Publication
Training can improve insulin sensitivity in individuals with type 2 diabetes, but a clear understanding of the mechanisms remains elusive. To further our knowledge in this area, we aimed to examine the effect of type 2 diabetes and of high-intensity interval training (HIIT) on the nuclear transcriptional response in skeletal muscle. We performed single-nucleus RNA-sequencing (snRNA-seq) and immunofluorescence analysis on muscle biopsies from the trained and the untrained legs of participants with and without type 2 diabetes, after 2 weeks of one-legged HIIT on a cycle ergometer. Surprisingly, the type 2 diabetes condition only seemed to have a minor effect on transcriptional activity in myonuclei related to major metabolic pathways when comparing the untrained legs. However, while in particular the type IIA myonuclei in the control group displayed a considerable metabolic response to HIIT, with increases in genes related to glycogen breakdown and glycolysis primarily in the type IIA myonuclei of the trained leg, this response was blunted in the diabetes group, despite a marked increase in glucose clearance in both groups. Additionally, we observed that fibre type distribution assessed by immunofluorescence significantly correlated with the proportion of myonuclei in the snRNA-seq analysis. In conclusion, the type 2 diabetes condition blunts the metabolic transcriptional response to HIIT in the type IIA myonuclei without affecting the improvement in insulin sensitivity. Additionally, our results indicate that snRNA-seq can be used as a surrogate marker for fibre type distribution in sedentary middle-aged adults.
Human cells share an identical genome yet exhibit diverse functions shaped by their tissue environment and cell-type-specific regulatory programs. While single-cell transcriptomic atlases have mapped cellular diversity across many human tissues, comparable atlases of regulatory elements at single-cell resolution remain scarce, limiting our understanding of how regulatory codes vary across organs. To address this gap, we generated high-quality 10x Genomics Multiome profiles from 21 human tissues collected from 4 GTEx donors, jointly measuring gene expression and chromatin accessibility in over 450,000 single cells. This cross-organ resource enables systematic characterization of transcription factor activity, cis-regulatory elements, and their coupling to gene expression across diverse cell types and tissue contexts. Together, these data illuminate the regulatory diversity of human single cells across organs and provide a foundational reference for studying tissue-specific gene regulation in health and disease.
Human white adipose tissue undergoes major remodelling during sustained weight gain that may compromise tissue function and drive cardiometabolic comorbidities. Although weight loss reverses many of these complications, the cellular and molecular adaptations of adipose tissue to different weight loss interventions are poorly understood. Here we profiled abdominal subcutaneous adipose tissue (SAT) from men and women living with severe obesity using single-nucleus RNA sequencing (snRNA-seq). Biopsies from the same individuals were collected at baseline, after modest lifestyle-induced (8–10%) weight loss, and again after bariatric surgery-induced (20–45%) weight loss. Lifestyle-induced weight loss activated proadipogenic gene programmes in progenitor cells, indicating early beneficial effects on SAT. Subsequent surgery-induced weight loss drove profound compositional and transcriptional remodelling of SAT, including increased vascularization and marked reduction of myeloid cell populations. Collectively, our study indicates that following major and sustained weight loss, SAT from individuals with severe obesity has the capacity to return to a state comparable to that observed in lean individuals.
While it is well established that the cellular composition of white adipose tissue (WAT) varies between depots, the functional relevance of this heterogeneity remains unclear. By combining spatial and single-nucleus RNA sequencing, we provide a comprehensive map of subcutaneous and visceral (omental, mesenteric, mesocolic, and epiploic) WAT in both men and women. Our analyses reveal shared features, such as the spatial organization of adipogenesis, alongside depot-specific characteristics, including distinct cell-type enrichments and unique cell-cell communication routes. Epiploic WAT stands out by harboring high proportions of serum amyloid A expressing fat cells (encoded by SAA1/SAA2) and several leukocyte populations. Through mechanistic studies, we demonstrate that adipocyte SAA1/SAA2 expression is induced by inflammatory signals, including lipopolysaccharide, and that SAA1 activates immune responses in adipose-resident myeloid cells. Collectively, our findings suggest that visceral WAT exhibits distinct cytoarchitectural properties, with those located near the colon adapting by developing specialized adipocytes and immune cell populations.
To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states.
Precision medicine is still not considered as a standard of care in obesity treatment, despite a large heterogeneity in the metabolic phenotype of individuals with obesity. One of the strongest factors influencing the variability in metabolic disease risk is adipose tissue (AT) dysfunction; however, there is little understanding of the link between distinct cell populations, cell-type-specific transcriptional programs, and disease severity. Here, we generated a comprehensive cellular map of subcutaneous and visceral AT of individuals with metabolically healthy and unhealthy obesity. By combining single-nucleus RNA-sequencing data with bulk transcriptomics and clinical parameters, we identified that mesothelial cells, adipocytes, and adipocyte-progenitor cells exhibit the strongest correlation with metabolic disease. Furthermore, we uncovered cell-specific transcriptional programs, such as the transitioning of mesothelial cells to a mesenchymal phenotype, that are involved in uncoupling obesity from metabolic disease. Together, these findings provide valuable insights by revealing biological drivers of clinical endpoints.
Reducing body weight to improve metabolic health and related comorbidities is a primary goal in treating obesity. However, maintaining weight loss is a considerable challenge, especially as the body seems to retain an obesogenic memory that defends against body weight changes. Overcoming this barrier for long-term treatment success is difficult because the molecular mechanisms underpinning this phenomenon remain largely unknown. Here, by using single-nucleus RNA sequencing, we show that both human and mouse adipose tissues retain cellular transcriptional changes after appreciable weight loss. Furthermore, we find persistent obesity-induced alterations in the epigenome of mouse adipocytes that negatively affect their function and response to metabolic stimuli. Mice carrying this obesogenic memory show accelerated rebound weight gain, and the epigenetic memory can explain future transcriptional deregulation in adipocytes in response to further high-fat diet feeding. In summary, our findings indicate the existence of an obesogenic memory, largely on the basis of stable epigenetic changes, in mouse adipocytes and probably other cell types. These changes seem to prime cells for pathological responses in an obesogenic environment, contributing to the problematic ‘yo-yo’ effect often seen with dieting. Targeting these changes in the future could improve long-term weight management and health outcomes.
Human adipose depots are functionally distinct. Yet, recent single-nucleus RNA-sequencing (snRNA-seq) analyses largely uncovered overlapping/similar cell-type landscapes. We hypothesized that adipocytes subtypes, differentiation trajectories, and/or intercellular communication patterns could illuminate this depot similarity-difference gap. For this, we performed snRNA-seq of human subcutaneous/visceral adipose tissues (5/10 samples, respectively). Of 27,665 adipocyte nuclei in both depots, the majority were “classical”, namely- enriched in lipid metabolism pathways. However, we also observed “non-classical” adipocyte subtypes, enriched in immune-related, extracellular matrix deposition (fibrosis), vascularization/angiogenesis, or ribosomal processes. Pseudo-temporal analysis showed a developmental trajectory from adipose progenitor cells to classical adipocytes via non-classical adipocytes, suggesting that the classical state stems from loss, rather than gain, of specialized functions. Lastly, intercellular communication routes were consistent with the different inflammatory tone of the two depots. Jointly, these findings provide a high-resolution view into the contribution of cellular composition, differentiation, and intercellular communication patterns to human fat depot differences.
The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1–3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions—in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.
Immunological processes that underpin human immune responses to therapeutics and vaccine components, such as vaccine adjuvants, remain poorly defined due to a paucity of models that faithfully recapitulate immune activation in lymphoid tissues. We describe precision-cut human lymph node (LN) slices as a functioning, architecturally preserved, full-organ cross-sectional model system. Using single-cell transcriptomics and multiplexed imaging, we explore early inflammatory response to a potent, clinically relevant liposomal vaccine adjuvant containing a TLR4-agonist and QS-21 saponin. Both TLR4 and NLRP3 inflammasome activation are involved in the direct initiation of the inflammatory response to adjuvant by monocytes and macrophages (Mon./Mac.) with secretion of interleukin (IL)-1β, but not IL-18, dependent on TLR4 signaling. Innate lymphoid cells, including natural killer cells, are indirectly activated by Mon./Mac.-produced cytokines, signaling downstream to B cells via interferon-γ secretion. Resident LN stromal populations, primed both directly and indirectly by vaccine adjuvant, are instrumental in mediating inflammatory cell recruitment, particularly neutrophils.
The Human Gut Cell Atlas (HGCA) brings together the collective expertise of gut biologists, clinicians, and data generators to create a community-driven, healthy reference for the human gut. It integrates 27 datasets representing several sample collection and library preparation methods from donors across age groups, with samples dissected or biopsied from the duodenum to the rectum. The gut is a complex organ, stratified along its longitudinal and radial axes and responsible for everything from nutrient absorption and excretion to immune regulation. The HGCA integrates 959,289 cells of 503 samples from 266 donors and provides community-informed annotations for a preliminary 105 cell types, including location-specific transcriptional states and rare intestinal populations. The atlas will continue to be versioned to reflect expanding cell type, technological, and population diversity, supporting a high-quality, community-maintained resource for the field. We invite gut and single-cell researchers to put their critical thinking caps on and help shape the HGCA's annotations by voting, reasoning, and contributing directly here on CAP.
The survival and proper function of the neural retina depend on the support of two underlying tissues: the retinal pigment epithelium (RPE) and the choroid. Dysfunction in the RPE and choroid can lead to a range of inherited and complex diseases. To better characterize the cellular, transcriptomic, and epigenomic heterogeneity and dynamics within the RPE and choroid, we conducted single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) as well as single-nucleus ATAC sequencing (snATAC-seq) on donor tissue samples. By integrating newly generated and previously published datasets, we developed version 1 of the human RPE and choroid cell atlas. This atlas includes scRNA-seq data from 205,925 cells from 67 donors and snRNA-seq data from 742,625 nuclei from 54 donors. Additionally, we incorporated snRNA-seq data from 136,555 nuclei from developmental RPE/choroid tissues spanning post-conception weeks (PCW) 10-23. This comprehensive dataset enabled the identification of 15 major cell classes, which were further subdivided into 25 cell types. Additionally, open chromatin profiling was performed on over 234,007 nuclei using snATAC-seq, providing an in-depth characterization of chromatin accessibility for each cell type. As part of the Human Cell Atlas project, this resource lays a robust foundation for advancing research into the biology of the RPE and choroid and the diseases associated with these tissues.
The integrated Human Lung Cell Atlas (HLCA) represents the first large-scale, integrated single-cell reference atlas of the human lung. It consists of over 2 million cells from the respiratory tract of 486 individuals, and includes 49 different datasets. It is split into the HLCA core, and the extended or full HLCA. The HLCA core includes data of healthy lung tissue from 107 individuals, and includes manual cell type annotations based on consensus across 6 independent experts, as well as demographic, biological and technical metadata. The datasets in the HLCA core were integrated using scANVI. The HLCA core can be used as a reference to map new datasets onto using scArches. The full HLCA includes 35 further datasets that include donors with various lung diseases. These datasets were mapped onto the core with scArches, and include disease annotations as well as cell type annotations transferred from the HLCA core onto the mapped datasets. Note that while the the HLCA includes an integrated, batch-correctd low-dimensional embedding, the gene counts themselves were not batch-corrected. Both the HLCA core and the full HLCA can be explored below. Detailed information about all metadata in the objects, as well as further HLCA-related information , can be found on the HLCA landing page: https://github.com/LungCellAtlas/HLCA. Raw counts are available in the downloaded .h5ad at adata.raw.X, in the downloaded .rds at seurat_object@assays$RNA@counts, and via CELLxGENE Census.
We provide a large-scale reference atlas of human hematopoiesis with balanced representation of CD34+ stem and progenitor cells together with terminally differentiated populations. Our reference map of 263,159 hematopoietic cells provides us with deep portraits of lineage transitions in both early and late stages of human hematopoiesis. HSPC annotations within this atlas were carefully validated to maximize concordance with immunophenotypically pure populations established through decades of functional studies. In particular, our transcriptionally defined HSC state is highly concordant with immunophenotypically defined Lin-CD34+CD38-CD45RA-CD90+CD49f+ long-term HSCs. Please visit the Github page for our R package to access functions for mapping query scRNA-seq profiles of normal or leukemic hematopoietic cells onto BoneMarrowMap. The entire process is optimized to take <10 min total for mapping ~100,000 single cells on a personal laptop. See also our Blood Cancer Discovery publication (Zeng et al, 2025) for more details on the atlas and an example of its application to leukemia.
We present the Human Liver Cell Atlas (HLiCA), integrating non-disease liver data from eight datasets across six research centers, covering 525,000+ cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the atlas incorporates expert-reviewed cell annotations, extensive feedback, and focused jamborees for refining myeloid and hepatocyte classifications.
The optic nerve is essential for transmitting visual information from the retina to the brain, where it is processed into images. Damage to the optic nerve can cause vision loss, leading to conditions such as glaucoma, optic neuritis, and optic neuropathy. To advance our understanding of the transcriptomic and epigenetic landscapes, as well as the dynamic cellular processes of the optic nerve, we conducted single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) on the optic nerve head (ONH) and optic nerve (ON) tissues from over 80 healthy human donor eyes spanning diverse ages, genders, and ethnic backgrounds. By integrating newly generated and previously published snRNA-seq datasets, we constructed a comprehensive ONH and ON cell atlas encompassing transcriptomic profiles of 959,629 nuclei. Our analysis identified 25 distinct cell classes and 86 cell types/states, including diverse populations of astrocytes, oligodendrocytes, oligodendrocyte precursor cells, microglia, and macrophages. Furthermore, snATAC-seq profiling of approximately 1 million nuclei generated high-resolution chromatin accessibility landscapes, enabling the identification of cis-regulatory elements and transcription factors specific to individual cell classes and types. As part of the Human Cell Atlas initiative, our ON atlas serves as a crucial resource for studying optic nerve physiology and deepening our understanding of the cellular and molecular mechanisms underlying optic neuropathies and related diseases.
The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis.
Fibroblasts are essential cells that sculpt the architecture and cellular microenvironments within various tissues. Understanding fibroblast heterogeneity and their spatial context in health and disease has major clinical relevance. In this study, we constructed a spatially-resolved atlas of 357 276 human skin fibroblasts from healthy skin and 23 skin disorders. We define 6 major skin fibroblast populations in health and a further three skin disease-specific fibroblast subtypes, and demonstrate the fibroblast composition in different types of skin disease. We also show that inflammatory myofibroblasts (IL11+MMP1+CXCL5+IL7R+) present in early human wounds are a conserved fibroblast subtype in inflammatory disorders and cancer, that likely recruit neutrophils and monocytes to different human tissues. We characterise a human-specific fibroblastic reticular cell (FRC)-like subtype in the skin superficial perivascular niche and postulate their origin from prenatal skin lymphoid tissue organiser (LTo)-like cells. We provide a harmonised nomenclature for skin fibroblasts that integrates previous findings from human skin and other tissues.
Organoids of the endoderm can recapitulate aspects of developing and adult human physiology. Organoids derived from embryonic or induced pluripotent stem cells model development and are guided to specific tissue types via morphogens, whereas organoids derived from tissue-resident fetal or adult stem cells are organ-identity-determined and may model ontogenetic features. However, it has remained difficult to assess the similarity and differences between organoid protocols, and to understand the precision and accuracy of organoid cell states through comparison with primary counterparts. Advances in computational single-cell biology allow the comprehensive integration of datasets with high technical variability. Here, we integrate published single-cell transcriptome datasets from organoids of diverse endoderm-derived tissues including lung, pancreas, intestine, salivary glands, liver, biliary system, stomach, and prostate to establish an initial version of a human endoderm organoid cell atlas (HEOCA). The integration includes nearly one million cells across diverse conditions and data sources. We align and compare cell types and states between organoid models, and harmonize cell type annotations by mapping the atlas to primary tissue counterparts. We focus on intestine and lung, and clarify developmental and adult physiology that can be modeled in vitro. We provide examples of data incorporation from new organoid protocols to expand the atlas, and showcase how comparison to the atlas can illuminate interesting biological features of new datasets. We also show that mapping disease organoid single-cell samples to HEOCA identifies shifts in cell proportion and gene expressions between normal and diseased cells. Taken together, the atlas makes diverse datasets centrally available, and it will be useful to assess organoid fidelity, characterize perturbed and diseased states, streamline protocol development, and will continuously grow in the future.
Understanding kidney health and disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied single-cell and single-nucleus assays to a broad spectrum of healthy reference and disease kidneys. This has provided a high-resolution cellular atlas that includes rare and novel cell populations. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several nephron segments. These analyses further define biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that are associated with a decline in kidney function during chronic kidney disease. Our collaborative efforts across the Kidney Precision Medicine Project (KPMP), the Human BioMolecular Atlas Program (HuBMAP) and the Human Cell Atlas consortia aim to generate a comprehensive human kidney cell atlas that includes injury and disease-relevant cell states and neighborhoods as a valuable resource to the research community. Here we provide an expanded data set from that published previously (Lake et al., Nature 2023) that includes additional KPMP biopsy data.
The Human Cell Atlas data provides the “recipe” for tissue engineering, including organoids. Human organoids – three-dimensional structures of cells that recapitulate organ development in vitro – hold tremendous potential for biomedical applications. They provide tractable models of human physiology and pathology, thereby enabling interventional studies that are difficult or impossible to conduct in human subjects, and reducing the need for animal experiments. Moreover, they can provide patient-specific “avatars” for drug development and personalized therapies, and they help advance regenerative medicine, with the ultimate goal of aiming towards regenerative medicine, and producing functional biological structures that can be transplanted into patients. Single-cell epigenome/transcriptome sequencing and spatial profiling can provide a comprehensive assessment of cell composition and cell states within organoids, enabling a comparison to matched primary tissue samples. These comparisons can catalyse the development of better organoid protocols. The Organoid Cell Atlas will foster the production, quality control, dissemination, and utilization of single-cell data for human organoids, and it will connect such datasets with the comprehensive profiles of primary tissue that are being generated within the HCA.
The trabecular meshwork (TM) and ciliary body (CB) play critical roles in regulating aqueous humor homeostasis in the human eye. The TM serves as the primary outflow pathway for aqueous humor, while the CB controls lens shape and produces aqueous humor. Both tissues are composed of diverse cell types, with cell abundances varying across several orders of magnitude. To create a comprehensive single-cell atlas and advance our understanding of TM and CB biology, we generated large-scale datasets from healthy human donors using single-nucleus RNA-seq (snRNA-seq), single-cell RNA-seq (scRNA-seq), and single-nucleus ATAC-seq (snATAC-seq) across a range of ages, genders, and ethnic backgrounds. Additionally, we performed a meta-analysis by integrating previously published datasets. All data were uniformly preprocessed and combined, resulting in a comprehensive TM and CB atlas that includes transcriptomic profiles of over 1 million cells from 70 donors. We identified 19 distinct cell types, including rare populations such as immune cells, with the least abundant types representing as little as 0.08% of the total cell population. Furthermore, snATAC-seq profiling of over 500,000 nuclei provided detailed chromatin accessibility landscapes, allowing for the identification of cis-regulatory elements specific to each cell type. All datasets are publicly accessible via CELLxGENE and the UCSC Cell Browser, enabling interactive exploration of gene expression and chromatin states at the single-cell level. As part of the Human Cell Atlas initiative, our TM and CB atlas offers a valuable resource for understanding eye physiology and establishes a foundation for further research into the cellular and molecular mechanisms regulating intraocular pressure and related ocular diseases.
The cornea, limbus, and sclera form the ocular surface, a critical interface that protects the eye, maintains visual function, and provides structural support. The cornea serves as the primary refractive surface and barrier against external pathogens. The limbus, located at the corneal-scleral junction, houses limbal stem cells essential for the regeneration of the corneal epithelium. The sclera provides structural integrity, protects the eye, and serves as the attachment site for extraocular muscles. Together, these three components play integral roles in sustaining ocular function and homeostasis. To capture the major cell types and subclasses of the ocular surface, we generated large-scale datasets using scRNA-seq and snRNA-seq technologies from healthy human donors spanning a wide range of ages, genders, and ethnicities. Additionally, publicly available datasets were integrated to expand the scope of the atlas. In total, the atlas includes over 1 million cells and nuclei from 102 donors. We identified 10 major cell classes and 31 types in the atlas, including rare populations such as limbal progenitor cells, Schlemn’s canel endothelium, and specific immune subtypes. The atlas also captures the full trajectory of epithelial differentiation. To further enhance our understanding of gene regulation, we generated a snATAC-seq atlas, profiling over 362k nuclei from 26 donors, including 8 developmental samples, facilitating the characterization of cell type-specific chromatin accessibility landscapes and regulatory elements. All datasets underwent a unified preprocessing pipeline and data integration to ensure consistency and quality. The atlas is publicly accessible through CELLxGENE, enabling interactive exploration of gene expression and regulatory landscapes. As part of the Human Cell Atlas initiative, this comprehensive ocular surface atlas marks a significant advance in understanding ocular tissue physiology and provides a valuable resource for studying ocular surface development, aging, and diseases such as corneal dystrophies, limbal stem cell deficiency, and scleral remodeling.
As the light sensing part of the visual system, the human retina is composed of five classes of neuron, including photoreceptors, horizontal cells, amacrine, bipolar, and retinal ganglion cells. Each class of neuron can be further classified into subgroups with the abundance varying three orders of magnitude. Therefore, to capture all cell types in the retina and generate a complete single cell reference atlas, it is essential to scale up from currently published single cell profiling studies to improve the sensitivity. In addition, to gain a better understanding of gene regulation at single cell level, it is important to include sufficient scATAC-seq data in the reference. To fill the gap, we performed snRNA-seq and snATAC-seq for the retina from healthy donors. To further increase the size of the dataset, we then collected and incorporated publicly available datasets. All data underwent a unified preprocessing pipeline and data integration. Multiple integration methods were benchmarked by scIB, and scVI was chosen. To harness the power of multiomics, snATAC-seq datasets were also preprocessed, and scGlue was used to generate co-embeddings between snRNA-seq and snATAC-seq cells. To facilitate the public use of references, we employ CELLxGENE and UCSC Cell Browser for visualization. By combining previously published and newly generated datasets, a single cell atlas of the human retina that is composed of 2.5 million single cells from 48 donors has been generated. As a result, over 90 distinct cell types are identified based on the transcriptomics profile with the rarest cell type accounting for about 0.01% of the cell population. In addition, open chromatin profiling has been generated for over 400K nuclei via single nuclei ATAC-seq, allowing systematic characterization of cis-regulatory elements for individual cell type. Integrative analysis reveals intriguing differences in the transcriptome, chromatin landscape, and gene regulatory network among cell class, subgroup, and type. In addition, changes in cell proportion, gene expression and chromatin openness have been observed between different gender and over age. Accessible through interactive browsers, this study represents the most comprehensive reference cell atlas of the human retina to date. As part of the human cell atlas project, this resource lays the foundation for further research in understanding retina biology and diseases.
Tonsils are constantly exposed to antigens through the upper respiratory tract, making them a valuable secondary lymphoid organ (SLO) for studying the interaction between innate and adaptive immune cells during germinal center (GC) development, which is crucial for building adaptive immunity. This reference includes 377,963 cells (10x Genomics 3' v3) from 17 healthy human donors spanning various age groups, including children, young adults, and older adults. The annotation provided in this first version comprises 42 categories that provide a stable categories to classify single-cell transcriptomes of SLOs, useful for tools like Azimuth (see external URLs). In the next version, we will add a more detailed classification, encompassing all cell types and states identified in the tonsil atlas. A validation cohort was included to confirm the presence and accuracy of each annotation this latter level, using criteria such as cell neighborhood preservation, conservation of bona fide marker genes, and annotation confidence derived from the KNN classifier. For a full description and interpretation of this validation cohort, please refer to the final section of the manuscript. The tonsil atlas is a FAIR resource—findable, accessible, interoperable, and reusable. The raw data has been deposited in ArrayExpress and can be remapped and reanalyzed by following the instructions provided in the TonsilAtlas and TonsilAtlasCAP GitHub repositories. The resulting expression matrices and Seurat objects are available on Zenodo, and the data can be accessed, explored, interpreted, and reused via the HCATonsilAtlas Bioconductor package and the Azimuth web interface. Additionally, we provide a detailed glossary of the marker genes, rationales, and publications used to annotate each cell type and state. The external URLs section contains links to all these resources.
Crohn’s disease is an inflammatory bowel disease (IBD) commonly treated through anti-TNF blockade. However, most patients still relapse and inevitably progress. Comprehensive single-cell RNA-sequencing (scRNA-seq) atlases have largely sampled patients with established treatment-refractory IBD, limiting our understanding of which cell types, subsets, and states at diagnosis anticipate disease severity and response to treatment. Here, through combining clinical, flow cytometry, histology, and scRNA-seq methods, we profile diagnostic human biopsies from the terminal ileum of treatment-naïve pediatric patients with Crohn’s disease (pediCD; n=14), matched repeat biopsies (pediCD-treated; n=8) and from non-inflamed pediatric controls with functional gastrointestinal disorders (FGID; n=13). To resolve and annotate epithelial, stromal, and immune cell states among the 201,883 baseline single-cell transcriptomes, we develop a principled and unbiased tiered clustering approach, ARBOL. Through flow cytometry and scRNA-seq, we observe that treatment-naïve pediCD and FGID have similar broad cell type composition. However, through high-resolution scRNA-seq analysis and microscopy, we identify significant differences in cell subsets and states that arise during pediCD relative to FGID. By closely linking our scRNA-seq analysis with clinical meta-data, we resolve a vector of T cell, innate lymphocyte, myeloid, and epithelial cell states in treatment-naïve pediCD (pediCD-TIME) samples which can distinguish patients along the trajectory of disease severity and anti-TNF response. By using ARBOL with integration, we position repeat on-treatment biopsies from our patients between treatment-naïve pediCD and on-treatment adult CD. We identify that anti-TNF treatment pushes the pediatric cellular ecosystem towards an adult, more treatment-refractory state. Our study jointly leverages a treatment-naïve cohort, high-resolution principled scRNA-seq data analysis, and clinical outcomes to understand which baseline cell states may predict Crohn’s disease trajectory.
Lack of diversity and proportionate representation in genomics datasets and databases contributes to inequity in healthcare outcomes globally. The relationships of human diversity with biological and biomedical phenotypes are pervasive, yet remain understudied, particularly in a single-cell genomics context. Here we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA-sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 healthy donors and 6 controls, spanning 7 population groups across 5 countries. AIDA is one of the largest healthy blood datasets in terms of number of cells, and also the most diverse in terms of number of population groups. Though population groups are frequently compared at the continental level, we identified a pervasive impact of sub-continental diversity on cellular and molecular properties of immune cells. These included cell populations and genes implicated in disease risk and pathogenesis as well as those relevant for diagnostics. We identified numerous examples where the effects of age and sex were modulated by self-reported ethnicity. We also detected age, sex, and self-reported ethnicity differences at the resolution of cell neighbourhoods, highlighting finer-grained distinctions than were apparent at cell-type level. We discovered functional genetic variants influencing cell type-specific gene expression, including context-dependent effects, which were under-represented in analyses of non-Asian population groups, and which helped contextualise disease-associated variants. We validated our findings using multiple independent datasets and cohorts. AIDA provides fundamental insights into the relationships of human diversity with immune cell phenotypes, enables analyses of multi-ancestry disease datasets, and facilitates the development of precision medicine efforts in Asia and beyond.
