CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
Nat Biotechnol. 2020 Jun;38(6):747-755. doi: 10.1038/s41587-020-0469-4. Epub 2020 Apr 6.
Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.
单细胞 RNA 测序（scRNA-seq）是一种用于描述样本中单细胞转录组的主要技术。最新的方案可扩展到数千个细胞，并被用于构建组织、器官和生物体的细胞图谱。然而，这些方案在 RNA 捕获效率、偏差、规模和成本方面存在很大差异，它们在不同应用中的相对优势尚不清楚。在本研究中，我们生成了基准数据集，以系统地评估这些方案在全面描述细胞类型和状态方面的能力。我们进行了一项多中心研究，比较了 13 种常用的 scRNA-seq 和单核 RNA-seq 方案在一个异质参考样本资源上的应用。比较分析显示，方案的性能存在显著差异。这些方案在文库复杂性和检测细胞类型标记物的能力方面存在差异，这影响了它们的预测价值和适合整合到参考细胞图谱中的能力。这些结果为单个研究人员和人类细胞图谱等联盟项目提供了指导。
