Background

High throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. However, the efficient generation of such atlases will depend on sufficient sampling of diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals.

Results

To examine the relationship between sampled cell numbers and transcriptional heterogeneity in the context of unbiased cell type classification, we explored the population structure of a publicly available 1.3 million cell dataset from E18.5 mouse brain and validated our findings in published data from adult mice. We propose a computational framework for inferring the saturation point of cluster discovery in a single-cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a "complexity index," which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether the detected biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells than the originally sampled, though technical saturation of rare populations such as Cajal-Retzius cells is not achieved. We additionally validated these findings with a recently published atlas of cell types across mouse organs and again find using subsampling that a much smaller number of cells recapitulates the cluster distinctions of the complete dataset.

Conclusions

Together, these findings suggest that most of the biologically interpretable cell types from the 1.3 million cell database can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high "cellular coverage," cell atlas studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage and then further enriching for populations of interest. This strategy is ideal for scenarios where cost and time are limited, though extremely rare populations of interest (< 1%) may be identifiable only with much higher cell numbers.

The cerebral cortex is the hallmark of the mammalian nervous system, and its large size and cellular diversity in humans support our most sophisticated cognitive abilities. Although the basic cellular organization of the cortex is conserved across mammals, cells have diversified during evolution. An increasingly integrated taxonomy of cell types, especially with the advent of single-cell transcriptomic data, has revealed an unprecedented variety of human cortical cell subtypes. Here, we broadly review the cellular composition and diversity of the mammalian brain, and how progenitor pools generate cell subtypes during development. We then discuss human cortical cells that are distinct from rodent cells, as well as the challenges and advantages of using model systems to study human cell types in health and disease.

Studies of the spatiotemporal, transcriptomic, and morphological diversity of radial glia (RG) have spurred our current models of human corticogenesis. In the developing cortex, neural intermediate progenitor cells (nIPCs) are a neuron-producing transit-amplifying cell type born in the germinal zones of the cortex from RG. The potential diversity of the nIPC population, that produces a significant portion of excitatory cortical neurons, is understudied, particularly in the developing human brain. Here we explore the spatiotemporal, transcriptomic, and morphological variation that exists within the human nIPC population and provide a resource for future studies. We observe that the spatial distribution of nIPCs in the cortex changes abruptly around gestational week (GW) 19/20, marking a distinct shift in cellular distribution and organization during late neurogenesis. We also identify five transcriptomic subtypes, one of which appears at this spatiotemporal transition. Finally, we observe a diversity of nIPC morphologies that do not correlate with specific transcriptomic subtypes. These results provide an analysis of the spatiotemporal, transcriptional, and morphological diversity of nIPCs in developing brain tissue and provide an atlas of nIPC subtypes in the developing human cortex that can benchmark in vitro models of human development such as cerebral organoids and help inform future studies of how nIPCs contribute to cortical neurogenesis.

Innovations in organoid-based models of human tissues have made them an exciting experimental platform for studying development and disease. However, these models require systematic benchmarking against primary tissue to establish their value. We discuss key parameters that impact the utility of organoid models, primarily focusing on cerebral organoids as examples.

Adult cortical areas consist of specialized cell types and circuits that support unique higher-order cognitive functions. How this regional diversity develops from an initially uniform neuroepithelium has been the subject of decades of seminal research, and emerging technologies, including single-cell transcriptomics, provide a new perspective on area-specific molecular diversity. Here, we review the early developmental processes that underlie cortical arealization, including both cortex intrinsic and extrinsic mechanisms as embodied by the protomap and protocortex hypotheses, respectively. We propose an integrated model of serial homology whereby intrinsic genetic programs and local factors establish early transcriptomic differences between excitatory neurons destined to give rise to broad "proto-regions," and activity-dependent mechanisms lead to progressive refinement and formation of sharp boundaries between functional areas. Finally, we explore the potential of these basic developmental processes to inform our understanding of the emergence of functional neural networks and circuit abnormalities in neurodevelopmental disorders.

The human cortex comprises diverse cell types that emerge from an initially uniform neuroepithelium that gives rise to radial glia, the neural stem cells of the cortex. To characterize the earliest stages of human brain development, we performed single-cell RNA-sequencing across regions of the developing human brain, including the telencephalon, diencephalon, midbrain, hindbrain and cerebellum. We identify nine progenitor populations physically proximal to the telencephalon, suggesting more heterogeneity than previously described, including a highly prevalent mesenchymal-like population that disappears once neurogenesis begins. Comparison of human and mouse progenitor populations at corresponding stages identifies two progenitor clusters that are enriched in the early stages of human cortical development. We also find that organoid systems display low fidelity to neuroepithelial and early radial glia cell types, but improve as neurogenesis progresses. Overall, we provide a comprehensive molecular and spatial atlas of early stages of human brain and cortical development.

The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.

The human brain is subdivided into distinct anatomical structures, including the neocortex, which in turn encompasses dozens of distinct specialized cortical areas. Early morphogenetic gradients are known to establish early brain regions and cortical areas, but how early patterns result in finer and more discrete spatial differences remains poorly understood¹. Here we use single-cell RNA sequencing to profile ten major brain structures and six neocortical areas during peak neurogenesis and early gliogenesis. Within the neocortex, we find that early in the second trimester, a large number of genes are differentially expressed across distinct cortical areas in all cell types, including radial glia, the neural progenitors of the cortex. However, the abundance of areal transcriptomic signatures increases as radial glia differentiate into intermediate progenitor cells and ultimately give rise to excitatory neurons. Using an automated, multiplexed single-molecule fluorescent in situ hybridization approach, we find that laminar gene-expression patterns are highly dynamic across cortical regions. Together, our data suggest that early cortical areal patterning is defined by strong, mutually exclusive frontal and occipital gene-expression signatures, with resulting gradients giving rise to the specification of areas between these two poles throughout successive developmental timepoints.

Despite the clinical and genetic heterogeneity of autism, bulk gene expression studies show that changes in the neocortex of autism patients converge on common genes and pathways. However, direct assessment of specific cell types in the brain affected by autism has not been feasible until recently. We used single-nucleus RNA sequencing of cortical tissue from patients with autism to identify autism-associated transcriptomic changes in specific cell types. We found that synaptic signaling of upper-layer excitatory neurons and the molecular state of microglia are preferentially affected in autism. Moreover, our results show that dysregulation of specific groups of genes in cortico-cortical projection neurons correlates with clinical severity of autism. These findings suggest that molecular changes in upper-layer cortical circuits are linked to behavioral manifestations of autism.

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.