Dataset Information

Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification.

ABSTRACT: Dysregulation of tyrosine kinase receptor (RTK) signaling pathways play important roles in glioblastoma (GBM). However, therapies targeting these signaling pathways have not been successful, partially because of drug resistance. Increasing evidence suggests that tumor heterogeneity, more specifically, GBM-associated stem and endothelial cell heterogeneity, may contribute to drug resistance. In this perspective article, we introduce a high-throughput, quantitative approach to profile plasma membrane RTKs on single cells. First, we review the roles of RTKs in cancer. Then, we discuss the sources of cell heterogeneity in GBM, providing context to the key cells directing resistance to drugs. Finally, we present our provisionally patented qFlow cytometry approach, and report results of a "proof of concept" patient-derived xenograft GBM study.

SUBMITTER: Chen S

PROVIDER: S-EPMC6050407 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification.

Chen Si S Le Thien T Harley Brendan A C BAC Imoukhuede P I PI

Frontiers in bioengineering and biotechnology 20180711

Dysregulation of tyrosine kinase receptor (RTK) signaling pathways play important roles in glioblastoma (GBM). However, therapies targeting these signaling pathways have not been successful, partially because of drug resistance. Increasing evidence suggests that tumor heterogeneity, more specifically, GBM-associated stem and endothelial cell heterogeneity, may contribute to drug resistance. In this perspective article, we introduce a high-throughput, quantitative approach to profile plasma membr ...[more]

PMID: 30050899

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Project description:BACKGROUND:Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure. With the advances in single-cell RNA Sequencing (scRNA-Seq), tumors can now be dissected at the cell level, unveiling information from their life history to their clinical implications. RESULTS:We propose a classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different cell populations (neoplastic and normal cells) are taken as classes. The goal is to identify gene features discriminating between the classes, but also those shared by different neoplastic clones. The latter will be approached via the network-based twiner regularizer to identify gene signatures shared by neoplastic cells from the tumor core and infiltrating neoplastic cells originated from the tumor periphery, as putative disease biomarkers to target multiple neoplastic clones. Our analysis is supported by the literature through the identification of several known molecular players in GBM. Moreover, the relevance of the selected genes was confirmed by their significance in the survival outcomes in bulk GBM RNA-Seq data, as well as their association with several Gene Ontology (GO) biological process terms. CONCLUSIONS:We presented a methodology intended to identify genes discriminating between GBM clones, but also those playing a similar role in different GBM neoplastic clones (including migrating cells), therefore potential targets for therapy research. Our results contribute to a deeper understanding on the genetic features behind GBM, by disclosing novel therapeutic directions accounting for GBM heterogeneity.

Project description:OBJECTIVES:The streptozotocin (STZ) model is widely used in diabetes research. However, the cellular and molecular states of pancreatic endocrine cells in this model remain unclear. This study explored the molecular characteristics of islet cells treated with STZ and re-evaluated ?-cell dysfunction and regeneration in the STZ model. METHODS:We performed single-cell RNA sequencing of pancreatic endocrine cells from STZ-treated mice. High-quality sequencing data from 2,999 cells were used to identify clusters via Louvain clustering analysis. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), force-directed layout (FDL), and differential expression analysis were used to define the heterogeneity and transcriptomic changes in islet cells. In addition, qPCR and immunofluorescence staining were used to confirm findings from the sequencing data. RESULTS:Untreated ?-cells were divided into two populations at the transcriptomic level, a large high-Glut2 expression (Glut2high) population and a small low-Glut2 expression (Glut2low) population. At the transcriptomic level, Glut2low ?-cells in adult mice did not represent a developmentally immature state, although a fraction of genes associated with ?-cell maturation and function were downregulated in Glut2low cells. After a single high-dose STZ treatment, most Glut2high cells were killed, but Glut2low cells survived and over time changed to a distinct cell state. We did not observe conversion of Glut2low to Glut2high ?-cells up to 9 months after STZ treatment. In addition, we did not detect transcriptomic changes in the non-? endocrine cells or a direct trans-differentiation pathway from the ?-cell lineage to the ?-cell lineage in the STZ model. CONCLUSIONS:We identified the heterogeneity of ?-cells in both physiological and pathological conditions. However, we did not observe conversion of Glut2low to Glut2high ?-cells, transcriptomic changes in the non-? endocrine cells, or direct trans-differentiation from the ?-cell lineage to the ?-cell lineage in the STZ model. Our results clearly define the states of islet cells treated with STZ and allow us to re-evaluate the STZ model widely used in diabetes studies.

Dataset Information

Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification.

Publications

Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification.

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