Högskolan i Skövde

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  • 1.
    Delsing, Louise
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. Department of Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden / Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Dönnes, Pierre
    SciCross AB, Skövde, Sweden.
    Sánchez, José
    Biostatistics, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Clausen, Maryam
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Voulgaris, Dmitrios
    Department of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden.
    Falk, Anna
    Department of Neuroscience, Karolinska Institutet, Stockholm.
    Herland, Anna
    Department of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden / Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
    Brolén, Gabriella
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Zetterberg, Henrik
    Department of Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden / iClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden / Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom / UK Dementia Research Institute at UCL, London, United Kingdom.
    Hicks, Ryan
    Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Barrier properties and transcriptome expression in human iPSC-derived models of the blood-brain barrier2018In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 36, no 12, p. 1816-1827Article in journal (Refereed)
    Abstract [en]

    Cell-based models of the blood-brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of inter-species differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in co-culture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our co-culture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in co-culture, including upregulation of tight junction proteins such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF and PI3K-Akt pathways upon co-culture. Our data suggests that co-culture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in co-culture can be used to further improve iPSC-derived BBB models through selective pathway manipulation.

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  • 2.
    Marzec-Schmidt, Katarzyna
    et al.
    Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Skara, Sweden.
    Ghosheh, Nidal
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Takara Bio Europe, Gothenburg, Sweden.
    Stahlschmidt, Sören Richard
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Küppers-Munther, Barbara
    Takara Bio Europe, Gothenburg, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Sweden.
    Ulfenborg, Benjamin
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Artificial intelligence supports automated characterization of differentiated human pluripotent stem cells2023In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 41, no 9, p. 850-861, article id sxad049Article in journal (Refereed)
    Abstract [en]

    Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

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  • 3.
    Olesen, Kim
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden ; Polymer Chemistry, Department of Chemistry – Ångström Laboratory, Uppsala University, Sweden.
    Rodin, Sergey
    Department of Surgical Sciences, Division of Cardiothoracic Surgery and Anaesthesiology, Uppsala University, Akademiska University Hospital, Uppsala, Sweden.
    Mak, Wing Cheung
    Biosensors and Bioelectronics Centre, Department of Physics, Chemistry and Biology (IFM), Linköping University, Sweden.
    Felldin, Ulrika
    Department of Surgical Sciences, Division of Cardiothoracic Surgery and Anaesthesiology, Uppsala University, Akademiska University Hospital, Uppsala, Sweden.
    Österholm, Cecilia
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Tilevik, Andreas
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Grinnemo, Karl-Henrik
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden ; Department of Surgical Sciences, Division of Cardiothoracic Surgery and Anaesthesiology, Uppsala University, Akademiska University Hospital, Uppsala, Sweden.
    Spatiotemporal extracellular matrix modeling for in situ cell niche studies2021In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 39, no 12, p. 1751-1765Article in journal (Refereed)
    Abstract [en]

    Extracellular matrix (ECM) components govern a range of cell functions such as migration, proliferation, maintenance of stemness and differentiation. Cell niches that harbor stem-/progenitor cells, with matching ECM, have been shown in a range of organs, although their presence in the heart is still under debate. Determining niches depends on a range of in vitro and in vivo models and techniques, where animal models are powerful tools for studying cell-ECM dynamics, however, they are costly and time-consuming to use. In vitro models based on recombinant ECM proteins lack the complexity of the in vivo ECM. To address these issues, we present the Spatiotemporal Extracellular Matrix Model (StEMM) for studies of cell-ECM dynamics, such as cell niches. This model combines gentle decellularization and sectioning of cardiac tissue, allowing retention of a complex ECM, with recellularization and subsequent image processing using image stitching, segmentation, automatic binning and generation of cluster maps. We have thereby developed an in situ representation of the cardiac ECM that is useful for assessment of repopulation dynamics and to study the effect of local ECM composition on phenotype preservation of reseeded mesenchymal progenitor cells. This model provides a platform for studies of organ-specific cell-ECM dynamics and identification of potential cell niches. © AlphaMed Press 2021 SIGNIFICANCE STATEMENT: Stem cells reside in adult organs within specific microenvironments called cell niches. The heart is a complex organ and so far, the presence and localization of stem-/progenitor cell niches are subject to constant debate. To address these issues, the authors have developed the Spatiotemporal Extracellular Matrix Model (StEMM), which combines a modified protocol for decellularization, with cryo-sectioning, recellularization, and subsequent image processing including automatic binning and generation of cluster maps. StEMM was developed within a cardiac context and validated using syngeneic mesenchymal progenitor cells. However, this model is not restricted with regard to species or organs.

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  • 4.
    Synnergren, Jane
    et al.
    University of Skövde, School of Humanities and Informatics.
    Giesler, Therese L.
    GE Healthcare, Piscataway, NJ, United States.
    Adak, Sudeshna
    GE John F. Welch Technology Centre Export Promotion Industrial Park, Bangalore, India.
    Tandon, Reeti
    GE John F. Welch Technology Centre Export Promotion Industrial Park, Bangalore, India.
    Noaksson, Karin
    Cellartis AB, Göteborg, Sweden.
    Lindahl, Anders
    Department of Clinical Chemistry/Transfusion Medicine, Sahlgrenska University Hospital, Göteborg, Sweden.
    Nilsson, Patric
    University of Skövde, School of Life Sciences.
    Nelson, Deirdre
    GE Global Research Center, Niskayuna, NY, United States.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Englund, Mikael C. O.
    Cellartis AB, Göteborg, Sweden.
    Abbott, Stewart
    GE Global Research Center, Niskayuna, NY, United States.
    Sartipy, Peter
    Cellartis AB, Göteborg, Sweden / Cellartis AB, Arvid Wallgrens Backe 20, SE-41346 Göteborg, Sweden.
    Differentiating human embryonic stem cells express a unique housekeeping gene signature2007In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 25, no 2, p. 473-480Article in journal (Refereed)
    Abstract [en]

    Housekeeping genes (HKGs) are involved in basic functions needed for the sustenance of the cell and are assumed to be constitutively expressed at a constant level. Based on these features, HKGs are frequently used for normalization of gene expression data. In the present study, we used the CodeLink Gene Expression Bioarray system to interrogate changes in gene expression occurring during differentiation of human ESCs (hESCs). Notably, in the three hESC lines used for the study, we observed that the RNA levels of 56 frequently used HKGs varied to a degree that rendered them inappropriate as reference genes. Therefore, we defined a novel set of HKGs specifically for hESCs. Here we present a comprehensive list of 292 genes that are stably expressed (coefficient of variation <20%) in differentiating hESCs. These genes were further grouped into high-, medium-, and low-expressed genes. The expression patterns of these novel HKGs show very little overlap with results obtained from somatic cells and tissues. We further explored the stability of this novel set of HKGs in independent, publicly available gene expression data from hESCs and observed substantial similarities with our results. Gene expression was confirmed by real-time quantitative polymerase chain reaction analysis. Taken together, these results suggest that differentiating hESCs have a unique HKG signature and underscore the necessity to validate the expression profiles of putative HKGs. In addition, this novel set of HKGs can preferentially be used as controls in gene expression analyses of differentiating hESCs.

  • 5.
    Synnergren, Jane
    et al.
    University of Skövde, School of Life Sciences.
    Åkesson, Karolina
    Cellartis AB, Gothenburg, Sweden.
    Dahlenborg, Kerstin
    Cellartis AB, Gothenburg, Sweden.
    Vidarsson, Hilmar
    Cellartis AB, Gothenburg, Sweden.
    Ameen, Caroline
    Cellartis AB, Gothenburg, Sweden.
    Steel, Daniella
    Cellartis AB, Gothenburg, Sweden.
    Lindahl, Anders
    Sahlgrens Univ Hosp, Dept Clin Chem Transfus Med, S-41345 Gothenburg, Sweden.
    Olsson, Björn
    University of Skövde, School of Life Sciences.
    Sartipy, Peter
    Cellartis AB, Gothenburg, Sweden.
    Molecular signature of cardiomyocyte clusters derived from human embryonic stem cells2008In: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 26, no 7, p. 1831-1840Article in journal (Refereed)
    Abstract [en]

    Human embryonic stem cells (hESCs) can differentiate in vitro into spontaneously contracting cardiomyocytes (CMs). These cells may prove extremely useful for various applications in basic research, drug discovery, and regenerative medicine. To fully use the potential of the cells, they need to be extensively characterized, and the regulatory mechanisms that control hESC differentiation toward the cardiac lineage need to be better defined. In this study, we used microarrays to analyze, for the first time, the global gene expression profile of isolated hESC-derived CM clusters. By comparing the clusters with undifferentiated hESCs and using stringent selection criteria, we identified 530 upregulated and 40 downregulated genes in the contracting clusters. To further characterize the family of upregulated genes in the hESC-derived CM clusters, the genes were classified according to their Gene Ontology annotation. The results indicate that the hESC-derived CM clusters display high similarities, on a molecular level, to human heart tissue. Moreover, using the family of upregulated genes, we created protein interaction maps that revealed topological characteristics. We also searched for cellular pathways among the upregulated genes in the hESC-derived CM clusters and identified eight significantly upregulated pathways. Real-time quantitative polymerase chain reaction and immunohistochemical analysis confirmed the expression of a subset of the genes identified by the microarrays. Taken together, the results presented here provide a molecular signature of hESC-derived CM clusters and further our understanding of the biological processes that are active in these cells.

1 - 5 of 5
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