronal differentiation are caused by ZIKV. These results point to biological mechanisms implicated in brain malformations, which are important to further the understanding of ZIKV infection and can be exploited as therapeutic potential targets to mitigate it.7Brazilian Development Bank (BNDES)Funding Authority for Studies and Projects (FINEP)National Council of Scientific and Technological Development (CNPq)Foundation for Research Support in the State of Rio de Janeiro (FAPERJ)Sao Paulo Research Foundation (FAPESP) [14/21035-0, 14/14881-1, 13/08711-3, 14/10068-4]Coordination for the Improvement of Higher Education Personnel (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

méthodologie de modélisation de la croissance de neurosphères sous microscope à contraste de phase

The study of stem cells is one of the most important fields of research in the biomedical field. Computer vision and image processing have been greatly emphasized in this area for the development of automated solutions for culture and observation of cells. This work proposes a new methodology for observing and modelling the proliferation of neural stem cell under a phase contrast microscope. At each time lapse observation performed by the microscope during the proliferation, the system determines a three-dimensional model of the structure formed by the observed cells. This is achieved by a framework combining analysis, synthesis and selection process. First, an analysis of the images from the microscope segments the neurosphere and the constituent cells. With this analysis, combined with prior knowledge about the cells and their culture protocol, several 3-D possible models are generated through a synthesis process. These models are finally selected and evaluated according to their likelihood with the microscope image using a 3-D to 2-D registration method. Through this approach, we present an automatic visualisation tool and observation of the proliferation of neural stem cell under a phase contrast microscope.L'étude des cellules souches est l'un des champs de recherches les plus importants dans le domaine biomédical. La vision par ordinateur et le traitement d'images ont été fortement mis en avant dans ce domaine pour le développement de solutions automatiques de culture et d'observation de cellules. Ce travail de thèse propose une nouvelle méthodologie pour l'observation et la modélisation de la prolifération de cellule souche neuronale sous microscope à contraste de phase. À chaque observation réalisée par le microscope durant la prolifération, notre système extrait un modèle en trois dimensions de la structure de cellules observées. Cela est réalisé par une suite de processus d'analyse, synthèse et sélection. Premièrement, une analyse de la séquence d'images de contraste de phase permet la segmentation de la neurosphère et des cellules la constituant. À partir de ces informations, combinées avec des connaissances a priori sur les cellules et le protocole de culture, plusieurs modèles 3-D possibles sont générés. Ces modèles sont finalement évalués et sélectionnés par rapport à l¿image d¿observation, grâce à une méthode de recalage 3-D vers 2-D. A travers cette approche, nous présentons un outil automatique de visualisation et d'observation de la prolifération de cellule souche neuronale sous microscope à contraste de phase

Framework for neurosphere growth modelling under phase-contrast microscopy

L'étude des cellules souches est l'un des champs de recherches les plus importants dans le domaine biomédical. La vision par ordinateur et le traitement d'images ont été fortement mis en avant dans ce domaine pour le développement de solutions automatiques de culture et d'observation de cellules. Ce travail de thèse propose une nouvelle méthodologie pour l'observation et la modélisation de la prolifération de cellule souche neuronale sous microscope à contraste de phase. À chaque observation réalisée par le microscope durant la prolifération, notre système extrait un modèle en trois dimensions de la structure de cellules observées. Cela est réalisé par une suite de processus d'analyse, synthèse et sélection. Premièrement, une analyse de la séquence d'images de contraste de phase permet la segmentation de la neurosphère et des cellules la constituant. À partir de ces informations, combinées avec des connaissances a priori sur les cellules et le protocole de culture, plusieurs modèles 3-D possibles sont générés. Ces modèles sont finalement évalués et sélectionnés par rapport à l¿image d¿observation, grâce à une méthode de recalage 3-D vers 2-D. A travers cette approche, nous présentons un outil automatique de visualisation et d'observation de la prolifération de cellule souche neuronale sous microscope à contraste de phase.The study of stem cells is one of the most important fields of research in the biomedical field. Computer vision and image processing have been greatly emphasized in this area for the development of automated solutions for culture and observation of cells. This work proposes a new methodology for observing and modelling the proliferation of neural stem cell under a phase contrast microscope. At each time lapse observation performed by the microscope during the proliferation, the system determines a three-dimensional model of the structure formed by the observed cells. This is achieved by a framework combining analysis, synthesis and selection process. First, an analysis of the images from the microscope segments the neurosphere and the constituent cells. With this analysis, combined with prior knowledge about the cells and their culture protocol, several 3-D possible models are generated through a synthesis process. These models are finally selected and evaluated according to their likelihood with the microscope image using a 3-D to 2-D registration method. Through this approach, we present an automatic visualisation tool and observation of the proliferation of neural stem cell under a phase contrast microscope.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D

We describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroids to be arranged into a gel-based array directly from a culture plate for downstream processing and analysis. Compared to processing individual samples, the technique uses 11-fold less reagents, saves time and enables automated imaging. To illustrate the power of the technology, we showcase applications of the methodology for investigating 3D spheroid morphology and marker expression and for in vitro safety and efficacy screens. Firstly, spheroid arrays of 11 cell-lines were rapidly assessed for differences in spheroid morphology. Secondly, highly-positive (SOX-2), moderately-positive (Ki-67) and weakly-positive (βIII-tubulin) protein targets were detected and quantified. Third, the arrays enabled screening of ten media compositions for inducing differentiation in human neurospheres. Lastly, the application of spheroid microarrays for spheroid-based drug-screens was demonstrated by quantifying the dose-dependent drop in proliferation and increase in differentiation in etoposide-treated neurospheres

Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early

Glioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients