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GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.GSEApy:一个用于在 Python 中进行基因集富集分析的综合软件包。
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Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.基于约束的代谢模型重建与分析:用于癌症研究的开源Python工具及应用
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Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.血浆和单细胞的综合分析揭示了 COVID-19 患者的代谢变化。
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Mechanisms of EGFR Resistance in Glioblastoma.胶质母细胞瘤中 EGFR 耐药的机制。
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Mislocalisation of Activated Receptor Tyrosine Kinases - Challenges for Cancer Therapy.激活的受体酪氨酸激酶的定位错误——癌症治疗的挑战。
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Glioblastoma Stem Cells: Driving Resilience through Chaos.胶质母细胞瘤干细胞:通过混沌驱动韧性。
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An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.胶质母细胞瘤的细胞状态、可塑性和遗传学综合模型
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单细胞中葡萄糖摄取的动力学轨迹揭示了药物处理数小时内药物诱导的细胞状态变化。

Kinetic Trajectories of Glucose Uptake in Single Cancer Cells Reveal a Drug-Induced Cell-State Change Within Hours of Drug Treatment.

作者信息

Kim Jungwoo, Ng Rachel H, Liang JingXin, Johnson Dazy, Shin Young Shik, Chatziioannou Arion F, Phelps Michael E, Wei Wei, Levine Raphael D, Heath James R

机构信息

Innovation Center for R&D Regulation and Management, Korea Institute of Science & Technology Evaluation and Planning, Eumseong-gun, Chungcheongbuk-do 27740, Korea.

Department of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

出版信息

J Phys Chem B. 2024 Aug 22;128(33):7978-7986. doi: 10.1021/acs.jpcb.4c03663. Epub 2024 Aug 8.

DOI:10.1021/acs.jpcb.4c03663
PMID:39115241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12327372/
Abstract

The development of drug resistance is a nearly universal phenomenon in patients with glioblastoma multiforme (GBM) brain tumors. Upon treatment, GBM cancer cells may initially undergo a drug-induced cell-state change to a drug-tolerant, slow-cycling state. The kinetics of that process are not well understood, in part due to the heterogeneity of GBM tumors and tumor models, which can confound the interpretation of kinetic data. Here, we resolve drug-adaptation kinetics in a patient-derived in vitro GBM tumor model characterized by the epithelial growth factor receptor (EGFR) variant(v)III oncogene treated with an EGFR inhibitor. We use radiolabeled F-fluorodeoxyglucose (FDG) to monitor the glucose uptake trajectories of single GBM cancer cells over a 12 h period of drug treatment. Autocorrelation analysis of the single-cell glucose uptake trajectories reveals evidence of a drug-induced cell-state change from a high- to low-glycolytic phenotype after 5-7 h of drug treatment. Information theoretic analysis of a bulk transcriptome kinetic series of the GBM tumor model delineated the underlying molecular mechanisms driving the cellular state change, including a shift from a stem-like mesenchymal state to a more differentiated, slow-cycling astrocyte-like state. Our results demonstrate that complex drug-induced cancer cell-state changes of cancer cells can be captured via measurements of single cell metabolic trajectories and reveal the extremely facile nature of drug adaptation.

摘要

耐药性的产生在多形性胶质母细胞瘤(GBM)脑肿瘤患者中几乎是一种普遍现象。在治疗过程中,GBM癌细胞最初可能会经历药物诱导的细胞状态变化,转变为耐药的慢循环状态。该过程的动力学尚未得到很好的理解,部分原因是GBM肿瘤和肿瘤模型的异质性,这可能会混淆动力学数据的解释。在这里,我们在一个患者来源的体外GBM肿瘤模型中解析了药物适应动力学,该模型以用表皮生长因子受体(EGFR)变体(v)III癌基因处理并使用EGFR抑制剂为特征。我们使用放射性标记的F-氟脱氧葡萄糖(FDG)来监测单个GBM癌细胞在12小时药物治疗期间的葡萄糖摄取轨迹。对单细胞葡萄糖摄取轨迹的自相关分析揭示了在药物治疗5-7小时后,药物诱导的细胞状态从高糖酵解表型向低糖酵解表型转变的证据。对GBM肿瘤模型的大量转录组动力学系列进行信息论分析,描绘了驱动细胞状态变化的潜在分子机制,包括从干细胞样间充质状态向更分化的、慢循环的星形胶质细胞样状态的转变。我们的结果表明,复杂的药物诱导的癌细胞状态变化可以通过测量单细胞代谢轨迹来捕获,并揭示了药物适应的极其容易的性质。