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Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

ABSTRACT: Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glioblastoma stem-like cell (GSC) lines. Employing the self-organizing maps, we displayed the landscape of the lncRNA expression dynamics for individual cells. Further analyses revealed heterogeneous nature of lncRNA in abundance and splicing patterns. Moreover, lncRNA expression variation is also ubiquitously present in the established GSC lines composed of seemingly identical cells. Through comparative analysis of GSC and corresponding differentiated cell cultures, we defined a stemness signature by the set of 31 differentially expressed lncRNAs, which can disclose stemness gradients in five tumors. Additionally, based on known classifier lncRNAs for molecular subtypes, each tumor was found to comprise individual cells representing four subtypes. Our systematic characterization of lncRNA expression heterogeneity lays the foundation for future efforts to further understand the function of lncRNA, develop valuable biomarkers, and enhance knowledge of GBM biology.

SUBMITTER: Lv D

PROVIDER: S-EPMC4951297 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

Lv Dekang D Wang Xiang X Dong Jun J Zhuang Yan Y Huang Shuyu S Ma Binbin B Chen Puxiang P Li Xiaodong X Zhang Bo B Li Zhiguang Z Jin Bilian B

Oncotarget 20160401 14

Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glio ...[more]

PMID: 26918340

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Project description:Molecular imaging is essential for diagnosis and treatment planning for glioblastoma patients. Positron emission tomography (PET) with tracers for the detection of the solute carrier family 7 member 5 (SLC7A5; also known as the amino acid transporter light chain L system, LAT1) and for the mitochondrial translocator protein (TSPO) is successfully used to provide additional information on tumor volume and prognosis. The current approaches for TSPO-PET and the visualization of tracer ([18F] Fluoroethyltyrosine, FET) uptake by LAT1 (FET-PET) do not yet exploit the full diagnostic potential of these molecular imaging techniques. Therefore, we investigated the expression of TSPO and LAT1 in patient glioblastoma (GBM) samples, as well as in various GBM mouse models representing patient GBMs of different genetic subtypes. By immunohistochemistry, we found that TSPO and LAT1 are upregulated in human GBM samples compared to normal brain tissue. Next, we orthotopically implanted patient-derived GBM cells, as well as genetically engineered murine GBM cells, representing different genetic subtypes of the disease. To determine TSPO and LAT1 expression, we performed immunofluorescence staining. We found that both TSPO and LAT1 expression was increased in tumor regions of the implanted human or murine GBM cells when compared to the neighboring mouse brain tissue. While LAT1 was largely restricted to tumor cells, we found that TSPO was also expressed by microglia, tumor-associated macrophages, endothelial cells, and pericytes. The Cancer Genome Atlas (TCGA)-data analysis corroborates the upregulation of TSPO in a bigger cohort of GBM patient samples compared to tumor-free brain tissue. In addition, AIF1 (the gene encoding for the myeloid cell marker Iba1) was also upregulated in GBM compared to the control. Interestingly, TSPO, as well as AIF1, showed significantly different expression levels depending on the GBM genetic subtype, with the highest expression being exhibited in the mesenchymal subtype. High TSPO and AIF1 expression also correlated with a significant decrease in patient survival compared to low expression. In line with this finding, the expression levels for TSPO and AIF1 were also significantly higher in (isocitrate-dehydrogenase wild-type) IDHWT compared to IDH mutant (IDHMUT) GBM. LAT1 expression, on the other hand, was not different among the individual GBM subtypes. Therefore, we could conclude that FET- and TSPO-PET confer different information on pathological features based on different genetic GBM subtypes and may thus help in planning individualized strategies for brain tumor therapy in the future. A combination of TSPO-PET and FET-PET could be a promising way to visualize tumor-associated myeloid cells and select patients for treatment strategies targeting the myeloid compartment.

Project description:Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.

Dataset Information

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

Publications

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

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