Högskolan i Skövde

Vascular diseases are the largest cause of death globally and impose a major global burden on healthcare. The gold standard for treating vascular diseases is the transplantation of autologous veins, if applicable. Alternative treatments still suffer from shortcomings, including low patency, lack of growth potential, the need for repeated intervention, and a substantial risk of developing infections. The use of a vascular ECM scaffold reconditioned with the patient's own cells has shown successful results in preclinical and clinical studies. In this study, we have compared the proteomes of personalized tissue-engineered veins of humans and pigs. By applying tandem mass tag (TMT) labeling LC/MS-MS, we have investigated the proteome of decellularized (DC) veins from humans and pigs and reconditioned (RC) DC veins produced through perfusion with the patient's whole blood in STEEN solution, applying the same technology as used in the preclinical studies. The results revealed high similarity between the proteomes of human and pig DC and RC veins, including the ECM texture after decellularization and reconditioning. In addition, functional enrichment analysis showed similarities in signaling pathways and biological processes involved in the immune system response. Furthermore, the classification of proteins involved in immune response activity that were detected in human and pig RC veins revealed proteins that evoke immunogenic responses, which may lead to graft rejection, thrombosis, and inflammation. However, the results from this study imply the initiation of wound healing rather than an immunogenic response, as both systems share the same processes, and no immunogenic response was reported in the preclinical and clinical studies. Finally, our study assessed the application of STEEN solution in tissue engineering and identified proteins that may be useful for the prediction of successful transplantations. 

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.

Human brain tissue models such as cerebral organoids are essential tools for developmental and biomedical research. Current methods to generate cerebral organoids often utilize Matrigel as an external scaffold to provide structure and biologically relevant signals. Matrigel however is a nonspecific hydrogel of mouse tumor origin and does not represent the complexity of the brain protein environment. In this study, we investigated the application of a decellularized adult porcine brain extracellular matrix (B-ECM) which could be processed into a hydrogel (B-ECM hydrogel) to be used as a scaffold for human embryonic stem cell (hESC)-derived brain organoids. We decellularized pig brains with a novel detergent- and enzyme-based method and analyzed the biomaterial properties, including protein composition and content, DNA content, mechanical characteristics, surface structure, and antigen presence. Then, we compared the growth of human brain organoid models with the B-ECM hydrogel or Matrigel controls in vitro. We found that the native brain source material was successfully decellularized with little remaining DNA content, while Mass Spectrometry (MS) showed the loss of several brain-specific proteins, while mainly different collagen types remained in the B-ECM. Rheological results revealed stable hydrogel formation, starting from B-ECM hydrogel concentrations of 5 mg/mL. hESCs cultured in B-ECM hydrogels showed gene expression and differentiation outcomes similar to those grown in Matrigel. These results indicate that B-ECM hydrogels can be used as an alternative scaffold for human cerebral organoid formation, and may be further optimized for improved organoid growth by further improving protein retention other than collagen after decellularization.

Human pluripotent stem cell-derived hepatocytes (hPSC-HEP) display many properties of mature hepatocytes, including expression of important genes of the drug metabolizing machinery, glycogen storage, and production of multiple serum proteins. To this date, hPSC-HEP do not, however, fully recapitulate the complete functionality of in vivo mature hepatocytes. In this study, we applied versatile bioinformatic algorithms, including functional annotation and pathway enrichment analyses, transcription factor binding-site enrichment, and similarity and correlation analyses, to datasets collected from different stages during hPSC-HEP differentiation and compared these to developmental stages and tissues from fetal and adult human liver. Our results demonstrate a high level of similarity between the in vitro differentiation of hPSC-HEP and in vivo hepatogenesis. Importantly, the transcriptional correlation of hPSC-HEP with adult liver (AL) tissues was higher than with fetal liver (FL) tissues (0.83 and 0.70, respectively). Functional data revealed mature features of hPSC-HEP including cytochrome P450 enzymes activities and albumin secretion. Moreover, hPSC-HEP showed expression of many genes involved in drug absorption, distribution, metabolism, and excretion. Despite the high similarities observed, we identified differences of specific pathways and regulatory players by analyzing the gene expression between hPSC-HEP and AL. These findings will aid future intervention and improvement of in vitro hepatocyte differentiation protocol in order to generate hepatocytes displaying the complete functionality of mature hepatocytes. Finally, on the transcriptional level, our results show stronger correlation and higher similarity of hPSC-HEP to AL than to FL. In addition, potential targets for further functional improvement of hPSC-HEP were also identified. 

 Efficiency in dealing with batch effects will be the next frontier in large-scale biological data analysis, particularly when involving the integration of different types of datasets. Large-scale omics techniques have quickly developed during the last decade and huge amounts of data are now generated, which has started to revolutionize the area of medical research. With the increase in the volume of data across the whole spectrum of biology, problems related to data analytics are continuously increasing as analysis and interpretation of these large volumes of molecular data has become a real challenge. Tremendous efforts have been made to obtain data from various molecular levels and the most recent trends show that more and more researchers now are trying to integrate data of various molecular types to inform hypotheses and biological questions. Tightly connected to this work are the batch-related biases that commonly are apparent between different datasets, but these problems are often not tackled. In present study the ComBat algorithm was applied and evaluated on two different data integration problems. Results show that the batch effects present in the integrated datasets efficiently could be removed by applying the ComBat algorithm.

Hepatocytes derived from human pluripotent stem cells (hPSC-HEP) have the potential to replace presently used hepatocyte sources applied in liver disease treatment and models of drug discovery and development. Established hepatocyte differentiation protocols are effective and generate hepatocytes, which recapitulate some key features of their in vivo counterparts. However, generating mature hPSC-HEP remains a challenge. In this study, we applied transcriptomics to investigate the progress of in vitro hepatic differentiation of hPSCs at the developmental stages, definitive endoderm, hepatoblasts, early hPSC-HEP, and mature hPSC-HEP, to identify functional targets that enhance efficient hepatocyte differentiation. Using functional annotation, pathway and protein interaction network analyses, we observed the grouping of differentially expressed genes in specific clusters representing typical developmental stages of hepatic differentiation. In addition, we identified hub proteins and modules that were involved in the cell cycle process at early differentiation stages. We also identified hub proteins that differed in expression levels between hPSC-HEP and the liver tissue controls. Moreover, we identified a module of genes that were expressed at higher levels in the liver tissue samples than in the hPSC-HEP. Considering that hub proteins and modules generally are essential and have important roles in the protein-protein interactions, further investigation of these genes and their regulators may contribute to a better understanding of the differentiation process. This may suggest novel target pathways and molecules for improvement of hPSC-HEP functionality, having the potential to finally bring this technology to a wider use.

Human pluripotent stem cells- (hPSCs-) derived hepatocytes have the potential to replace many hepatic models in drug discovery and provide a cell source for regenerative medicine applications. However, the generation of fully functional hPSC-derived hepatocytes is still a challenge. Towards gaining better understanding of the differentiation and maturation process, we employed a standardized protocol to differentiate six hPSC lines into hepatocytes and investigated the synchronicity of the hPSC lines by applying RT-qPCR to assess the expression of lineage-specific genes (OCT4, NANOG, T, SOX17, CXCR4, CER1, HHEX, TBX3, PROX1, HNF6, AFP, HNF4a, KRT18, ALB, AAT, and CYP3A4) which serve as markers for different stages during liver development. The data was evaluated using correlation and clustering analysis, demonstrating that the expression of these markers is highly synchronized and correlated well across all cell lines. The analysis also revealed a distribution of the markers in groups reflecting the developmental stages of hepatocytes. Functional analysis of the differentiated cells further confirmed their hepatic phenotype. Taken together, these results demonstrate, on the molecular level, the highly synchronized differentiation pattern across multiple hPSC lines. Moreover, this study provides additional understanding for future efforts to improve the functionality of hPSC-derived hepatocytes and thereby increase the value of related models.

Human hepatocytes display substantial functional inter-individual variation regarding drug metabolizing functions. In order to investigate if this diversity is mirrored in hepatocytes derived from different human pluripotent stem cell (hPSC) lines, we evaluated 25 hPSC lines originating from 24 different donors for hepatic differentiation and functionality. Homogenous hepatocyte cultures could be derived from all hPSC lines using onestandardized differentiation procedure. To the best of our knowledge this is the first report of a standardized hepatic differentiation procedure that is generally applicable across a large panel of hPSC lines without any adaptations to individual lines. Importantly, with regard to functional aspects, such as Cytochrome P450 activities, we observed that hepatocytes derived from different hPSC lines displayed inter-individual variation characteristic for primary hepatocytes obtained from different donors, while these activities were highly reproducible between repeated experiments using the same line. Taken together, these data demonstrate the emerging possibility to compile panels of hPSC-derived hepatocytes of particular phenotypes/genotypes relevant for drug metabolism and toxicity studies. Moreover, these findings are of significance for applications within the regenerative medicine field, since our stringent differentiation procedure allows the derivation of homogenous hepatocyte cultures from multiple donors which is a prerequisite for the realization of future personalized stem cell based therapies.

Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells (hiPSCs), obtained during directed differentiation towards endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by quantitative PCR (RT-qPCR) analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs.