to clarify that statements made comparing this and previous studies relate to the published analyses of data within those studies and not the detection of genes within the datasets themselves. The changes made are shown below and both the online full-text and PDF versions have been updated. In the Introduction, the following statement was changed: Original However, existing datasets have apparently not provided the transcriptional depth to identify the signalling pathways that operate within the human fetal kidney, and fail to detect several known ligand and receptor expression patterns in mouse. Corrected The analyses performed on existing datasets have not comprehensively identified the signalling pathway components known to be operating within the mouse fetal kidney. In the Discussion, the following statements were changed: Original For example, although >20,000 cells were profiled at P1 (Adam et al., 2017), several known signalling molecules with functionally validated roles in the nephrogenic niche such as Gdnf, Fgf20, Fgf9, Bmp7, Wnt4 and Fgf8 were not detected in that analysis, precluding further insight into signalling interactions. Corrected Although >20,000 cells were profiled at P1 (Adam et al., 2017), several known signalling molecules with functionally validated roles in the nephrogenic niche were not highlighted in that analysis. Original The improved resolution of gene expression in our study may be due to sequencing depth (∼3000 genes detected per cell), biological replication and differential expression analysis with the edgeR method, which has recently been shown to be a top performer in a comparison of 36 differential expression analysis methods for scRNA-seq data (Soneson and Robinson, 2018). Corrected The improved analysis of signalling interactions provided in this study may be due to sequencing depth (∼3000 genes detected per cell), biological replication and differential expression analysis with the edgeR method, which has recently been shown to be a top performer in a comparison of 36 differential expression analysis methods for scRNA-seq data (Soneson and Robinson, 2018).