Implementazione di un plug-in 3-way PCA per ImageJ per elaborazione dei dati multivariati: applicazione all'analisi morfologica di neuroni

L’obiettivo di questa tesi è l’implementazione di un plug-in, in linguaggio di programmazione Java, che esegua la 3-way PCA. Questa è una tecnica di analisi multivariata che deriva dalla PCA, tecnica molto nota ed utilizzata per l’analisi di dati che provengono dall’osservazione di un certo numero di variabili su un certo numero di soggetti od oggetti. La 3-way PCA è modellata per lo studio di dati raccolti all’interno di una matrice tridimensionale. In questo caso, rispetto alla classica PCA, si aggiunge una terza dimensione che rappresenta le condizioni in cui le variabili sono misurate sugli oggetti. Lo scopo di questa tecnica è quello di riassumere tutte le informazioni contenute all’interno di una matrice di dati in poche componenti e di farlo in maniera efficace. L’utilizzo della 3-way PCA assume un’importanza fondamentale nell’analisi dei dati biomedici, che spesso vengono acquisiti su un elevato numero di oggetti in diverse condizioni, ad esempio in diversi istanti temporali. Una possibile applicazione di tale tecnica riguarda l’analisi di caratteristiche morfologiche estratte da immagini di cellule in coltura. La letteratura biologica consultabile di oggi testimonia come, in diverse patologie, come ad esempio l’autismo, siano evidenti alterazioni sia macroscopiche che microscopiche della morfologia neuronale. In quest’ottica assume notevole importanza la quantificazione di variabili morfologiche di neuroni estratti da modelli animali di tali patologie. Il presente lavoro di tesi è articolato quindi in due fasi: dapprima si è effettuato un debug di Ne.Mo., un tool per l’analisi morfologica di neuroni, implementato in ambiente Matlab in due precedenti lavori di tesi. Oltre al debug, si è reso il tool il più user-friendly possibile, inserendo suggerimenti su come procedere nell’elaborazione e nell’analisi delle immagini che rappresentano neuroni: in questo modo, può essere utilizzato anche da utenti con conoscenze di informatica di base. La seconda parte del lavoro, quella più consistente e parte centrale della tesi, è stata l’implementazione del suddetto plug-in in Java. La 3-way PCA era già stata implementata in ambiente Matlab da Leardi e collaboratori: è stata quindi operata una “traduzione” in un altro linguaggio di programmazione. Il perché della scelta di Java è dovuto al fatto che in questo modo il plug-in potesse essere condiviso con la comunità di ImageJ, software open-source che negli ultimi anni è diventato uno dei software per l’analisi delle immagini più utilizzato nei laboratori di tutto il mondo. Per validare il plug-in, si sono analizzate, sia con gli algoritmi di Leardi, sia con quelli implementati in questo lavoro di tesi, tre matrici di dati, rappresentanti caratteristiche morfologiche di neuroni in coltura. Le prime due matrici sono state ottenute analizzando immagini di cellule di Purkinje estratte da topi wild-type e da modelli di autismo e fotografate in diversi giorni di coltura, mentre la terza è stata ottenuta fotografando neuroni fissati e colorati al Golgi provenienti da slice a diverse profondità di claustro, sottile lamina di sostanza grigia situata nella corteccia dell’insula

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

clarifying mid brain organoids application of the clarity protocol to unperfusable samples

The aim of this study was to apply a workflow, integrating delipidation methods and advanced 3D imaging techniques for mapping of the global neuronal organization of brain organoids. These are self-organizing constructs in vitro generated from human pluripotent stem cells encased in a Matrigel shell, which resemble downscaled structural and functional features of human brains. In particular, we focused on midbrain organoids, widely considered a promising tool for studying dopaminergic neuron degeneration in Parkinson's Disease. The evaluation of the microanatomical alterations at a patient-level will potentially guide future research of this neuropathy, providing meaningful human specific data in line with the European Directives and the 3Rs principles

Parvalbumin expression in the claustrum of the adult dog. An immunohistochemical and topographical study with comparative notes on the structure of the nucleus

Although the detailed structure and function of the claustrum remain enigmatic, its extensive reciprocal connection with the cortex suggests a role in the integration of multisensory information. Claustrum samples, obtained from necropsy of four dogs, were formalin fixed for paraffin embedding. Sections were either stained for morpho-histological analysis or immunostained for parvalbumin (PV). We focused on PV because in cortical and hippocampal areas it is a marker of the fast-splicing interneurons which have an important role in the information transmission and processing. Soma area, perimeter and circularity were considered as morphological parameters to quantitatively group the PV positive somata by k-means clustering. The histological investigation revealed a superior pyramidoid puddle and a posterior puddle characterized by a "cloud" of neurons in its dorso-lateral part. Immunostaining showed positive somata and fibers throughout the rostro-caudal extent of the dog claustrum, localized principally in the dorsal region. k-Means clustering analysis enabled neuron classification according to size, identifying respectively big (radius = 11.42 +/- 1.99 mu m) and small (radius = 6.33 +/- 1.08 mu m) cells. No statistical differences in soma shape were observed. The topographical distribution of PV immunoreactivity suggests that the dog dorsal claustrum might be functionally related to the processing of visual inputs. Taken together our findings may help in the understanding the physiology of claustrum when compared with anatomical and functional data obtained in other species

Toward scalable in vitro models: a novel experimental and computational pipeline for the identification of cellular metabolic parameters

Oxygen utilization by cells has a crucial role in the design of advanced in vitro models. The aim of this study is to develop an experimental and computational pipeline for identifying oxygen metabolism parameters. We applied the approach to HepG2 cell monolayer cultures, demonstrating that such parameters depend on cell density

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

Clarifying CLARITY: Quantitative optimization of the diffusion based delipidation protocol for genetically labeled tissue

Tissue clarification has been recently proposed to allow deep tissue imaging without light scattering. The clarification parameters are somewhat arbitrary and dependent on tissue type, source and dimension: Every laboratory has its own protocol, but a quantitative approach to determine the optimum clearing time is still lacking. Since the use of transgenic mouse lines that express fluorescent proteins to visualize specific cell populations is widespread, a quantitative approach to determine the optimum clearing time for genetically labeled neurons from thick murine brain slices using CLARITY2 is described. In particular, as the main objective of the delipidation treatment is to clarify tissues, while limiting loss of fluorescent signal, the "goodness" of clarification was evaluated by considering the bulk tissue clarification index (BTCi) and the fraction of the fluorescent marker retained in the slice as easily quantifiable macroscale parameters. Here we describe the approach, illustrating an example of how it can be used to determine the optimum clearing time for 1 mm-thick cerebellar slice from transgenic L7GFP mice, in which Purkinje neurons express the GFP (green fluorescent protein) tag. To validate the method, we evaluated confocal stacks of our samples using standard image processing indices (i.e., the mean pixel intensity of neurons and the contrast-to-noise ratio) as figures of merit for image quality. The results show that detergent-based delipidation for more than 5 days does not increase tissue clarity but the fraction of GFP in the tissue continues to diminish. The optimum clearing time for 1 mm-thick slices was thus identified as 5 days, which is the best compromise between the increase in light penetration depth due to removal of lipids and a decrease in fluorescent signal as a consequence of protein loss: Further clearing does not improve tissue transparency, but only leads to more protein removal or degradation. The rigorous quantitative approach described can be generalized to any clarification method to identify the moment when the clearing process should be terminated to avoid useless protein loss

On the adhesion-cohesion balance and oxygen consumption characteristics of liver organoids

Liver organoids (LOs) are of interest in tissue replacement, hepatotoxicity and pathophysiological studies. However, it is still unclear what triggers LO self-Assembly and what the optimal environment is for their culture. Hypothesizing that LO formation occurs as a result of a fine balance between cell-substrate adhesion and cell-cell cohesion, we used 3 cell types (hepatocytes, liver sinusoidal endothelial cells and mesenchymal stem cells) to investigate LO self-Assembly on different substrates keeping the culture parameters (e.g. culture media, cell types/number) and substrate stiffness constant. As cellular spheroids may suffer from oxygen depletion in the core, we also sought to identify the optimal culture conditions for LOs in order to guarantee an adequate supply of oxygen during proliferation and differentiation. The oxygen consumption characteristics of LOs were measured using an O2 sensor and used to model the O2 concentration gradient in the organoids. We show that no LO formation occurs on highly adhesive hepatic extra-cellular matrix-based substrates, suggesting that cellular aggregation requires an optimal trade-off between the adhesiveness of a substrate and the cohesive forces between cells and that this balance is modulated by substrate mechanics. Thus, in addition to substrate stiffness, physicochemical properties, which are also critical for cell adhesion, play a role in LO self-Assembly

Study of the Adhesion of the Human Gut Microbiota on Electrospun Structures

Although the adhesion of bacteria on surfaces is a widely studied process, to date, most of the works focus on a single species of microorganisms and are aimed at evaluating the antimicrobial properties of biomaterials. Here, we describe how a complex microbial community, i.e., the human gut microbiota, adheres to a surface to form stable biofilms. Two electrospun structures made of natural, i.e., gelatin, and synthetic, i.e., polycaprolactone, polymers were used to study their ability to both promote the adhesion of the human gut microbiota and support microbial growth in vitro. Due to the different wettabilities of the two surfaces, a mucin coating was also added to the structures to decouple the effect of bulk and surface properties on microbial adhesion. The developed biofilm was quantified and monitored using live/dead imaging and scanning electron microscopy. The results indicated that the electrospun gelatin structure without the mucin coating was the optimal choice for developing a 3D in vitro model of the human gut microbiota

Millifluidic culture improves human midbrain organoid vitality and differentiation

Human midbrain-specific organoids (hMOs) serve as an experimental in vitro model for studying the pathogenesis of Parkinson's disease (PD). In hMOs, neuroepithelial stem cells (NESCs) give rise to functional midbrain dopaminergic (mDA) neurons that are selectively degenerating during PD. A limitation of the hMO model is an under-supply of oxygen and nutrients to the densely packed core region, which leads eventually to a "dead core". To reduce this phenomenon, we applied a millifluidic culture system that ensures media supply by continuous laminar flow. We developed a computational model of oxygen transport and consumption in order to predict oxygen levels within the hMOs. The modelling predicts higher oxygen levels in the hMO core region under millifluidic conditions. In agreement with the computational model, a significantly smaller "dead core" was observed in hMOs cultured in a bioreactor system compared to those ones kept under conventional shaking conditions. Comparing the necrotic core regions in the organoids with those obtained from the model allowed an estimation of the critical oxygen concentration necessary for ensuring cell vitality. Besides the reduced "dead core" size, the differentiation efficiency from NESCs to mDA neurons was elevated in hMOs exposed to medium flow. Increased differentiation involved a metabolic maturation process that was further developed in the millifluidic culture. Overall, bioreactor conditions that improve hMO quality are worth considering in the context of advanced PD modelling