Co-culture of Adult Mesenchymal Stem Cells and Nucleus Pulposus Cells in Bilaminar Pellets for Intervertebral Disc Regeneration

Background: Our goal is to optimize stem cell-based tissue engineering strategies in the context of the intervertebral disc environment. We explored the benefits of co-culturing nucleus pulposus cells (NPC) and adult mesenchymal stem cells (MSC) using a novel spherical bilaminar pellet culture system where one cell type is enclosed in a sphere of the other cell type. Our 3D system provides a structure that exploits embryonic processes such as tissue induction and condensation. We observed a unique phenomenon: the budding of co-culture pellets and the formation of satellite pellets that separate from the main pellet. Methods: MSC and NPC co-culture pellets were formed with three different structural organizations. The first had random organization. The other two had bilaminar organization with either MSC inside and NPC outside or NPC inside and MSC outside. Results: By 14 days, all co-culture pellets exhibited budding and spontaneously generated satellite pellets. The satellite pellets were composed of both cell types and, surprisingly, all had the same bilaminar organization with MSC on the inside and NPC on the outside. This organization was independent of the structure of the main pellet that the satellites stemmed from. Conclusion: The main pellets generated satellite pellets that spontaneously organized into a bilaminar structure. This implies that structural organization occurs naturally in this cell culture system and may be inherently favorable for cell-based tissue engineering strategies. The occurrence of budding and the organization of satellite pellets may have important implications for the use of co-culture pellets in cell-based therapies for disc regeneration. Clinical Relevance: From a therapeutic point of view, the generation of satellite pellets may be a beneficial feature that would serve to spread donor cells throughout the host matrix and restore normal matrix composition in a sustainable way, ultimately renewing tissue function. © 2009 The Spine Arthroplasy Society

Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand

Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of building stock energy demand has so far been limited to cases where detailed, building specific data is available. This paper introduces two approaches of using synthetic building stock energy modelling (SBSEM) to model spatially distributed synthetic building stocks based on aggregate data. The two approaches build on different types of data that are implemented and validated for two separate case studies in Ireland and Austria. The results demonstrate the feasibility of both approaches to accurately reproduce the spatial distribution of the building stocks of the two cases. Furthermore, the results demonstrate that by using a SBSEM approach, a spatial analysis for building stock energy demand can be carried out for cases where no building level data is available and how these results may be used in energy planning

A convolutional neural network for segmentation of yeast cells without manual training annotations

MOTIVATION: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS: We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION: The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Stochastic generation of biologically accurate brain networks

Basic circuits, which form the building blocks of the brain, have been identiffied in recent literature. We propose to treat these basic circuits as "stochastic generators" whose instances serve to wire a portion of the mouse brain. Very much in the same manner as genes generate proteins by providing templates for their construction, we view the catalog of basic circuits as providing templates for wiring up the neurons of the brain. This thesis work involves a) deffining a framework for the stochastic generation of brain networks, b) generation of sample networks from the basic circuits, and c) visualization of the generated networks

ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches

Using synthetic biology to make cells tomorrow's test tubes

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting