| 1. |
- Dolatabadi, Soheila, et al.
(author)
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JAK–STAT signalling controls cancer stem cell properties including chemotherapy resistance in myxoid liposarcoma
- 2019
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In: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 145:2, s. 435-449
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Journal article (peer-reviewed)abstract
- Myxoid liposarcoma (MLS) shows extensive intratumoural heterogeneity with distinct subpopulations of tumour cells. Despite improved survival of MLS patients, existing therapies have shortcomings as they fail to target all tumour cells. The nature of chemotherapy-resistant cells in MLS remains unknown. Here, we show that MLS cell lines contained subpopulations of cells that can form spheres, efflux Hoechst dye and resist doxorubicin, all properties attributed to cancer stem cells (CSCs). By single-cell gene expression, western blot, phospho-kinase array, immunoprecipitation, immunohistochemistry, flow cytometry and microarray analysis we showed that a subset of MLS cells expressed JAK–STAT genes with active signalling. JAK1/2 inhibition via ruxolitinib decreased, while stimulation with LIF increased, phosphorylation of STAT3 and the number of cells with CSC properties indicating that JAK–STAT signalling controlled the number of cells with CSC features. We also show that phosphorylated STAT3 interacted with the SWI/SNF complex. We conclude that MLS contains JAK–STAT-regulated subpopulations of cells with CSC features. Combined doxorubicin and ruxolitinib treatment targeted both proliferating cells as well as cells with CSC features, providing new means to circumvent chemotherapy resistance in treatment of MLS patients. © 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC
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| 2. |
- Forootan, Amin, et al.
(author)
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Identification of Distinct and Common Subpopulations of Myxoid Liposarcoma and Ewing Sarcoma Cells Using Self-Organizing Maps
- 2023
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In: Chemosensors. - : MDPI AG. - 2227-9040. ; 11:1
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Journal article (peer-reviewed)abstract
- Myxoid liposarcoma and Ewing sarcoma are the two most common tumor types that are characterized by the FET (FUS, EWSR1 and TAF15) fusion oncogenes. These FET fusion oncogenes are considered to have the same pathological mechanism. However, the cellular similarities between cells from the different tumor entities remain unknown. Here, we profiled individual myxoid liposarcoma and Ewing sarcoma cells to determine common gene expression signatures. Five cell lines were analyzed, targeting 76 different genes. We employed unsupervised clustering, focusing on self-organizing maps, to identify biologically relevant subpopulations of tumor cells. In addition, we outlined the basic concepts of self-organizing maps. Principal component analysis and a t-distributed stochastic neighbor embedding plot showed gradual differences among all cells. However, we identified five distinct and robust subpopulations using self-organizing maps. Most cells were similar to other cells within the same tumor entity, but four out of five groups contained both myxoid liposarcoma and Ewing sarcoma cells. The major difference between the groups was the overall transcriptional activity, which could be linked to cell cycle regulation. We conclude that self-organizing maps are useful tools to define biologically relevant subpopulations and that myxoid liposarcoma and Ewing sarcoma exhibit cells with similar gene expression signatures.
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| 3. |
- Ståhlberg, Anders, 1975, et al.
(author)
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Multiway real-time PCR gene expression profiling in yeast Saccharomyces cerevisiae reveals altered transcriptional response of ADH-genes to glucose stimuli.
- 2008
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In: BMC genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 9
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Journal article (peer-reviewed)abstract
- BACKGROUND: The large sensitivity, high reproducibility and essentially unlimited dynamic range of real-time PCR to measure gene expression in complex samples provides the opportunity for powerful multivariate and multiway studies of biological phenomena. In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc. Here we perform a multiway study of the temporal response of four yeast Saccharomyces cerevisiae strains with different glucose uptake rates upon altered metabolic conditions. RESULTS: We measured the expression of 18 genes as function of time after addition of glucose to four strains of yeast grown in ethanol. The data are analyzed by matrix-augmented PCA, which is a generalization of PCA for 3-way data, and the results are confirmed by hierarchical clustering and clustering by Kohonen self-organizing map. Our approach identifies gene groups that respond similarly to the change of nutrient, and genes that behave differently in mutant strains. Of particular interest is our finding that ADH4 and ADH6 show a behavior typical of glucose-induced genes, while ADH3 and ADH5 are repressed after glucose addition. CONCLUSION: Multiway real-time PCR gene expression profiling is a powerful technique which can be utilized to characterize functions of new genes by, for example, comparing their temporal response after perturbation in different genetic variants of the studied subject. The technique also identifies genes that show perturbed expression in specific strains.
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| 4. |
- Ståhlberg, Anders, 1975, et al.
(author)
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RT-qPCR work-flow for single-cell data analysis.
- 2013
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In: Methods (San Diego, Calif.). - : Elsevier BV. - 1095-9130 .- 1046-2023. ; 59:1, s. 80-88
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Journal article (peer-reviewed)abstract
- Individual cells represent the basic unit in tissues and organisms and are in many aspects unique in their properties. The introduction of new and sensitive techniques to study single-cells opens up new avenues to understand fundamental biological processes. Well established statistical tools and recommendations exist for gene expression data based on traditional cell population measurements. However, these workflows are not suitable, and some steps are even inappropriate, to apply on single-cell data. Here, we present a simple and practical workflow for preprocessing of single-cell data generated by reverse transcription quantitative real-time PCR. The approach is demonstrated on a data set based on profiling of 41 genes in 303 single-cells. For some pre-processing steps we present options and also recommendations. In particular, we demonstrate and discuss different strategies for handling missing data and scaling data for downstream multivariate analysis. The aim of this workflow is provide guide to the rapidly growing community studying single-cells by means of reverse transcription quantitative real-time PCR profiling.
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