Dataset Information

An easy-to-use and versatile method for building cell-laden microfibres.

ABSTRACT: Fibre-shaped materials are useful for creating different functional three-dimensional (3D) structures that could mimic complex tissues. Several methods (e.g. extrusion, laminar flow or electrospinning) have been proposed for building hydrogel microfibres, with distinctive cell types and with different degrees of complexity. However, these methods require numerous protocol adaptations in order to achieve fibre fabricating and lack the ability to control microfibre alignment. Here, we present a simple method for the production of microfibers, based on a core shell approach, composed of calcium alginate and type I collagen. The process presented here allows the removal of the calcium alginate shell, after only 24?hours of culture, leading to stable and reproducible fibre shaped cellular constructs. With time of culture cells show to distribute preferentially to the surface of the fibre and display a uniform cellular orientation. Moreover, when cultured inside the fibres, murine bone marrow mesenchymal stem cells show the capacity to differentiate towards the osteoblastic lineage, under non-osteoinductive culture conditions. This work establishes a novel method for cellular fibre fabrication that due to its inherent simplicity can be easily upscaled and applied to other cell types.

SUBMITTER: Kalisky J

PROVIDER: S-EPMC5018858 | biostudies-other | 2016

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

An easy-to-use and versatile method for building cell-laden microfibres.

Kalisky Jérome J Raso Jérémie J Rigothier Claire C Rémy Murielle M Siadous Robin R Bareille Reine R Fricain Jean-Christophe JC Amedée-Vilamitjana Joëlle J Oliveira Hugo H Devillard Raphaël R

Scientific reports 20160912

Fibre-shaped materials are useful for creating different functional three-dimensional (3D) structures that could mimic complex tissues. Several methods (e.g. extrusion, laminar flow or electrospinning) have been proposed for building hydrogel microfibres, with distinctive cell types and with different degrees of complexity. However, these methods require numerous protocol adaptations in order to achieve fibre fabricating and lack the ability to control microfibre alignment. Here, we present a si ...[more]

PMID: 27616467

Similar Datasets

Project description:Riemerella anatipestifer is a member of the family Flavobacteriaceae and a major causative agent of duck serositis. Little is known about its genetics and pathogenesis. Several bacteria are competent for natural transformation; however, whether R. anatipestifer is also competent for natural transformation has not been investigated. Here, we showed that R. anatipestifer strain ATCC 11845 can uptake the chromosomal DNA of R. anatipestifer strain RA-CH-1 in all growth phases. Subsequently, a natural transformation-based knockout method was established for R. anatipestifer ATCC 11845. Targeted mutagenesis gave transformation frequencies of ?10-5 transformants. Competition assay experiments showed that R. anatipestifer ATCC 11845 preferentially took up its own DNA rather than heterogeneous DNA, such as Escherichia coli DNA. Transformation was less efficient with the shuttle plasmid pLMF03 (transformation frequencies of ?10-9 transformants). However, the efficiency of transformation was increased approximately 100-fold using pLMF03 derivatives containing R. anatipestifer DNA fragments (transformation frequencies of ?10-7 transformants). Finally, we found that the R. anatipestifer RA-CH-1 strain was also naturally transformable, suggesting that natural competence is widely applicable for this species. The findings described here provide important tools for the genetic manipulation of R. anatipestiferIMPORTANCERiemerella anatipestifer is an important duck pathogen that belongs to the family Flavobacteriaceae At least 21 different serotypes have been identified. Genetic diversity has been demonstrated among these serotypes. The genetic and pathogenic mechanisms of R. anatipestifer remain largely unknown because no genetic tools are available for this bacterium. At present, natural transformation has been found in some bacteria but not in R. anatipestifer For the first time, we showed that natural transformation occurred in R. anatipestifer ATCC 11845 and R. anatipestifer RA-CH-1. Then, we established an easy gene knockout method in R. anatipestifer based on natural transformation. This information is important for further studies of the genetic diversity and pathogenesis in R. anatipestifer.

Project description:BackgroundThe recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.ResultsHere we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein.ConclusionsAs the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Dataset Information

An easy-to-use and versatile method for building cell-laden microfibres.

Publications

An easy-to-use and versatile method for building cell-laden microfibres.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets