scholarly journals 1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

2018 ◽  
Vol 45 (S3) ◽  
pp. S13-S14
Author(s):  
VG LeBlanc ◽  
D Trinh ◽  
M Hughes ◽  
I Luthra ◽  
D Livingstone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tissue Sample ◽  
Expression Patterns ◽  
Model Systems ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Original Tumour ◽  
Current Therapies ◽  
Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

scholarly journals P13.10 Glioblastoma within the subventricular zone associates with increased mesenchymal transition: an intratumoral gene expression analysis

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii64-iii64
Author(s):  
S Berendsen ◽  
D Dalemans ◽  
K Draaisma ◽  
P A Robe ◽  
T J Snijders
Keyword(s):  
Gene Expression ◽  
Subventricular Zone ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
En Bloc ◽  
Tissue Samples ◽  
Mesenchymal Transition ◽  
Gene Set

Abstract BACKGROUND Involvement of the subventricular zone (SVZ) in GBM is associated with poor prognosis and suggested to associate with specific tumor-biological characteristics. The SVZ microenvironment can influence gene expression and migration in GBM cells in preclinical models. We aimed to investigate whether the SVZ microenvironment has any influence on intratumoral gene expression patterns in GBM patients. MATERIAL AND METHODS The publicly available Ivy GBM database contains clinical, radiological and whole exome sequencing data from multiple regions from en bloc resected GBMs. SVZ involvement of the various tissue samples was evaluated on MRI scans. In the tumors that contacted the SVZ, we performed gene expression analyses and gene set enrichment analyses to compare gene (set) expression in tumor regions within the SVZ to tumor regions outside the SVZ, within the same tumors. We also compared these samples to GBMs that made no contact with the SVZ. RESULTS Within GBMs that contacted the SVZ, tissue samples within the SVZ showed enrichment of gene sets involved in (epithelial-)mesenchymal transition, NF-κB and STAT3 signaling, angiogenesis and hypoxia, compared to the samples outside of the SVZ region from the same tumors (p<0.05, FDR<0.25). Comparison of GBM samples within the SVZ region to samples from tumors that did not contact the SVZ yielded similar results. In contrast, we observed no difference in gene set enrichment when comparing the samples outside of the SVZ from SVZ-contacting GBMs with samples from GBMs that did not contact the SVZ at all. CONCLUSION GBM samples in the SVZ region associate with increased (epithelial-)mesenchymal transition and angiogenesis/hypoxia signaling, possibly mediated by the SVZ microenvironment.

scholarly journals A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

2021 ◽  
Author(s):  
Manuel Neumann ◽  
Xiaocai Xu ◽  
Cezary Smaczniak ◽  
Julia Schumacher ◽  
Wenhao Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Floral Meristem ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Gene Expression Atlas ◽  
Spatial Reconstruction ◽  
Expression Atlas ◽  
3D Gene

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing

2020 ◽  
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Local Community ◽  
Expression Patterns ◽  
Imputation Method ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data therefore need to be carefully processed before in-depth analysis. Here we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local community of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single cell sequencing), on six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

scholarly journals Glioblastomas within the Subventricular Zone Are Region-Specific Enriched for Mesenchymal Transition Markers: An Intratumoral Gene Expression Analysis

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3764
Author(s):  
Diana J. Z. Dalemans ◽  
Sharon Berendsen ◽  
Kaspar Draaisma ◽  
Pierre A. Robe ◽  
Tom J. Snijders
Keyword(s):  
Gene Expression ◽  
Subventricular Zone ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Mesenchymal Transition ◽  
Gene Set ◽  
Multiple Regions ◽  
Mri Scans

Background: Involvement of the subventricular zone (SVZ) in glioblastoma is associated with poor prognosis and is associated with specific tumor-biological characteristics. The SVZ microenvironment can influence gene expression in glioblastoma cells in preclinical models. We aimed to investigate whether the SVZ microenvironment has any influence on intratumoral gene expression patterns in glioblastoma patients. Methods: The publicly available Ivy Glioblastoma database contains clinical, radiological and whole exome sequencing data from multiple regions from resected glioblastomas. SVZ involvement of the various tissue samples was evaluated on MRI scans. In tumors that contacted the SVZ, we performed gene expression analyses and gene set enrichment analyses to compare gene (set) expression in tumor regions within the SVZ to tumor regions outside the SVZ. We also compared these samples to glioblastomas that did not contact the SVZ. Results: Within glioblastomas that contacted the SVZ, tissue samples within the SVZ showed enrichment of gene sets involved in (epithelial-)mesenchymal transition, NF-κB and STAT3 signaling, angiogenesis and hypoxia, compared to the samples outside of the SVZ region from the same tumors (p < 0.05, FDR < 0.25). Comparison of glioblastoma samples within the SVZ region to samples from tumors that did not contact the SVZ yielded similar results. In contrast, we observed no differences when comparing the samples outside of the SVZ from SVZ-contacting glioblastomas with samples from glioblastomas that did not contact the SVZ at all. Conclusion: Glioblastoma samples in the SVZ region are enriched for increased (epithelial-)mesenchymal transition and angiogenesis/hypoxia signaling, possibly mediated by the SVZ microenvironment.

scholarly journals Isoform-level gene expression patterns in single-cell RNA-sequencing data

2018 ◽  
Vol 34 (14) ◽  
pp. 2392-2400 ◽  
Author(s):  
Trung Nghia Vu ◽  
Quin F Wills ◽  
Krishna R Kalari ◽  
Nifang Niu ◽  
Liewei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

2020 ◽  
Author(s):  
Edward Zhao ◽  
Matthew R. Stone ◽  
Xing Ren ◽  
Thomas Pulliam ◽  
Paul Nghiem ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Patterns ◽  
Resolution Enhancement ◽  
Sequencing Data ◽  
Tissue Sections ◽  
Transcriptomics Data ◽  
Fully Bayesian ◽  
Comprehensive Measurement

AbstractRecently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace’s improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

scholarly journals Co-expression of cancer driver genes: IDH-wildtype glioblastoma-derived tumorspheres

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Seon-Jin Yoon ◽  
Hye Young Son ◽  
Jin-Kyoung Shim ◽  
Ju Hyung Moon ◽  
Eui-Hyun Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Noncoding Rnas ◽  
Tissue Level ◽  
Long Noncoding Rnas ◽  
Driver Gene ◽  
Driver Genes ◽  
Tissue Samples

Abstract Background Driver genes of GBM may be crucial for the onset of isocitrate dehydrogenase (IDH)-wildtype (WT) glioblastoma (GBM). However, it is still unknown whether the genes are expressed in the identical cluster of cells. Here, we have examined the gene expression patterns of GBM tissues and patient-derived tumorspheres (TSs) and aimed to find a progression-related gene. Methods We retrospectively collected primary IDH-WT GBM tissue samples (n = 58) and tumor-free cortical tissue samples (control, n = 20). TSs are isolated from the IDH-WT GBM tissue with B27 neurobasal medium. Associations among the driver genes were explored in the bulk tissue, bulk cell, and a single cell RNAsequencing techniques (scRNAseq) considering the alteration status of TP53, PTEN, EGFR, and TERT promoter as well as MGMT promoter methylation. Transcriptomic perturbation by temozolomide (TMZ) was examined in the two TSs. Results We comprehensively compared the gene expression of the known driver genes as well as MGMT, PTPRZ1, or IDH1. Bulk RNAseq databases of the primary GBM tissue revealed a significant association between TERT and TP53 (p < 0.001, R = 0.28) and its association increased in the recurrent tumor (p  < 0.001, R = 0.86). TSs reflected the tissue-level patterns of association between the two genes (p < 0.01, R = 0.59, n = 20). A scRNAseq data of a TS revealed the TERT and TP53 expressing cells are in a same single cell cluster. The driver-enriched cluster dominantly expressed the glioma-associated long noncoding RNAs. Most of the driver-associated genes were downregulated after TMZ except IGFBP5. Conclusions GBM tissue level expression patterns of EGFR, TERT, PTEN, IDH1, PTPRZ1, and MGMT are observed in the GBM TSs. The driver gene-associated cluster of the GBM single cells were enriched with the glioma-associated long noncoding RNAs.

scholarly journals Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

2021 ◽  
Author(s):  
Rajeev Vikram ◽  
Wen□Cheng Chou ◽  
Pei-Ei Wu ◽  
Wei-Ting Chen ◽  
Chen-Yang Shen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
High Grade ◽  
Sequencing Data ◽  
Quiescent Cells ◽  
Single Cell Rna Sequencing ◽  
Tumorigenic Cells

ABSTRACTBackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis.MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells.ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells.ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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