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03559nam a2200793Ia 4500 |
| 001 |
10.15252-msb.202010105 |
| 008 |
220427s2021 CNT 000 0 und d |
| 020 |
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|a 17444292 (ISSN)
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| 245 |
1 |
0 |
|a Modeling glioblastoma heterogeneity as a dynamic network of cell states
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| 260 |
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0 |
|b John Wiley and Sons Inc
|c 2021
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.15252/msb.202010105
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| 520 |
3 |
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|a Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition. © 2021 The Authors. Published under the terms of the CC BY 4.0 license
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| 650 |
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|a Article
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| 650 |
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4 |
|a astrocyte
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| 650 |
0 |
4 |
|a bone morphogenetic protein 4
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| 650 |
0 |
4 |
|a Brain Neoplasms
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| 650 |
0 |
4 |
|a brain tumor
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| 650 |
0 |
4 |
|a cancer cell
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| 650 |
0 |
4 |
|a CD24 antigen
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| 650 |
0 |
4 |
|a cell differentiation
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| 650 |
0 |
4 |
|a cell growth
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| 650 |
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4 |
|a cell heterogeneity
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| 650 |
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4 |
|a Cell Line, Tumor
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| 650 |
0 |
4 |
|a cell lineage
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| 650 |
0 |
4 |
|a cell proliferation
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| 650 |
0 |
4 |
|a cell state
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| 650 |
0 |
4 |
|a cellular barcoding
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| 650 |
0 |
4 |
|a controlled study
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| 650 |
0 |
4 |
|a cyclin dependent kinase inhibitor 1A
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| 650 |
0 |
4 |
|a flow cytometry
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| 650 |
0 |
4 |
|a fluorescence activated cell sorting
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| 650 |
0 |
4 |
|a gene set enrichment analysis
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| 650 |
0 |
4 |
|a genetics
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| 650 |
0 |
4 |
|a glioblastoma
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| 650 |
0 |
4 |
|a Glioblastoma
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| 650 |
0 |
4 |
|a Hermes antigen
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| 650 |
0 |
4 |
|a human
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| 650 |
0 |
4 |
|a human cell
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| 650 |
0 |
4 |
|a Humans
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| 650 |
0 |
4 |
|a Markov chain
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| 650 |
0 |
4 |
|a mathematical model
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| 650 |
0 |
4 |
|a Neoplasm Recurrence, Local
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| 650 |
0 |
4 |
|a neural stem cell
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| 650 |
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4 |
|a oligodendrocyte precursor cell
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| 650 |
0 |
4 |
|a patient-derived brain tumor cells
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| 650 |
0 |
4 |
|a RNA
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| 650 |
0 |
4 |
|a single cell analysis
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| 650 |
0 |
4 |
|a single cell RNA seq
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| 650 |
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4 |
|a Single-Cell Analysis
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| 650 |
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4 |
|a single-cell lineage tracing
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| 650 |
0 |
4 |
|a stochastic model
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| 650 |
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4 |
|a temozolomide
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| 650 |
0 |
4 |
|a time-dependent computational models
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| 650 |
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4 |
|a transcription factor Sox2
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| 650 |
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4 |
|a transcription factor Sox4
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| 650 |
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4 |
|a transcriptome
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| 650 |
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4 |
|a tumor cell line
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| 650 |
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4 |
|a tumor recurrence
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| 650 |
0 |
4 |
|a upregulation
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| 700 |
1 |
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|a Dalmo, E.
|e author
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| 700 |
1 |
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|a Doroszko, M.
|e author
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| 700 |
1 |
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|a Elgendy, R.
|e author
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| 700 |
1 |
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|a Jörnsten, R.
|e author
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| 700 |
1 |
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|a Larsson, I.
|e author
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| 700 |
1 |
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|a Nelander, S.
|e author
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| 700 |
1 |
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|a Niklasson, M.
|e author
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| 700 |
1 |
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|a Segerman, A.
|e author
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| 700 |
1 |
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|a Westermark, B.
|e author
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| 773 |
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|t Molecular Systems Biology
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