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Cell-type annotation with accurate unseen cell-type identification using multiple references.
The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.
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Single-cell atlas of endothelial cells in atherosclerosis: identifying C1 CXCL12+ ECs as key proliferative drivers for immunological precision therapeutics in atherosclerosis.
BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease characterized by endothelial dysfunction, monocyte infiltration, smooth muscle proliferation, and extracellular matrix accumulation. Endothelial cell (EC) dysfunction plays a pivotal role in the initiation and progression of AS. Despite progress in traditional research methods, the complexity of cellular heterogeneity within the disease remains poorly understood, necessitating a more refined approach for uncovering disease mechanisms. METHODS: In this study, we employed single-cell RNA sequencing (scRNA-seq) to map the endothelial cell landscape in AS comprehensively. By analyzing cellular heterogeneity, differentiation trajectories, and functional states, we identified critical endothelial subpopulations and their roles in the progression of AS. Functional enrichment and differentiation analyses were conducted, and the findings were validated through in vitro experiments. RESULTS: The single-cell analysis revealed distinct EC subpopulations with unique contributions to AS progression. Among these, C1 CXCL12+ ECs emerged as a key subpopulation associated with endothelial differentiation, vascular remodeling, and inflammation. These cells demonstrated high proliferative potential and were enriched in pathways related to endothelial migration and repair. Through CCK-8, Transwell assay, EdU staining and angiogenesis ability, we found that knockdown of FOXM1 in C1 CXCL12+ ECs resulted in decreased proliferation, migration and invasion. Thus, it affects the progression of AS. CONCLUSION: This study provides a detailed single-cell atlas of endothelial cells in AS, identifying critical subpopulations, regulatory pathways, and key factors driving disease progression. The application of single-cell technologies paves the way for advancing our understanding of cardiovascular diseases and offers significant potential for developing personalized therapeutic strategies in immunology and precision medicine.
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Mouse organogenesis atlas at single-cell resolution.
The generation of spatial transcriptomes of whole embryo has been limited in scale and resolution due to various technological restrictions. In this issue of Cell, Chen et al. introduce a DNA nanoball-based sample-capture technology for spatial transcriptome analysis to generate a molecular atlas of mouse organogenesis at single-cell resolution.
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Cellular diversity and gene expression profiles in the male and female brain of Aedes aegypti.
BACKGROUND: Aedes aegypti is a medically-important mosquito vector that transmits arboviruses including yellow fever, dengue, chikungunya, and Zika viruses to humans. The mosquito exhibits typical sexually dimorphic behaviors such as courtship, mating, host seeking, bloodfeeding, and oviposition. All these behaviors are mainly regulated by the brain; however, little is known about the function and neuron composition of the mosquito brain. In this study, we generated an initial atlas of the adult male and female brain of Ae. aegypti using 10xGenomics based single-nucleus RNA sequencing. RESULTS: We identified 35 brain cell clusters in male and female brains, and 15 of those clusters were assigned to known cell types. Identified cell types include glia (astrocytes), Kenyon cells, (ventral) projection neurons, monoaminergic neurons, medulla neurons, and proximal medulla neurons. In addition, the cell type compositions of male and female brains were compared to each other showing that they were quantitatively distinct, as 17 out of 35 cell clusters varied significantly in their cell type proportions. Overall, the transcriptomes from each cell cluster looked very similar between the male and female brain as only up to 25 genes were differentially expressed in these clusters. The sex determination factor Nix was highly expressed in neurons and glia of the male brain, whereas doublesex (dsx) was expressed in all neuron and glia cell clusters of the male and female brain. CONCLUSIONS: An initial cell atlas of the brain of the mosquito Ae. aegypti has been generated showing that the cellular compositions of the male and female brains of this hematophagous insect differ significantly from each other. Although some of the rare brain cell types have not been detected in our single biological replicate, this study provides an important basis for the further development of a complete brain cell atlas as well as a better understanding of the neurobiology of the brains of male and female mosquitoes and their sexually dimorphic behaviors.
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A single-cell atlas reveals shared and distinct immune responses and metabolic profiles in SARS-CoV-2 and HIV-1 infections.
Introduction: Within the inflammatory immune response to viral infection, the distribution and cell type-specific profiles of immune cell populations and the immune-mediated viral clearance pathways vary according to the specific virus. Uncovering the immunological similarities and differences between viral infections is critical to understanding disease progression and developing effective vaccines and therapies. Insight into COVID-19 disease progression has been bolstered by the integration of single-cell (sc)RNA-seq data from COVID-19 patients with data from related viruses to compare immune responses. Expanding this concept, we propose that a high-resolution, systematic comparison between immune cells from SARS-CoV-2 infection and an inflammatory infectious disease with a different pathophysiology will provide a more comprehensive picture of the viral clearance pathways that underscore immunological and clinical differences between infections. Methods: Using a novel consensus single-cell annotation method, we integrate previously published scRNA-seq data from 111,566 single PBMCs from 7 COVID-19, 10 HIV-1+, and 3 healthy patients into a unified cellular atlas. We compare in detail the phenotypic features and regulatory pathways in the major immune cell clusters. Results: While immune cells in both COVID-19 and HIV-1+ cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activity, and downregulated mitophagy. Discussion: Our results indicate that differential IFN-I signaling regulates the distinct immune responses in the two diseases, revealing insight into fundamental disease biology and potential therapeutic candidates.
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Mapping Gene Expression to an Interpretable Semantic Space.
Cell embeddings organize single-cell expression data, but their dimensions have no biological meaning, so clusters are interpreted afterward. We present MESIC (Mapping Expression to Semantic space with Interpretable Components), which builds the written knowledge about genes held in curated databases into the dimensions themselves. A biomedical language model converts each gene's summary into a semantic embedding. MESIC compresses these embeddings into a small number of components, each concentrated on a small set of genes and explained by their annotations. The components are computed once from the summaries, so any expression dataset can be mapped onto them, and every cluster, outlier, or cell-type assignment is then characterized by named genes. In cardiomyocytes, outliers in the component space were enriched for hypertrophic cardiomyopathy. In a lung atlas, unsupervised clusters in that space matched the broad cell types that experts had annotated. In both, the components that separated the cells matched their known biology. For about half of the cells that the atlas itself had left unannotated, the same space gave a confident cluster assignment, and with it an interpretation through component-associated genes. Gene summaries thus give single-cell analysis a coordinate system in which every result is traced to genes and what is written about them.
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Single-cell transcriptome analysis reveals status changes of immune cells in chronic kidney disease.
BACKGROUND AND AIMS: The immune system plays a crucial role in the development of kidney diseases. Chronic kidney disease (CKD) can lead to various complications, potentially affecting multiple systems throughout the body. Currently, the description of the immune system in human CKD is not comprehensive enough. Constructing a CKD kidney atlas using single-cell RNA sequencing (scRNA-seq) can provide deeper insights into the composition and functional changes of immune cells in CKD, facilitating the discovery of new therapeutic targets. METHODS: We processed and integrated scRNA-seq datasets from healthy and CKD kidneys from three independent cohorts using the same approach (including 42 normal samples and 23 chronic kidney disease samples). Subsequently, we conducted gene enrichment and intercellular communication analysis to construct an immune cell atlas of the kidneys in CKD patients. RESULTS: We identified nine major kidney cell clusters. Further clustering analysis of different immune cell clusters revealed that, compared to normal kidneys, CKD patients' kidneys had decreased CD16+ NK cells while CD4+ naive helper T cells and CCR7+ DC increased. Partial activation of the WNT signaling pathway was observed in T cells and NK cells of CKD patients, while some metabolism-related genes were inhibited. Myeloid cell subgroups also exhibited abnormal signaling pathway alterations. Additionally, we discovered a unique population of SPP1 macrophages in CKD, which are recruited by chemokines released from aPT and aTAL cell subpopulations. These SPP1 macrophages may promote cellular fibrosis through the signaling of SPP1, FN1, and various receptors. CONCLUSION: We established a human CKD kidney immune cell atlas and identified SPP1 macrophages as a unique cell type in CKD. The interaction between SPP1 macrophages and damaged cells may serve as a potential therapeutic target for treating CKD in the future.
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The Molecular Diversity of Vagal Afferents Revealed.
Using single-cell RNA sequencing (RNA-seq), Kupari and coworkers (Cell Rep., 2019) have generated a long sought-after molecular atlas of vagal afferents in the mouse. Vagal afferents were found to be organized into 24 subtypes, revealing a level of diversity that was not previously recognized.
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A Single-Cell Atlas of Crab Ovary Provides New Insights Into Oogenesis in Crustaceans.
Oogenesis is crucial for sexual reproduction and provides the material basis for population continuation. Nonetheless, the identity of the cells involved, the nature of transformation, and underlying regulators of oogenesis in crustaceans remain elusive. Here, an atlas of the ovary is plotted via single-nuclei RNA sequencing (snRNA-seq) in the mud crab Scylla paramamosain, resulting in five cell types, including germ cells, somatic cells, and three follicle cell types identified, which in turn provides abundant candidate markers for them. Moreover, profiles of ligand-receptor in different cells of the crab ovary indicate the roles of cell communication in oogenesis. Dozens of transcription factors in the trajectory from oogonia to oocytes as well as the key molecules/pathways in somatic cells and follicle cells relevant to oogenesis are screened, which is evolutionarily conserved and its underlying regulatory mechanism is subject to some modification across various phyla. The spatiotemporal expression patterns of seven markers are further verified and the RNAi confirms the essential roles of piwi and VgR in oogenesis. These data help to elucidate the mechanism underlying gametogenesis and the evolution of reproductive strategy in invertebrates.
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Single-cell RNA-sequencing reveals the dynamic process and novel markers in porcine spermatogenesis.
BACKGROUND: Spermatogenesis is the process by which male gametes are formed from spermatogonial stem cells and it is essential for the reliable transmission of genetic information between generations. To date, the dynamic transcriptional changes of defined populations of male germ cells in pigs have not been reported. RESULTS: To characterize the atlas of porcine spermatogenesis, we profiled the transcriptomes of ~ 16,966 testicular cells from a 150-day-old pig testis through single-cell RNA-sequencing (scRNA-seq). The scRNA-seq analysis identified spermatogonia, spermatocytes, spermatids and three somatic cell types in porcine testes. The functional enrichment analysis demonstrated that these cell types played diverse roles in porcine spermatogenesis. The accuracy of the defined porcine germ cell types was further validated by comparing the data from scRNA-seq with those from bulk RNA-seq. Since we delineated four distinct spermatogonial subsets, we further identified CD99 and PODXL2 as novel cell surface markers for undifferentiated and differentiating spermatogonia, respectively. CONCLUSIONS: The present study has for the first time analyzed the transcriptome of male germ cells and somatic cells in porcine testes through scRNA-seq. Four subsets of spermatogonia were identified and two novel cell surface markers were discovered, which would be helpful for studies on spermatogonial differentiation in pigs. The datasets offer valuable information on porcine spermatogenesis, and pave the way for identification of key molecular markers involved in development of male germ cells.