Impact of Physical Obstacles on the Structural and Effective Connectivity of in silico Neuronal Circuits

Scaffolds and patterned substrates are among the most successful strategies to dictate the connectivity between neurons in culture. Here, we used numerical simulations to investigate the capacity of physical obstacles placed on a flat substrate to shape structural connectivity, and in turn collective dynamics and effective connectivity, in biologically-realistic neuronal networks. We considered μ-sized obstacles placed in mm-sized networks. Three main obstacle shapes were explored, namely crosses, circles and triangles of isosceles profile. They occupied either a small area fraction of the substrate or populated it entirely in a periodic manner. From the point of view of structure, all obstacles promoted short length-scale connections, shifted the in- and out-degree distributions toward lower values, and increased the modularity of the networks. The capacity of obstacles to shape distinct structural traits depended on their density and the ratio between axonal length and substrate diameter. For high densities, different features were triggered depending on obstacle shape, with crosses trapping axons in their vicinity and triangles funneling axons along the reverse direction of their tip. From the point of view of dynamics, obstacles reduced the capacity of networks to spontaneously activate, with triangles in turn strongly dictating the direction of activity propagation. Effective connectivity networks, inferred using transfer entropy, exhibited distinct modular traits, indicating that the presence of obstacles facilitated the formation of local effective microcircuits. Our study illustrates the potential of physical constraints to shape structural blueprints and remodel collective activity, and may guide investigations aimed at mimicking organizational traits of biological neuronal circuits

GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data

Detecting and discovering new gene interactions based on known gene expressions and gene interaction data presents a significant challenge. Various statistical and deep learning methods have attempted to tackle this challenge by leveraging the topological structure of gene interactions and gene expression patterns to predict novel gene interactions. In contrast, some approaches have focused exclusively on utilizing gene expression profiles. In this context, we introduce GENER, a parallel-layer deep learning network designed exclusively for the identification of gene-gene relationships using gene expression data. We conducted two training experiments and compared the performance of our network with that of existing statistical and deep learning approaches. Notably, our model achieved an average AUROC score of 0.834 on the combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting gene-gene interactions

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast

Under embargo until: 2021-12-17Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the YEASTRACT database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.acceptedVersio

In Vitro Development of Human iPSC-Derived Functional Neuronal Networks on Laser-Fabricated 3D Scaffolds

Neural progenitor cells generated from human induced pluripotent stem cells (hiPSCs) are the forefront of ″brain-on-chip″ investigations. Viable and functional hiPSC-derived neuronal networks are shaping powerful in vitro models for evaluating the normal and abnormal formation of cortical circuits, understanding the underlying disease mechanisms, and investigating the response to drugs. They therefore represent a desirable instrument for both the scientific community and the pharmacological industry. However, culture conditions required for the full functional maturation of individual neurons and networks are still unidentified. It has been recognized that three-dimensional (3D) culture conditions can better emulate in vivo neuronal tissue development compared to 2D cultures and thus provide a more desirable in vitro approach. In this paper, we present the design and implementation of a 3D scaffold platform that supports and promotes intricate neuronal network development. 3D scaffolds were produced through direct laser writing by two-photon polymerization (2PP), a high-resolution 3D laser microstructuring technology, using the biocompatible and nondegradable photoreactive resin Dental LT Clear (DClear). Neurons developed and interconnected on a 3D environment shaped by vertically stacked scaffold layers. The developed networks could support different cell types. Starting at the day 50 of 3D culture, neuronal progenitor cells could develop into cortical projection neurons (CNPs) of all six layers, different types of inhibitory neurons, and glia. Additionally and in contrast to 2D conditions, 3D scaffolds supported the long-term culturing of neuronal networks over the course of 120 days. Network health and functionality were probed through calcium imaging, which revealed a strong spontaneous neuronal activity that combined individual and collective events. Taken together, our results highlight advanced microstructured 3D scaffolds as a reliable platform for the 3D in vitro modeling of neuronal functions.publishedVersio

Etude du diagramme de phases des solutions d'électrolytes sous conditions extrêmes

The study of amorphous and crystalline phases of solutions gives essential insight on the behaviour of water under conditions relevant for biology and planetary science. The aim of this work is the exploration of the phase diagram of common electrolyte solutions (LiCl-water, NaCl-water) under pressure and temperature conditions (from 77 K to 330 K and up to 5 GPa) relevant for icy bodies such as Europe and Ganymede. In experiments and simulations we search for crystalline phases of ice at high-pressure, which can contain considerable amounts of salt in their lattice (up to 10 % of by weight). We probe the existence of these salty ices, and characterize two exotic, pressure induced properties, polyamorphism and ionic inclusions in the ice lattice. We have produced highly concentrated amorphous solutions of NaCl in water by fast quenching to liquid nitrogen temperature. Our neutron and X-ray diffraction experiments show that the local structure of this amorphous solution at ambient pressure is very similar to the high density amorphous structure of pure water. Our high-pressure experiments with the Paris-Edinburgh cell and our classical Molecular Dynamics calculations show only smooth structure and density changes during compression up to 4 GPa. We discuss the possibility of salt (NaCl) inclusions in the ice VII lattice at high pressure in our experiments by complementary calculations based on Density Functional Theory. The ice VII which crystallized in our experiments is either pure ice, or it contains only a small fraction of the ions from the solution. It may be possible that ions can be included in larger quantities at higher pressures.L’étude des phases amorphes et cristallines de solutions permet est d'un fort intérêt pour la biologie et la planétologie. Le but de cette thèse est l’exploration du diagramme de phase des solutions d’électrolytes (LiCl et NaCl dans l’eau) sous des conditions de pression et température typiques des corps glacés tels Europe et Ganymède (de 77 à 300 K et jusqu’à 5 GPa). Nous avons étudié des phases de glaces amorphes et cristallines pouvant incorporer des quantités considérables de sel (jusqu’à 10 % de masse de sel). En outre de la mise en évidence de phases de glace salées, nous avons caractérisé deux propriétés exotiques induites sous pression, à savoir le polyamorphisme et l’inclusion des ions dans le réseau de la glace. Nous avons produit des échantillons de phase amorphe de solutions de NaCl dans l’eau par trempe rapide à 77 Kelvin. Nos expériences de diffraction de neutron et de rayons X montrent que la structure locale de cette phase amorphe est très similaire de celle de la phase haute densité de l’eau pure. Nos expériences haute pression avec la presse Paris-Edinbourg et nos calculs de dynamique moléculaire montrent que la densité et la structure évoluent de manière continue en compression jusqu’à 4 GPa. La possibilité d’inclusion du sel (NaCl) dans le réseau de la glace VII sous pression dans nos expériences est analysée en comparaison avec des simulations utilisant la théorie de la densité fonctionnelle. La glace VII qui cristallise dans nos expériences est soit pure, soit elle contient une fraction faible des ions de la solution mère. Il est possible que des quantités de sel plus grandes puissent être incorporées à des pressions plus élevées

Investigating Mono- and Quadrupole Gravitational Light Deflection by Jupiter

Gravitational light deflection in the Solar System can be detected by high precision astrometric measurements. We discuss the parametrized post-Newtonian framework and the comparison of metric theories of gravity. At the precision of a few micro-arcseconds, Gaia data will permit tests of the PPN parameters beta and gamma and to distinguish monopole and quadrupole gravitational light deflection. Accounting for relativistic effects is necessary to achieve the aimed for precision. The theoretical formulation of light deflection is discussed. We deduce an expression for the source direction derivatives required by the AGIS scheme in a simplified relativistic model. This model accounting for monopole and quadrupole deflection terms has been implemented in AGISLab. We have validated the implementation and maintain convergence of the astrometric solution for Gaia

Astrometic Detection of Gravitational Light Deflection by Jupiter with Gaia Data

Gravitational light deflection in the Solar System can be detected by high precision astrometric measurements. We discuss the parametrized post-Newtonian framework and the comparison of metric theories of gravity. At the precision of a few micro-arcseconds, Gaia data will permit tests of the PPN parameters beta and gamma and to distinguish monopole and quadrupole gravitational light deflection. Accounting for relativistic effects is necessary to achieve the aimed for precision. The theoretical formulation of light deflection is discussed. We deduce an expression for the source direction derivatives required by the AGIS scheme in a simplified relativistic model. This model accounting for monopole and quadrupole deflection terms has been implemented in AGISLab. We have validated the implementation and maintain convergence of the astrometric solution for Gaia. We investigate the precision of the determination of PPN gamma with Gaia data for the Sun and planets using the new relativistic model for source direction computations. Simulations in AGISLab show that previously obtained precision for PPN gamma can be matched. Full precision Gaia data should allow for a determination down to 10-6. We performed realistic simulations including observation noise and conclude that quadrupole effect remains detectable with a 6 sigma confidence level even for a 5 arcsec radius of the exclusion zone around Jupiter

Exploring the phase diagram of electrolyte solutions under extreme conditions

L’étude des phases amorphes et cristallines de solutions permet est d'un fort intérêt pour la biologie et la planétologie. Le but de cette thèse est l’exploration du diagramme de phase des solutions d’électrolytes (LiCl et NaCl dans l’eau) sous des conditions de pression et température typiques des corps glacés tels Europe et Ganymède (de 77 à 300 K et jusqu’à 5 GPa). Nous avons étudié des phases de glaces amorphes et cristallines pouvant incorporer des quantités considérables de sel (jusqu’à 10 % de masse de sel). En outre de la mise en évidence de phases de glace salées, nous avons caractérisé deux propriétés exotiques induites sous pression, à savoir le polyamorphisme et l’inclusion des ions dans le réseau de la glace. Nous avons produit des échantillons de phase amorphe de solutions de NaCl dans l’eau par trempe rapide à 77 Kelvin. Nos expériences de diffraction de neutron et de rayons X montrent que la structure locale de cette phase amorphe est très similaire de celle de la phase haute densité de l’eau pure. Nos expériences haute pression avec la presse Paris-Edinbourg et nos calculs de dynamique moléculaire montrent que la densité et la structure évoluent de manière continue en compression jusqu’à 4 GPa. La possibilité d’inclusion du sel (NaCl) dans le réseau de la glace VII sous pression dans nos expériences est analysée en comparaison avec des simulations utilisant la théorie de la densité fonctionnelle. La glace VII qui cristallise dans nos expériences est soit pure, soit elle contient une fraction faible des ions de la solution mère. Il est possible que des quantités de sel plus grandes puissent être incorporées à des pressions plus élevées.The study of amorphous and crystalline phases of solutions gives essential insight on the behaviour of water under conditions relevant for biology and planetary science. The aim of this work is the exploration of the phase diagram of common electrolyte solutions (LiCl-water, NaCl-water) under pressure and temperature conditions (from 77 K to 330 K and up to 5 GPa) relevant for icy bodies such as Europe and Ganymede. In experiments and simulations we search for crystalline phases of ice at high-pressure, which can contain considerable amounts of salt in their lattice (up to 10 % of by weight). We probe the existence of these salty ices, and characterize two exotic, pressure induced properties, polyamorphism and ionic inclusions in the ice lattice. We have produced highly concentrated amorphous solutions of NaCl in water by fast quenching to liquid nitrogen temperature. Our neutron and X-ray diffraction experiments show that the local structure of this amorphous solution at ambient pressure is very similar to the high density amorphous structure of pure water. Our high-pressure experiments with the Paris-Edinburgh cell and our classical Molecular Dynamics calculations show only smooth structure and density changes during compression up to 4 GPa. We discuss the possibility of salt (NaCl) inclusions in the ice VII lattice at high pressure in our experiments by complementary calculations based on Density Functional Theory. The ice VII which crystallized in our experiments is either pure ice, or it contains only a small fraction of the ions from the solution. It may be possible that ions can be included in larger quantities at higher pressures

Impact of physical obstacles on the structural and effective connectivity of in silico neuronal circuits

Scaffolds and patterned substrates are among the most successful strategies to dictate the connectivity between neurons in culture. Here, we used numerical simulations to investigate the capacity of physical obstacles placed on a flat substrate to shape structural connectivity, and in turn collective dynamics and effective connectivity, in biologically-realistic neuronal networks. We considered μ-sized obstacles placed in mm-sized networks. Three main obstacle shapes were explored, namely crosses, circles and triangles of isosceles profile. They occupied either a small area fraction of the substrate or populated it entirely in a periodic manner. From the point of view of structure, all obstacles promoted short length-scale connections, shifted the in- and out-degree distributions toward lower values, and increased the modularity of the networks. The capacity of obstacles to shape distinct structural traits depended on their density and the ratio between axonal length and substrate diameter. For high densities, different features were triggered depending on obstacle shape, with crosses trapping axons in their vicinity and triangles funneling axons along the reverse direction of their tip. From the point of view of dynamics, obstacles reduced the capacity of networks to spontaneously activate, with triangles in turn strongly dictating the direction of activity propagation. Effective connectivity networks, inferred using transfer entropy, exhibited distinct modular traits, indicating that the presence of obstacles facilitated the formation of local effective microcircuits. Our study illustrates the potential of physical constraints to shape structural blueprints and remodel collective activity, and may guide investigations aimed at mimicking organizational traits of biological neuronal circuits