Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics.
Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics.
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DOI：
10.1093/neuonc/noaa091
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发表时间：
2020-08-17
期刊：
影响因子：
15.9
通讯作者：
Conrad C
中科院分区：
文献类型：
--
作者：
Krieger TG;Tirier SM;Park J;Jechow K;Eisemann T;Peterziel H;Angel P;Eils R;Conrad C
胶质母细胞瘤（GBM）由具有高侵袭能力的破坏性肿瘤组成，很难在人源模型系统中进行体外研究。治疗进展还受到肿瘤内部和肿瘤之间细胞异质性以及治疗耐药性等其他因素的限制。为了应对这些挑战，我们提出了一个实验模型，使用人脑类器官作为患者来源的 GBM 细胞侵袭的支架。本研究将组织透明化、共聚焦显微镜与 GBM 细胞与类器官细胞共培养前后的单细胞 RNA 测序相结合。我们发现类器官内的肿瘤细胞延伸出一个长微管网络，重现了 GBM 的体内行为。侵袭过程中涉及的转录变化在患者样本中是一致的，表明 GBM 细胞反应性上调其分散所需的基因。通过计算机受体-配体配对筛选确定的 GBM 和类器官细胞之间的潜在相互作用表明了功能性治疗靶点。综上所述，我们的模型已被证明可用于体外研究 GBM 侵袭和转录异质性，并可在适合临床实践的时间尺度上应用于药理学筛选和患者特异性治疗选择。
Glioblastoma (GBM) consists of devastating neoplasms with high invasive capacity, which have been difficult to study in vitro in a human-derived model system. Therapeutic progress is also limited by cellular heterogeneity within and between tumors, among other factors such as therapy resistance. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived GBM cell invasion. This study combined tissue clearing and confocal microscopy with single-cell RNA sequencing of GBM cells before and after co-culture with organoid cells. We show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Transcriptional changes implicated in the invasion process are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Potential interactions between GBM and organoid cells identified by an in silico receptor–ligand pairing screen suggest functional therapeutic targets. Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection on a time scale amenable to clinical practice.
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影响因子：
82.9
作者：
Klughammer J;Kiesel B;Roetzer T;Fortelny N;Nemc A;Nenning KH;Furtner J;Sheffield NC;Datlinger P;Peter N;Nowosielski M;Augustin M;Mischkulnig M;Ströbel T;Alpar D;Ergüner B;Senekowitsch M;Moser P;Freyschlag CF;Kerschbaumer J;Thomé C;Grams AE;Stockhammer G;Kitzwoegerer M;Oberndorfer S;Marhold F;Weis S;Trenkler J;Buchroithner J;Pichler J;Haybaeck J;Krassnig S;Mahdy Ali K;von Campe G;Payer F;Sherif C;Preiser J;Hauser T;Winkler PA;Kleindienst W;Würtz F;Brandner-Kokalj T;Stultschnig M;Schweiger S;Dieckmann K;Preusser M;Langs G;Baumann B;Knosp E;Widhalm G;Marosi C;Hainfellner JA;Woehrer A;Bock C
通讯作者：
Bock C
影响因子：
48
作者：
Bian S;Repic M;Guo Z;Kavirayani A;Burkard T;Bagley JA;Krauditsch C;Knoblich JA
通讯作者：
Knoblich JA
影响因子：
8.8
作者：
Ogawa J;Pao GM;Shokhirev MN;Verma IM
通讯作者：
Verma IM
影响因子：
4.7
作者：
Cheng W;Tseng CJ;Lin TT;Cheng I;Pan HW;Hsu HC;Lee YM
通讯作者：
Lee YM
影响因子：
23.9
作者：
Lathia JD;Gallagher J;Heddleston JM;Wang J;Eyler CE;Macswords J;Wu Q;Vasanji A;McLendon RE;Hjelmeland AB;Rich JN
通讯作者：
Rich JN