Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 – S – G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.
Members of the human FET family of RNA‐binding proteins, comprising FUS, EWSR1, and TAF15, are ubiquitously expressed and engage at several levels of gene regulation. Many sarcomas and leukemias are characterized by the expression of fusion oncogenes with FET genes as 5′ partners and alternative transcription factor‐coding genes as 3′ partners. Here, we report that the N terminus of normal FET proteins and their oncogenic fusion counterparts interact with the SWI/SNF chromatin remodeling complex. In contrast to normal FET proteins, increased fractions of FET oncoproteins bind SWI/SNF, indicating a deregulated and enhanced interaction in cancer. Forced expression of FET oncogenes caused changes of global H3K27 trimethylation levels, accompanied by altered gene expression patterns suggesting a shift in the antagonistic balance between SWI/SNF and repressive polycomb group complexes. Thus, deregulation of SWI/SNF activity could provide a unifying pathogenic mechanism for the large group of tumors caused by FET fusion oncoproteins. These results may help to develop common strategies for therapy.
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.
The need to perform gene expression profiling using next generation sequencing and quantitative real-time PCR (qPCR) on small sample sizes and single cells is rapidly expanding. However, to analyse few molecules, preamplification is required. Here, we studied global and target-specific preamplification using 96 optimised qPCR assays. To evaluate the preamplification strategies, we monitored the reactions in real-time using SYBR Green I detection chemistry followed by melting curve analysis. Next, we compared yield and reproducibility of global preamplification to that of target-specific preamplification by qPCR using the same amount of total RNA. Global preamplification generated 9.3-fold lower yield and 1.6-fold lower reproducibility than target-specific preamplification. However, the performance of global preamplification is sufficient for most downstream applications and offers several advantages over target-specific preamplification. To demonstrate the potential of global preamplification we analysed the expression of 15 genes in 60 single cells. In conclusion, we show that global preamplification simplifies targeted gene expression profiling of small sample sizes by a flexible workflow. We outline the pros and cons for global preamplification compared to target-specific preamplification.
Fusion oncogenes are among the most common types of oncogene in human cancers. The gene rearrangements result in new combinations of regulatory elements and functional protein domains. Here we studied a subgroup of sarcomas and leukaemias characterized by the FET (FUS, EWSR1, TAF15) family of fusion oncogenes, including FUS-DDIT3 in myxoid liposarcoma (MLS). We investigated the regulatory mechanisms, expression levels and effects of FUS-DDIT3 in detail. FUS-DDIT3 showed a lower expression than normal FUS at both the mRNA and protein levels, and single-cell analysis revealed a lack of correlation between FUS-DDIT3 and FUS expression. FUS-DDIT3 transcription was regulated by the FUS promotor, while its mRNA stability depended on the DDIT3 sequence. FUS-DDIT3 protein stability was regulated by protein interactions through the FUS part, rather than the leucine zipper containing DDIT3 part. In addition, in vitro as well as in vivo FUS-DDIT3 protein expression data displayed highly variable expression levels between individual MLS cells. Combined mRNA and protein analyses at the single-cell level showed that FUS-DDIT3 protein expression was inversely correlated to the expression of cell proliferation-associated genes. We concluded that FUS-DDIT3 is uniquely regulated at the transcriptional as well as the post-translational level and that its expression level is important for MLS tumour development. The FET fusion oncogenes are potentially powerful drug targets and detailed knowledge about their regulation and functions may help in the development of novel treatments.
Myxoid/round-cell liposarcoma (MLS/RCLS) is characterized by either the fusion gene FUS-DDIT3 or the less commonly occurring EWSR1-DDIT3 and most cases carry few or no additional cytogenetic changes. There are conflicting reports concerning the status and role of TP53 in MLS/RCLS. Here we analysed four MLS/RCLS derived cell lines for TP53 mutations, expression and function. Three SV40 transformed cell lines expressed normal TP53 proteins. Irradiation caused normal posttranslational modifications of TP53 and induced P21 expression in two of these cell lines. Transfection experiments showed that the FUS-DDIT3 fusion protein had no effects on irradiation induced TP53 responses. Ion Torrent AmpliSeq screening, using the Cancer Hotspot panel, showed no dysfunctional or disease associated alleles/mutations. In conclusion, our results suggest that most MLS/RCLS cases carry functional TP53 genes and this is consistent with the low numbers of secondary mutations observed in this tumor entity.
Myxoid sarcoma (MLS) is one of the most common types of malignant soft tissue tumors. MLS is characterized by the FUS-DDIT3 or EWSR1-DDIT3 fusion oncogenes that encode abnormal transcription factors. The receptor tyrosine kinase (RTK) encoding RET was previously identified as a putative downstream target gene to FUS-DDIT3 and here we show that cultured MLS cells expressed phosphorylated RET together with its ligand Persephin. Treatment with RET specific kinase inhibitor Vandetanib failed to reduce RET phosphorylation and inhibit cell growth, suggesting that other RTKs may phosphorylate RET. A screening pointed out EGFR and ERBB3 as the strongest expressed phosphorylated RTKs in MLS cells. We show that ERBB3 formed nuclear and cytoplasmic complexes with RET and both RTKs were previously reported to form complexes with EGFR. The formation of RTK hetero complexes could explain the observed Vandetanib resistence in MLS. EGFR and ERBB3 are clients of HSP90 that help complex formation and RTK activation. Treatment of cultured MLS cells with HSP90 inhibitor 17-DMAG, caused loss of RET and ERBB3 phosphorylation and lead to rapid cell death. Treatment of MLS xenograft carrying Nude mice resulted in massive necrosis, rupture of capillaries and hemorrhages in tumor tissues. We conclude that complex formation between RET and other RTKs may cause RTK inhibitor resistance. HSP90 inhibitors can overcome this resistance and are thus promising drugs for treatment of MLS/RCLS.
Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.
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