The origins and functions of kidney macrophages in the adult have been explored, but their roles during development remain largely unknown. Here we characterise macrophage arrival, localisation, heterogeneity, and functions during kidney organogenesis. Using genetic approaches to ablate macrophages, we identify a role for macrophages in nephron progenitor cell clearance as mouse kidney development begins. Throughout renal organogenesis, most kidney macrophages are perivascular and express F4/80 and CD206. These macrophages are enriched for mRNAs linked to developmental processes, such as blood vessel morphogenesis. Using antibody-mediated macrophage-depletion, we show macrophages support vascular anastomoses in cultured kidney explants. We also characterise a subpopulation of galectin-3⁺ (Gal3⁺) myeloid cells within the developing kidney. Our findings may stimulate research into macrophage-based therapies for renal developmental abnormalities and have implications for the generation of bioengineered kidney tissues.

GATA1 has a foundational role in erythropoiesis. The investigators compare the function of 2 forms (the full-length protein and a shorter form) of the transcription factor GATA1 and show that the N-terminal domain of GATA1 is critical to red cell differentiation.

Significance Statement: Kidney development is a complex process involving multiple interacting and transitioning cell types. Drop-seq single-cell technology, which measures gene expression from many thousands of individual cells, has been used to characterize these cellular differentiation changes that underlie organ development. However, the alternative splicing of many genes creates an additional layer of cellular heterogeneity that Drop-seq technology cannot measure. Therefore, in this study, full transcript length single-cell RNA sequencing was used to characterize alternative splicing in the mouse embryonic kidney, with particular attention to the identification of genes that are alternatively spliced during the transition from mesenchymal to epithelial cell states, as well as their splicing regulators. These results improve our understanding of the molecular mechanisms that underlie kidney development. Background: During mammalian kidney development, nephron progenitors undergo a mesenchymal-to-epithelial transition and eventually differentiate into the various tubular segments of the nephron. Recently, Drop-seq single-cell RNA sequencing technology for measuring gene expression from thousands of individual cells identified the different cell types in the developing kidney. However, that analysis did not include the additional layer of heterogeneity that alternative mRNA splicing creates. Methods: Full transcript length single-cell RNA sequencing characterized the transcriptomes of 544 individual cells from mouse embryonic kidneys. Results: Gene expression levels measured with full transcript length single-cell RNA sequencing identified each cell type. Further analysis comprehensively characterized splice isoform switching during the transition between mesenchymal and epithelial cellular states, which is a key transitional process in kidney development. The study also identified several putative splicing regulators, including the genes Esrp1/2 and Rbfox1/2. Conclusions: Discovery of the sets of genes that are alternatively spliced as the fetal kidney mesenchyme differentiates into tubular epithelium will improve our understanding of the molecular mechanisms that drive kidney development.

Wilms' tumor is a pediatric malignancy that is thought to originate from faulty kidney development during the embryonic stage. However, there is a large variation between tumors from different patients in both histology and gene expression that is not well characterized. Here we use a meta-analysis of published microarray datasets to show that Favorable Histology Wilms' Tumors (FHWT's) fill a triangle-shaped continuum in gene expression space of which the vertices represent three idealized "archetypes". We show that these archetypes have predominantly renal blastemal, stromal, and epithelial characteristics and that they correlate well with the three major lineages of the developing embryonic kidney. Moreover, we show that advanced stage tumors shift towards the renal blastemal archetype. These results illustrate the potential of this methodology for characterizing the cellular composition of Wilms' tumors and for assessing disease progression.

A major aim of single cell biology is to identify important cell types such as stem cells in heterogeneous tissues and tumors. This is typically done by isolating hundreds of individual cells and measuring expression levels of multiple genes simultaneously from each cell. Then, clustering algorithms are used to group together similar single-cell expression profiles into clusters, each representing a distinct cell type. However, many of these clusters result from overfitting, meaning that rather than representing biologically meaningful cell types, they describe the intrinsic ‘noise’ in gene expression levels due to limitations in experimental precision or the intrinsic randomness of biochemical cellular processes. Consequentially, these non-meaningful clusters are most sensitive to noise: a slight shift in gene expression levels due to a repeated measurement will rearrange the grouping of data points such that these clusters break up. To identify the biologically meaningful clusters we propose a ‘cluster robustness score’: We add increasing amounts of noise (zero mean and increasing variance) and check which clusters are most robust in the sense that they do not mix with their neighbors up to high levels of noise. We show that biologically meaningful cell clusters that were manually identified in previously published single cell expression datasets have high robustness scores. These scores are higher than what would be expected in corresponding randomized homogeneous datasets having the same expression level statistics. We believe that this scoring system provides a more automated way to identify cell types in heterogeneous tissues and tumors. Supplementary data are available at Bioinformatics online.