The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from post-mortem brain, generating 3,227 sets of single neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish novel and orthologous neuronal subtypes as well as regional identity within the human brain.

Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, and computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus Droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive-site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from the human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for studying complex processes in the brain, such as genetic programs coordinating adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, providing insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

Single-cell RNA sequencing can reveal the transcriptional state of cells, yet provides little insight into the upstream regulatory landscape associated with open or accessible chromatin regions. Joint profiling of accessible chromatin and RNA within the same cells would permit direct matching of transcriptional regulation to its outputs. Here, we describe droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq), a method that can link a cell's transcriptome with its accessible chromatin for sequencing at scale. Specifically, accessible sites are captured by Tn5 transposase in permeabilized nuclei to permit, within many droplets in parallel, DNA barcode tagging together with the mRNA molecules from the same cells. To demonstrate the utility of SNARE-seq, we generated joint profiles of 5,081 and 10,309 cells from neonatal and adult mouse cerebral cortices. We reconstructed the transcriptome and epigenetic landscapes of major and rate cell types, uncovered lineage-specific accessible sites especially for low-abundance cells, and connected the dynamics of promoter accessibility with transcription level during neurogenesis.RNA sequencing of single cells or nuclei reveals their transcription state, whereas chromatin accessibility sequencing uncovers the associated regulatory landscape. Current strategies 1,2 , which involve profiling these modalities separately followed by computational integration, may not fully recapitulate the true biological state. Joint profiling of two layers of -omics information within the same cells would enable a direct matching of transcriptional regulation to its output, allowing for more accurate reconstruction of the molecular processes underlying a cell's physiology.

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.

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.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Significant heterogeneities in gene expression among individual cells are typically interrogated using single whole cell approaches. However, tissues that have highly interconnected processes, such as in the brain, present unique challenges. Single-nucleus RNA sequencing (SNS) has emerged as an alternative method of assessing a cell’s transcriptome through the use of isolated nuclei. However, studies directly comparing expression data between nuclei and whole cells are lacking. Here, we have characterized nuclear and whole cell transcriptomes in mouse single neurons and provided a normalization strategy to reduce method-specific differences related to the length of genic regions. We confirmed a high concordance between nuclear and whole cell transcriptomes in the expression of cell type and metabolic modeling markers, but less so for a subset of genes associated with mitochondrial respiration. Therefore, our results indicate that single-nucleus transcriptome sequencing provides an effective means to profile cell type expression dynamics in previously inaccessible tissues.

Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of ~1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.