Dataset Information

Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry.

ABSTRACT: Most patients with glioblastoma (GBM) die within 2 years. A major therapeutic goal is to target GBM stem cells (GSCs), a subpopulation of cells that contribute to treatment resistance and recurrence. Since their discovery in 2003, GSCs have been isolated using single-surface markers, such as CD15, CD44, CD133, and ?6 integrin. It remains unknown how these single-surface marker-defined GSC populations compare with each other in terms of signaling and function and whether expression of different combinations of these markers is associated with different functional capacity. Using mass cytometry and fresh operating room specimens, we found 15 distinct GSC subpopulations in patients, and they differed in their MEK/ERK, WNT, and AKT pathway activation status. Once in culture, some subpopulations were lost and previously undetectable ones materialized. GSCs that highly expressed all 4 surface markers had the greatest self-renewal capacity, WNT inhibitor sensitivity, and in vivo tumorigenicity. This work highlights the potential signaling and phenotypic diversity of GSCs. Larger patient sample sizes and antibody panels are required to confirm these findings.

SUBMITTER: Galdieri L

PROVIDER: S-EPMC7934942 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry.

Galdieri Luciano L Jash Arijita A Malkova Olga O Mao Diane D DD DeSouza Patrick P Chu Yunli E YE Salter Amber A Campian Jian L JL Naegle Kristen M KM Brennan Cameron W CW Wakimoto Hiroaki H Oh Stephen T ST Kim Albert H AH Chheda Milan G MG

JCI insight 20210222 4

Most patients with glioblastoma (GBM) die within 2 years. A major therapeutic goal is to target GBM stem cells (GSCs), a subpopulation of cells that contribute to treatment resistance and recurrence. Since their discovery in 2003, GSCs have been isolated using single-surface markers, such as CD15, CD44, CD133, and α6 integrin. It remains unknown how these single-surface marker-defined GSC populations compare with each other in terms of signaling and function and whether expression of different c ...[more]

PMID: 33400685

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Project description:A remarkable feature of the self-renewing population of embryonic stem cells (ESCs) is their phenotypic heterogeneity: Nanog and other marker proteins of ESCs show large cell-to-cell variation in their expression level, which should significantly influence the differentiation process of individual cells. The molecular mechanism and biological implication of this heterogeneity, however, still remain elusive. We address this problem by constructing a model of the core gene-network of mouse ESCs. The model takes account of processes of binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and modification of histone code at each locus of genes in the network. These processes are hierarchically interrelated to each other forming the dynamical feedback loops. By simulating stochastic dynamics of this model, we show that the phenotypic heterogeneity of ESCs can be explained when the chromatin at the Nanog locus undergoes the large scale reorganization in formation/dissolution of transcription apparatus, which should have the timescale similar to the cell cycle period. With this slow transcriptional switching of Nanog, the simulated ESCs fluctuate among multiple transient states, which can trigger the differentiation into the lineage-specific cell states. From the simulated transitions among cell states, the epigenetic landscape underlying transitions is calculated. The slow Nanog switching gives rise to the wide basin of ESC states in the landscape. The bimodal Nanog distribution arising from the kinetic flow running through this ESC basin prevents transdifferentiation and promotes the definite decision of the cell fate. These results show that the distribution of timescales of the regulatory processes is decisively important to characterize the fluctuation of cells and their differentiation process. The analyses through the epigenetic landscape and the kinetic flow on the landscape should provide a guideline to engineer cell differentiation.

Dataset Information

Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry.

Publications

Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry.

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