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      Macromolecular crowding in human tenocyte and skin fibroblast cultures: A comparative analysis

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          Abstract

          Although human tenocytes and dermal fibroblasts have shown promise in tendon engineering, no tissue engineered medicine has been developed due to the prolonged ex vivo time required to develop an implantable device. Considering that macromolecular crowding has the potential to substantially accelerate the development of functional tissue facsimiles, herein we compared human tenocyte and dermal fibroblast behaviour under standard and macromolecular crowding conditions to inform future studies in tendon engineering. Basic cell function analysis made apparent the innocuousness of macromolecular crowding for both cell types. Gene expression analysis of the without macromolecular crowding groups revealed expression of tendon related molecules in human dermal fibroblasts and tenocytes. Protein electrophoresis and immunocytochemistry analyses showed significantly increased and similar deposition of collagen fibres by macromolecular crowding in the two cell types. Proteomics analysis demonstrated great similarities between human tenocyte and dermal fibroblast cultures, as well as the induction of haemostatic, anti-microbial and tissue-protective proteins by macromolecular crowding in both cell populations. Collectively, these data rationalise the use of either human dermal fibroblasts or tenocytes in combination with macromolecular crowding in tendon engineering.

          Graphical abstract

          Macromolecular crowding in human dermal fibroblast and human tenocyte cultures.

          Highlights

          • Dermal fibroblasts express tendon regulatory genes and proteins.

          • Macromolecular crowding enhances dermal fibroblast and tenocyte collagen deposition.

          • Dermal fibroblasts and tenocytes produce similar in composition extracellular matrix.

          • Dermal fibroblasts and tenocytes quantitative proteome show great similarity.

          • Dermal fibroblasts and macromolecular crowding show promise for tendon engineering.

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          Most cited references210

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Mater Today Bio
                Mater Today Bio
                Materials Today Bio
                Elsevier
                2590-0064
                28 January 2024
                April 2024
                28 January 2024
                : 25
                : 100977
                Affiliations
                [a ]Laboratory of Animal Science, Nutrition and Biotechnology, School of Agriculture, University of Ioannina, Arta, Greece
                [b ]School of Veterinary Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
                [c ]Regenerative, Modular & Developmental Engineering Laboratory (REMODEL), Charles Institute of Dermatology, Conway Institute of Biomolecular & Biomedical Research and School of Mechanical & Materials Engineering, University College Dublin (UCD), Dublin, Ireland
                [d ]Proteomics Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
                [e ]Merlin Park University Hospital, Galway, Ireland
                [f ]Galway University Hospital, Galway, Ireland
                [g ]Science Foundation Ireland (SFI) Centre for Research in Medical Devices (CÚRAM), Biomedical Sciences Building, University of Galway, Galway, Ireland
                Author notes
                []Corresponding author. REMODEL, UCD, Ireland. dimitrios.zevgolis@ 123456ucd.ie
                Article
                S2590-0064(24)00036-X 100977
                10.1016/j.mtbio.2024.100977
                10846491
                38322661
                335475cc-9ca9-473e-affb-183e114b4bcb
                © 2024 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 4 October 2023
                : 22 December 2023
                : 24 January 2024
                Categories
                Full Length Article

                tendon engineering,dermal fibroblasts,tenocytes,macromolecular crowding,proteomics

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