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2021 (English)In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 17, no 9, article id e10105Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
John Wiley & SonsWILEY, 2021
Keywords
cell state, cellular barcoding, patient-derived brain tumor cells, single-cell lineage tracing, time-dependent computational models
National Category
Cell Biology Cell and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-466275 (URN)10.15252/msb.202010105 (DOI)000700839700005 ()34528760 (PubMedID)
Funder
Swedish Cancer SocietySwedish Research CouncilSwedish Foundation for Strategic Research
2022-01-262022-01-262024-01-15Bibliographically approved