The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Metabolism is vital to every aspect of cell function, yet the metabolome of iPSCs remains largely unexplored. Here we report, using an untargeted metabolomics approach, that human iPSCs share a pluripotent metabolomic signature with ESCs that is distinct from their parental cells, and that is characterized by changes in metabolites involved in cellular respiration. Examination of cellular bioenergetics corroborated with our metabolomic analysis, and demonstrated that somatic cells convert from an oxidative state to a glycolytic state in pluripotency. Interestingly, the bioenergetics of various somatic cells correlated with their reprogramming efficiencies. We further identified metabolites that differ between iPSCs and ESCs, which revealed novel metabolic pathways that play a critical role in regulating somatic cell reprogramming. Our findings are the first to globally analyze the metabolome of iPSCs, and provide mechanistic insight into a new layer of regulation involved in inducing pluripotency, and in evaluating iPSC and ESC equivalence.

Adjacent CpG sites in mammalian genomes can be co-methylated due to the processivity of methyltransferases or demethylases. Yet discordant methylation patterns have also been observed, and found related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that exhibit highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks (MHBs) with 61 sets of whole genome bisulfite sequencing (WGBS) data, and further validated with 101 sets of reduced representation bisulfite sequencing (RRBS) data and 637 sets of methylation array data. Using a metric called methylation haplotype load (MHL), we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 cancer patients using methylation haplotypes.

Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples.