Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

中文翻译：

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基因选择，用于根据单细胞转录组学数据最佳预测组织中的细胞位置。

单细胞RNA测序（scRNAseq）技术正在迅速发展。尽管非常有用，但在标准scRNAseq实验中，原始组织中细胞的空间组织丢失了。相反，旨在维持细胞定位的空间RNA测序技术的产量和基因覆盖率有限。将scRNAseq映射到具有空间信息的基因可增加覆盖范围，同时提供空间位置。但是，执行这种映射的方法尚未进行基准测试。为了填补这一空白，我们组织了DREAM单细胞转录组学挑战赛，重点是根据scRNAseq数据，利用银标准的基因和来自Berkeley Drosophila的原位杂交数据，对果蝇胚胎的细胞进行空间重建。转录网络项目参考图集。34个参赛团队使用多种算法进行基因选择和位置预测，同时能够正确定位细胞簇。选择预测基因对于该任务至关重要。预测基因显示出相对较高的表达熵，较高的空间聚类性，并包括突出的发育基因，例如缺口和成对规则基因以及组织标志物。与果蝇数据相比，将前10种方法应用于斑马鱼胚胎数据集可获得与所选基因相似的性能和统计特性。这表明在这一挑战中开发的方法能够提取可广泛用于精确重建组织中细胞空间排列的基因的通用属性。