Lessons we Learned from High-Throughput and Top-Down Systems Biology Analyses about Glioma Stem Cells

2014 ◽  
Vol 20 (1) ◽  
pp. 66-72 ◽  
Andreas Mock ◽  
Sara Chiblak ◽  
Christel Herold-Mende
2018 ◽  
Vol 23 (8) ◽  
pp. 842-849 ◽  
Victor Quereda ◽  
Shurong Hou ◽  
Franck Madoux ◽  
Louis Scampavia ◽  
Timothy P. Spicer ◽  

Glioblastoma (GBM) is the most aggressive primary brain cancer with an average survival time after diagnosis of only 12–14 months, with few (<5%) long-term survivors. A growing body of work suggests that GBMs contain a small population of glioma stem cells (GSCs) that are thought to be major contributors to treatment resistance and disease relapse. Identifying compounds that modulate GSC proliferation would provide highly valuable molecular probes of GSC-directed signaling. However, targeting GSCs pharmacologically has been challenging. Patient-derived GSCs can be cultured as neurospheres, and in vivo these cells functionally recapitulate the heterogeneity of the original tumor. Using patient-derived GSC-enriched cultures, we have developed a 1536-well spheroid-based proliferation assay and completed a pilot screen, testing ~3300 compounds comprising approved drugs. This cytotoxic and automation-friendly assay yielded a signal-to-background (S/B) ratio of 161.3 ± 7.5 and Z′ of 0.77 ± 0.02, demonstrating its robustness. Importantly, compounds were identified with anti-GSC activity, demonstrating the applicability of this assay for large-scale high-throughput screening (HTS).

2015 ◽  
Vol 9 (2) ◽  
pp. 70-77
Suojun Zhang ◽  
Feng Wan ◽  
Lin Han ◽  
Fei Ye ◽  
Dongsheng Guo ◽  

Oncogene ◽  
2021 ◽  
Kazuya Fukasawa ◽  
Takuya Kadota ◽  
Tetsuhiro Horie ◽  
Kazuya Tokumura ◽  
Ryuichi Terada ◽  

2021 ◽  
Vol 12 (6) ◽  
Zetao Chen ◽  
Yihong Chen ◽  
Yan Li ◽  
Weidong Lian ◽  
Kehong Zheng ◽  

AbstractGlioma is one of the most lethal cancers with highly vascularized networks and growing evidences have identified glioma stem cells (GSCs) to account for excessive angiogenesis in glioma. Aberrant expression of paired-related homeobox1 (Prrx1) has been functionally associated with cancer stem cells including GSCs. In this study, Prrx1 was found to be markedly upregulated in glioma specimens and elevated Prrx1 expression was inversely correlated with prognosis of glioma patients. Prrx1 potentiated stemness acquisition in non-stem tumor cells (NSTCs) and stemness maintenance in GSCs, accompanied with increased expression of stemness markers such as SOX2. Prrx1 also promoted glioma angiogenesis by upregulating proangiogenic factors such as VEGF. Consistently, silencing Prrx1 markedly inhibited glioma proliferation, stemness, and angiogenesis in vivo. Using a combination of subcellular proteomics and in vitro analyses, we revealed that Prrx1 directly bound to the promoter regions of TGF-β1 gene, upregulated TGF-β1 expression, and ultimately activated the TGF-β/smad pathway. Silencing TGF-β1 mitigated the malignant behaviors induced by Prrx1. Activation of this pathway cooperates with Prrx1 to upregulate the expression of stemness-related genes and proangiogenic factors. In summary, our findings revealed that Prrx1/TGF-β/smad signal axis exerted a critical role in glioma stemness and angiogeneis. Disrupting the function of this signal axis might represent a new therapeutic strategy in glioma patients.

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 575
Jelena Ochs ◽  
Ferdinand Biermann ◽  
Tobias Piotrowski ◽  
Frederik Erkens ◽  
Bastian Nießing ◽  

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

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