Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium; Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Ghent University, Ghent, Belgium.
Cell Rep. 2024 Oct 22;43(10):114804. doi: 10.1016/j.celrep.2024.114804. Epub 2024 Oct 3.
Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.
神经母细胞瘤是一种罕见的胚胎肿瘤，起源于神经嵴发育，占儿童癌症相关死亡人数的 15%。最近，对神经母细胞瘤患者样本进行了几项单细胞转录组研究，以研究起源细胞和肿瘤异质性。然而，这些单独的研究涉及少量的肿瘤和细胞，限制了可以得出的结论。为了克服这一限制，我们将七个单细胞或单核数据集整合到一个协调的细胞图谱中，涵盖了 61 名患者的 362991 个细胞。我们使用这个图谱来解析神经母细胞瘤的单细胞分辨率转录景观，揭示肿瘤内部转录组谱与临床结果之间的关联。此外，我们还描述了复杂的免疫细胞景观，并揭示了肿瘤相关巨噬细胞之间存在相当大的异质性。最后，我们通过扩展额外的数据并将其用作数据驱动的细胞类型注释的参考，展示了我们的图谱作为资源的实用性。
