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.

Single-cell RNA sequencing is a powerful technology for obtaining transcriptomes at single-cell resolutions. However, it suffers from dropout events (i.e., excess zero counts) since only a small fraction of transcripts get sequenced in each cell during the sequencing process. This inherent sparsity of expression profiles hinders further characterizations at cell/gene-level such as cell type identification and downstream analysis. To alleviate this dropout issue we introduce a network-based method, netImpute, by leveraging the hidden information in gene co-expression networks to recover real signals. netImpute employs Random Walk with Restart (RWR) to adjust the gene expression level in a given cell by borrowing information from its neighbors in a gene co-expression network. Performance evaluation and comparison with existing tools on simulated data and seven real datasets show that netImpute substantially enhances clustering accuracy and data visualization clarity, thanks to its effective treatment of dropouts. While the idea of netImpute is general and can be applied with other types of networks such as cell co-expression network or protein–protein interaction (PPI) network, evaluation results show that gene co-expression network is consistently more beneficial, presumably because PPI network usually lacks cell type context, while cell co-expression network can cause information loss for rare cell types. Evaluation results on several biological datasets show that netImpute can more effectively recover missing transcripts in scRNA-seq data and enhance the identification and visualization of heterogeneous cell types than existing methods.

Single-cell RNA-seq (scRNAseq) technologies are rapidly evolving and a growing number of datasets are now available. While very informative, in standard scRNAseq experiments the spatial organization of the cells in the organism or tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to data of genes with spatial information can thus increase coverage while providing spatial location. However, methods to perform such a mapping are still in their infancy and have not been benchmarked in an unbiased manner. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge to evaluate methods for reconstructing the spatial arrangement of single cells from single-cell RNA sequencing data. The challenge focused on the spatial reconstruction of cells from the Drosophila embryo from single-cell transcriptomic and, leveraging as gold standard, in situ hybridization data of a set of selected driver genes from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used an array of different algorithms for gene selection and location prediction. We devised a novel scoring and cross-validation scheme to evaluate the robustness of the best performing algorithms. Participants were able to correctly and robustly localize rare subpopulations of cells, accurately mapping both spatially co-localized and scattered groups of cells. The selection of predictor genes was essential for accurately locating the cells in the embryo. Among the most frequently selected set of genes we measured a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers. IntroductionThe recent technological advances in single-cell sequencing technologies have revolutionized 1 the biological sciences. In particular single-cell RNA sequencing (scRNAseq) methods allow 2 transcriptome profiling in a highly parallel manner, resulting in the quantification of thousands of 3 genes across thousands of cells of the same tissue. However, with a few exceptions [1, 2, 3, 4, 5] 4 current high-throughput scRNAseq methods share the drawback of losing the information relative 5 to the spatial arrangement of the cells in the tissue during the cell dissociation step. 6 One way of regaining spatial information computationally is to appropriately combine the single-7 cell RNA dataset at hand with a reference database, or atlas, containing spatial expression patterns 8 for several genes across the tissue. This approach was pursued in a few studies [6, 7, 8, 9, 10]. 9 Achim et al identified the location of 139 cells using 72 reference genes with spatial information 10 from whole mount in situ hybridization (WMISH) of a marine annelid and Satija et al developed 11 the Seurat algorithm to predict position of 851 zebrafish cells based on their scRNAseq data and 12 spatial information from in s...

DGK is a cofounder of Xrad Therapeutics, which is developing radiosensitizers, and he serves on the Scientific Advisory Board of Lumicell, which is commercializing intraoperative imaging technology.

Highlights d Inhibition of aKG-dependent demethylation destabilizes chromatin regulatory landscape d 2HG subverts lineage fidelity and increases cell-level variability in motif accessibility d 2HG-high breast tumors display enhanced cellular heterogeneity d A2P eradicates heterogeneity in high 2HG-producing basallike cancer cells