The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

A Single-Cell RNA Sequencing Profiles the Developmental Landscape of Arabidopsis Root

Cells of eukaryotic multicellular organisms have inherent heterogeneity. Recent advances in single-cell gene expression studies enable us to explore transcriptional regulation in dynamic development processes and highly heterogeneous cell populations. In this study, using a high-throughput single-cell RNA-sequencing assay, we found that the cells in Arabidopsis root are highly heterogeneous in their transcriptomes. A total of 24 putative cell clusters and the cluster-specific marker genes were identified. The spatial distribution and temporal ordering of the individual cells at different developmental stages illustrate their hierarchical structures and enable the reconstruction of continuous differentiation trajectory of root development. Moreover, we found that each root cell cluster exhibits distinct patterns of ion assimilation and hormonal responses. Collectively, our study reveals a high degree of heterogeneity of root cells and identifies the expression signatures of intermediate states during root cell differentiation at single-cell resolution. We also established a web server (http://wanglab.sippe.ac.cn/rootatlas/) to facilitate the use of the datasets generated in this study.

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

A single-cell analysis of the Arabidopsis vegetative shoot apex

The shoot apical meristem allows for reiterative formation of new aerial structures throughout the life cycle of a plant. We use single-cell RNA sequencing to define the cellular taxonomy of the Arabidopsis vegetative shoot apex at the transcriptome level. We find that the shoot apex is composed of highly heterogeneous cells, which can be partitioned into 7 broad populations with 23 transcriptionally distinct cell clusters. We delineate cell-cycle continuums and developmental trajectories of epidermal cells, vascular tissue, and leaf mesophyll cells and infer transcription factors and gene expression signatures associated with cell fate decisions. Integrative analysis of shoot and root apical cell populations further reveals common and distinct features of epidermal and vascular tissues. Our results, thus, offer a valuable resource for investigating the basic principles underlying cell division and differentiation in plants at single-cell resolution.

An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB⁺CD27⁻ early memory population, a class-switched CD39⁺ tonsil-resident population, and a CD19^hiCD11c⁺ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

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A mass cytometry screen reveals 98 surface molecules expressed by human B cells

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High-dimensional analysis identifies twelve B cell subsets across four tissues

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CD45RB, CD11c, CD39, CD73, and CD95 define subsets of antigen-experienced B cells

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Isotype usage, signaling, and metabolism vary in accordance with cell surface phenotype

A Single-Cell Transcriptomic Atlas of Thymus Organogenesis Resolves Cell Types and Developmental Maturation

Thymus development is critical to the adaptive immune system, yet a comprehensive transcriptional framework capturing thymus organogenesis at single-cell resolution is still needed. We applied single-cell RNA sequencing (RNA-seq) to capture 8 days of thymus development, perturbations of T cell receptor rearrangement, and in vitro organ cultures, producing profiles of 24,279 cells. We resolved transcriptional heterogeneity of developing lymphocytes, and genetic perturbation confirmed T cell identity of conventional and non-conventional lymphocytes. We characterized maturation dynamics of thymic epithelial cells in vivo, classified cell maturation state in a thymic organ culture, and revealed the intrinsic capacity of thymic epithelium to preserve transcriptional regularity despite exposure to exogenous retinoic acid. Finally, by integrating the cell atlas with human genome-wide association study (GWAS) data and autoimmune-disease-related genes, we implicated embryonic thymus-resident cells as possible participants in autoimmune disease etiologies. This resource provides a single-cell transcriptional framework for biological discovery and molecular analysis of thymus organogenesis.

Advances and Opportunities in Single-Cell Transcriptomics for Plant Research

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

Single-Cell Transcriptomic Analyses Define Distinct Peripheral B Cell Subsets and Discrete Development Pathways

Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the “Double negative” (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.

A cell atlas of the chick retina based on single-cell transcriptomics

Retinal structure and function have been studied in many vertebrate orders, but molecular characterization has been largely confined to mammals. We used single-cell RNA sequencing (scRNA-seq) to generate a cell atlas of the chick retina. We identified 136 cell types plus 14 positional or developmental intermediates distributed among the six classes conserved across vertebrates - photoreceptor, horizontal, bipolar, amacrine, retinal ganglion, and glial cells. To assess morphology of molecularly defined types, we adapted a method for CRISPR-based integration of reporters into selectively expressed genes. For Müller glia, we found that transcriptionally distinct cells were regionally localized along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. We also identified immature photoreceptor, horizontal cell, and oligodendrocyte types that persist into late embryonic stages. Finally, we analyzed relationships among chick, mouse, and primate retinal cell classes and types. Our results provide a foundation for anatomical, physiological, evolutionary, and developmental studies of the avian visual system.

Tracing cell-type evolution by cross-species comparison of cell atlases

Cell types are the basic building units of multicellular life, with extensive diversities. The evolution of cell types is a crucial layer of comparative cell biology but is thus far not comprehensively studied. We define a compendium of cell atlases using single-cell RNA-seq (scRNA-seq) data from seven animal species and construct a cross-species cell-type evolutionary hierarchy. We present a roadmap for the origin and diversity of major cell categories and find that muscle and neuron cells are conserved cell types. Furthermore, we identify a cross-species transcription factor (TF) repertoire that specifies major cell categories. Overall, our study reveals conservation and divergence of cell types during animal evolution, which will further expand the landscape of comparative genomics.

Molecular classification of zebrafish retinal ganglion cells links genes to cell types to behavior

Retinal ganglion cells (RGCs) form an array of feature detectors, which convey visual information to central brain regions. Characterizing RGC diversity is required to understand the logic of the underlying functional segregation. Using single-cell transcriptomics, we systematically classified RGCs in adult and larval zebrafish, thereby identifying marker genes for >30 mature types and several developmental intermediates. We used this dataset to engineer transgenic driver lines, enabling specific experimental access to a subset of RGC types. Expression of one or few transcription factors often predicts dendrite morphologies and axonal projections to specific tectal layers and extratectal targets. In vivo calcium imaging revealed that molecularly defined RGCs exhibit specific functional tuning. Finally, chemogenetic ablation of eomesa+ RGCs, which comprise melanopsin-expressing types with projections to a small subset of central targets, selectively impaired phototaxis. Together, our study establishes a framework for systematically studying the functional architecture of the visual system.

GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping" Genes and Diverse Human Transporter Expression Profiles

The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.

Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We characterized 163 distinct cell types and performed an in-depth analysis of changes in tissue cell composition, gene expression, and cell identities. We further developed aging clock models to predict fly age and show that ribosomal gene expression is a conserved predictive factor for age. Combining all aging features, we find distinctive cell type-specific aging patterns. This atlas provides a valuable resource for studying fundamental principles of aging in complex organisms.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

Changes in an animal's behavior and internal state are accompanied by widespread changes in activity across its brain. However, how neurons across the brain encode behavior and how this is impacted by state is poorly understood. We recorded brain-wide activity and the diverse motor programs of freely moving C. elegans and built probabilistic models that explain how each neuron encodes quantitative behavioral features. By determining the identities of the recorded neurons, we created an atlas of how the defined neuron classes in the C. elegans connectome encode behavior. Many neuron classes have conjunctive representations of multiple behaviors. Moreover, although many neurons encode current motor actions, others integrate recent actions. Changes in behavioral state are accompanied by widespread changes in how neurons encode behavior, and we identify these flexible nodes in the connectome. Our results provide a global map of how the cell types across an animal's brain encode its behavior.

Single-cell transcriptomics of the Drosophila wing disc reveals instructive epithelium-to-myoblast interactions

In both vertebrates and invertebrates, generating a functional appendage requires interactions between ectoderm-derived epithelia and mesoderm-derived cells. To investigate such interactions, we used single-cell transcriptomics to generate a temporal cell atlas of the Drosophila wing disc from two developmental time points. Using these data, we visualized gene expression using a multilayered model of the wing disc and cataloged ligand–receptor pairs that could mediate signaling between epithelial cells and adult muscle precursors (AMPs). We found that localized expression of the fibroblast growth factor ligands, Thisbe and Pyramus, in the disc epithelium regulates the number and location of the AMPs. In addition, Hedgehog ligand from the epithelium activates a specific transcriptional program within adjacent AMP cells, defined by AMP-specific targets Neurotactin and midline, that is critical for proper formation of direct flight muscles. More generally, our annotated temporal cell atlas provides an organ-wide view of potential cell–cell interactions between epithelial and myogenic cells.

Linking transcriptomes with morphological and functional phenotypes in mammalian retinal ganglion cells

Retinal ganglion cells (RGCs) are the brain's gateway to the visual world. They can be classified into different types on the basis of their electrophysiological, transcriptomic, or morphological characteristics. Here, we characterize the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch sequencing (Patch-seq), providing functional and morphological annotation of many transcriptomic-defined cell types of a previously established RGC atlas. We show a convergence of different modalities in defining the RGC identity and reveal the degree of correspondence for well-characterized cell types across multimodal data. Moreover, we complement some RGC types with detailed morphological and functional properties. We also identify differentially expressed genes among ON, OFF, and ON-OFF RGCs such as Vat1l, Slitrk6, and Lmo7, providing candidate marker genes for functional studies. Our research suggests that the molecularly distinct clusters may also differ in their roles of encoding visual information.

Single-Cell Transcriptome Profiles Reveal Fibrocytes as Potential Targets of Cell Therapies for Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is potentially life-threatening in aging population due to the risk of aortic rupture and a lack of optimal treatment. The roles of different vascular and immune cells in AAA formation and pathogenesis remain to be future characterized. Single-cell RNA sequencing was performed on an angiotensin (Ang) II-induced mouse model of AAA. Macrophages, B cells, T cells, fibroblasts, smooth muscle cells and endothelial cells were identified through bioinformatic analyses. The discovery of multiple subtypes of macrophages, such as the re-polarization of Trem2⁺Acp5⁺ osteoclast-like and M2-like macrophages toward the M1 type macrophages, indicates the heterogenous nature of macrophages during AAA development. More interestingly, we defined CD45⁺COL1⁺ fibrocytes, which was further validated by flow cytometry and immunostaining in mouse and human AAA tissues. We then reconstituted these fibrocytes into mice with Ang II-induced AAA and found the recruitment of these fibrocytes in mouse AAA. More importantly, the fibrocyte treatment exhibited a protective effect against AAA development, perhaps through modulating extracellular matrix production and thus enhancing aortic stability. Our study reveals the heterogeneity of macrophages and the involvement of a novel cell type, fibrocyte, in AAA. Fibrocyte may represent a potential cell therapy target for AAA.

Modeling Human Thyroid Development by Fetal Tissue-Derived Organoid Culture

Euthyroidism is of profound importance for lifetime health. However, the early diagnosis or therapeutics of thyroid developmental defects has not been established, mainly due to limited understanding of human thyroid development and a lack of recapitulating research model. Herein, the authors elaborate the cell atlas and potential regulatory signaling of the evolution of heterogeneous thyrocyte population from 12 to 16 gestational weeks. Moreover, they establish a long-term culture of human fetal thyroid organoids (hFTOs) system, which retains the fetal thyroid lineages and molecular signatures, as well as the ability to generate functional human thyroid follicles post mice renal transplantation. Notably, cAMP signaling activation in hFTOs by forskolin boosts the maturation of follicle and thus thyroid hormone T4 secretion, which recapitulates the key developmental events of fetal thyroid. Employing this ex vivo system, it is found that enhanced chromatin accessibility at thyroid maturation genes (such as TPO and TG) loci permits the transcription for hormone production. This study provides the cell atlas of and an organoid model for human thyroid development, which will facilitate thyroid research and prospective medicine.

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets

Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. We present FIRM, a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. Compared with existing state-of-the-art integration methods, FIRM provides accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. FIRM is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.

Large-scale multiplexed mosaic CRISPR perturbation in the whole organism

Here, we report inducible mosaic animal for perturbation (iMAP), a transgenic platform enabling in situ CRISPR targeting of at least 100 genes in parallel throughout the mouse body. iMAP combines Cre-loxP and CRISPR-Cas9 technologies and utilizes a germline-transmitted transgene carrying a large array of individually floxed, tandemly linked gRNA-coding units. Cre-mediated recombination triggers expression of all the gRNAs in the array but only one of them per cell, converting the mice to mosaic organisms suitable for phenotypic characterization and also for high-throughput derivation of conventional single-gene perturbation lines via breeding. Using gRNA representation as a readout, we mapped a miniature Perturb-Atlas cataloging the perturbations of 90 genes across 39 tissues, which yields rich insights into context-dependent gene functions and provides a glimpse of the potential of iMAP in genome decoding.

Atlas of the neuromuscular system in the Trachymedusa Aglantha digitale: Insights from the advanced hydrozoan

Cnidaria is the sister taxon to bilaterian animals, and therefore, represents a key reference lineage to understand early origins and evolution of the neural systems. The hydromedusa Aglantha digitale is arguably the best electrophysiologically studied jellyfish because of its system of giant axons and unique fast swimming/escape behaviors. Here, using a combination of scanning electron microscopy and immunohistochemistry together with phalloidin labeling, we systematically characterize both neural and muscular systems in Aglantha, summarizing and expanding further the previous knowledge on the microscopic neuroanatomy of this crucial reference species. We found that the majority, if not all (~2,500) neurons, that are labeled by FMRFamide antibody are different from those revealed by anti-α-tubulin immunostaining, making these two neuronal markers complementary to each other and, therefore, expanding the diversity of neural elements in Aglantha with two distinct neural subsystems. Our data uncovered the complex organization of neural networks forming a functional "annulus-type" central nervous system with three subsets of giant axons, dozen subtypes of neurons, muscles, and a variety of receptors fully integrated with epithelial conductive pathways supporting swimming, escape and feeding behaviors. The observed unique adaptations within the Aglantha lineage (including giant axons innervating striated muscles) strongly support an extensive and wide-spread parallel evolution of integrative and effector systems across Metazoa.

There and back again: malaria parasite single-cell transcriptomics comes full circle

The Malaria Cell Atlas (MCA) is an ambitious, ongoing project to profile the intensity and heterogeneity of gene expression throughout the entire malaria parasite life cycle with single-cell resolution. Real et al. now complete the cycle by adding the transmission stages of the most virulent malaria parasite, Plasmodium falciparum, to this easy-to-use resource.

Multidimensional Single-Cell Analyses in Organ Development and Maintenance

The revolution of single-cell analysis tools in epigenomics, transcriptomics, lineage tracing, and transcriptome-scale RNA imaging, has boosted our understanding of the underlying molecular mechanisms during organ development and maintenance. Application of these tools enables the multidimensional study of organs, from cell atlas profiling, spatial organization, to cell-cell interaction. Here, we discuss recent progress in employing multidimensional single-cell analyses to address fundamental questions related to the development and maintenance of hematopoietic organs, brain and lung, which will also help provide insights into a better understanding of relevant diseases.