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Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB-CD8+ memory T cells and accumulation of type 2 memory T cells.
Extensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for the systematic understanding of human aging. Here, using single-cell RNA/T cell receptor (TCR)/BCR-seq with protein feature barcoding, we profiled 317 samples from 166 healthy individuals aged 25-85 years old. From this, we generated a dataset from ∼2 million cells that described 55 subpopulations of blood immune cells. Twelve subpopulations changed with age, including the accumulation of GZMK+CD8+ T cells and HLA-DR+CD4+ T cells. In contrast to other T cell memory subsets, transcriptionally distinct NKG2C+GZMB-CD8+ T cells counterintuitively decreased with age. Furthermore, we found a concerted age-associated increase in type 2/interleukin (IL)4-expressing memory subpopulations across CD4+ and CD8+ T cell compartments (CCR4+CD8+ Tcm and Th2 CD4+ Tmem), suggesting a systematic functional shift in immune homeostasis with age. Our work provides novel insights into healthy human aging and a comprehensive annotated resource.
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Single-cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland.
The thyroid gland regulates growth and metabolism via production of thyroid hormone in follicles composed of thyrocytes. So far, thyrocytes have been assumed to be a homogenous population. To uncover heterogeneity in the thyrocyte population and molecularly characterize the non-thyrocyte cells surrounding the follicle, we developed a single-cell transcriptome atlas of the region containing the zebrafish thyroid gland. The 6249-cell atlas includes profiles of thyrocytes, blood vessels, lymphatic vessels, immune cells, and fibroblasts. Further, the thyrocytes show expression heterogeneity, including bimodal expression of the transcription factor pax2a. To validate thyrocyte heterogeneity, we generated a CRISPR/Cas9-based pax2a knock-in line that monitors pax2a expression in the thyrocytes. A population of pax2a-low mature thyrocytes interspersed in individual follicles can be distinguished. We corroborate heterogeneity within the thyrocyte population using RNA sequencing of pax2a-high and pax2a-low thyrocytes, which demonstrates 20% differential expression in transcriptome between the two subpopulations. Our results identify and validate transcriptional differences within the presumed homogenous thyrocyte population.
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Single-cell RNA sequencing reveals altered placental microenvironment due to maternal high-fat diet.
BACKGROUND: The prevalence of childhood metabolic diseases has markedly increased in recent decades whereas the effects of maternal nutrients affecting the placental microenvironment are not clearly addressed. METHODS: In the present study, female C57BL/6J mice were fed with a high-fat diet (HFD) or control diet (CON) for 5 weeks prior to mating and then during pregnancy. Single-cell RNA sequencing (scRNA-seq) was used to dissect the placental cell atlas at embryonic day(E) 18.5. RESULTS: Maternal HFD led to a reduction in the composition of placental labyrinth zone (LZ) trophoblast cells and endothelial cells, together with a diminished LZ area and narrowed fetal vessels, related to a decreased placental efficiency. Pro-inflammatory placental NK1 cells and M1-like macrophages were more apparent in the placenta from HFD dams with inflammation features. Increased glycogen deposition and glycogen trophoblast cells were also detectable in the HFD placenta, likely associated with the altered metabolism of placental NK cells and macrophages. CONCLUSIONS: Maternal HFD before and during pregnancy leads to an altered placental microenvironment with more inflammatory features and abnormal metabolic properties, which provides a better understanding on the mechanisms of intergenerational inheritance.
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Single-cell transcriptomic landscape identifies the expansion of peripheral blood monocytes as an indicator of HIV-1-TB co-infection.
Certain circulating cell subsets are involved in immune dysregulation in human immunodeficiency virus type 1 (HIV-1) and tuberculosis (TB) co-infection; however, the characteristics and role of these subclusters are unknown. Peripheral blood mononuclear cells (PBMCs) of patients with HIV-1 infection alone (HIV-pre) and those with HIV-1-TB co-infection without anti-TB treatment (HIV-pre & TB-pre) and with anti-TB treatment for 2 weeks (HIV-pre & TB-pos) were subjected to single-cell RNA sequencing (scRNA-seq) to characterize the transcriptome of different immune cell subclusters. We obtained > 60,000 cells and identified 32 cell subclusters based on gene expression. The proportion of immune-cell subclusters was altered in HIV-1-TB co-infected individuals compared with that in HIV-pre-group, indicating immune dysregulation corresponding to different disease states. The proportion of an inflammatory CD14+CD16+ monocyte subset was higher in the HIV-pre & TB-pre group than in the HIV-pre group; this was validated in an additional cohort (n = 80) via a blood cell differential test, which also demonstrated a good discriminative performance (area under the curve, 0.8046). These findings depicted the atlas of immune PBMC subclusters in HIV-1-TB co-infection and demonstrate that monocyte subsets in peripheral blood might serve as a discriminating biomarker for diagnosis of HIV-1-TB co-infection.
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Single-cell sequencing reveals a regulatory role of WT1 in mast cell proliferation in endometrial polyps.
Endometrial polyps are the predominant structural anomalies of the endometrial mucosa observed in unexplained infertility cases, potentially compromising endometrial receptivity and suggesting shared etiological characteristics. However, their comprehensive cell atlas and immune landscape remain inadequately defined. In this study, we employed single-cell RNA sequencing and bulk RNA-seq to systematically analyze ectopic endometrial polyps (EPs) alongside adjacent eutopic endometrial tissues (EUs). This enabled us to delineate alterations in cell composition and transcriptional dynamics across diverse cell types associated with endometrial polyps. Notably, we observed an increase and activation of mast cells, with significant transcriptional profile variations. Through transcription regulatory network analysis, WT1 was identified as a pivotal transcriptional regulator mediating mast cell proliferation in EPs, concomitant with the dysregulation of WT1 target genes involved in cell growth. These findings provide novel insights into the cellular heterogeneity and molecular mechanisms of endometrial polyps at single-cell resolution, presenting potential therapeutic targets for clinical intervention.
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Molecular and spatial signatures of mouse brain aging at single-cell resolution.
The diversity and complex organization of cells in the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline during aging. Here, we generated a high-resolution cell atlas of brain aging within the frontal cortex and striatum using spatially resolved single-cell transcriptomics and quantified changes in gene expression and spatial organization of major cell types in these regions over the mouse lifespan. We observed substantially more pronounced changes in cell state, gene expression, and spatial organization of non-neuronal cells over neurons. Our data revealed molecular and spatial signatures of glial and immune cell activation during aging, particularly enriched in the subcortical white matter, and identified both similarities and notable differences in cell-activation patterns induced by aging and systemic inflammatory challenge. These results provide critical insights into age-related decline and inflammation in the brain.
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Single-Nuclei Characterization of Lacrimal Gland in Scopolamine-Induced Dry Eye Disease.
PURPOSE: The lacrimal gland (LG) is the main organ responsible for tear secretion and an important pathogenic site for dry eye disease (DED). This study aimed to comprehensively characterize LG cellular heterogeneity under normal and DED conditions using single-nucleus RNA sequencing (snRNA-seq). METHODS: Single LG nuclei isolated from mice with or without DED induced by scopolamine (SCOP)/desiccating stress (DS) were subjected to snRNA-seq using the 10x Genomics platform. These cells were clustered and annotated using the t-distributed stochastic neighbor embedding (t-SNE) method and unbiased computational informatic analysis. Cluster identification and functional analysis were performed based on marker gene expression and bioinformatic data mining. RESULTS: The snRNA-seq analysis of 30,351 nuclei identified eight major cell types, with acinar cells (∼72.6%) being the most abundant cell type in the LG. Subclustering analysis revealed that the LG mainly contained two acinar cell subtypes, two ductal cell subclusters, three myoepithelial cell (MECs) subtypes, and four immunocyte subclusters. In the SCOP-induced DED model, three major LG parenchymal cell types were significantly altered, characterized by a reduced proportion of acinar cells with a lowered secretion potential and an augmented proportion of ductal cells and MECs. LG immunocytes in DED scenarios showed an intensified inflammatory response and dysregulated intercellular communication with three major LG parenchymal cells. CONCLUSIONS: Overall, this study offers a systemic single-nucleus transcriptomic profile of LGs in both normal and DED conditions and an atlas of the complicated interactions of immunocytes with major LG parenchymal cells. The findings also facilitate understanding the pathogenesis of DED.
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Spatial atlas of upper tract urothelial carcinoma identifies SLC14A1 + and S100A8/9+ tumor cells as key determinants of malignant progression.
Upper tract urothelial carcinoma (UTUC) is characterized by a high incidence of muscle invasion and distinct molecular heterogeneity, with tumor microenvironment (TME) heterogeneity playing a pivotal role in its malignant progression. However, the spatial distribution patterns of functional cell populations in UTUC and their regulatory mechanisms driving high-grade (HG) and muscle-invasive (MI) progression remain largely unelucidated. Herein, we integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (ST-seq) to systematically characterize the cellular landscape, spatial molecular network, and intercellular communication profile of UTUC tissues. Six tumor cell subpopulations were identified in UTUC, among which SLC14A1⁺ tumor cells were correlated with favorable clinical prognosis, whereas S100A8/9⁺ tumor cells were associated with poor clinical outcomes. Mechanistically, SLC14A1⁺ tumor cells decelerated UTUC progression via modulating cell apoptosis mediated by the FASLG-TNFRSF10B ligand-receptor pair. In contrast, S100A8/9⁺ tumor cells promoted the malignant progression of UTUC by regulating extracellular matrix remodeling and epithelial-mesenchymal transition (EMT) via the CCL19-CCR7, CCN2-EGFR, ADAM28-ITGA4 and CD14-ITGB2 ligand-receptor pairs. Collectively, our study uncovers the cellular and spatial heterogeneity of the UTUC TME, identifies SLC14A1⁺ tumor cells as a tumor-suppressive subpopulation and S100A8/9⁺ tumor cells as a key driver of UTUC malignant progression, and further delineates the core spatial signaling pathways underlying HG and MI progression of UTUC. These findings provide novel prognostic biomarkers and potential therapeutic targets for UTUC, and advance our mechanistic understanding of spatial TME regulation in urothelial carcinoma.
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MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data.
Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package "mogsa."
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Single cell transcriptome profiling of infrapatellar fat pad highlights the role of interstitial inflammatory fibroblasts in osteoarthritis.
OBJECTIVES: Osteoarthritis (OA) is a whole-joint disease in which the role of the infrapatellar fat pad (IFP) in its pathogenesis is unclear. Our study explored the cellular heterogeneity of IFP to understand OA and identify therapeutic targets. METHODS: Single-cell and single-nuclei RNA sequencing were used to analyze 10 IFP samples, comprising 5 from OA patients and 5 from healthy controls. Analyses included differential gene expression, enrichment, pseudotime trajectory, and cellular communication, along with comparative studies with visceral and subcutaneous fats. Key subcluster and pathways were validated using multiplex immunohistochemistry. RESULTS: The scRNA-seq performed on the IFPs of the OA and control group profiled the gene expressions of over 49,674 cells belonging to 11 major cell types. We discovered that adipose stem and progenitor cells (ASPCs), contributing to the formation of both adipocytes and synovial-lining fibroblasts (SLF). Interstitial inflammatory fibroblasts (iiFBs) were a subcluster of ASPCs that exhibit notable pro-inflammatory and proliferative characteristics. We identified four adipocyte subtypes, with one subtype showing a reduced lipid synthesis ability. Furthermore, iiFBs modulated the activities of macrophages and T cells in the IFP. Compared to subcutaneous and visceral adipose tissues, iiFBs represented a distinctive subpopulation of ASPCs in IFP that regulated cartilage proliferation through the MK pathway. CONCLUSION: This study presents a comprehensive single-cell transcriptomic atlas of IFP, uncovering its complex cellular landscape and potential impact on OA progression. Our findings highlight the role of iiFBs in OA, especially through MK pathway, opening new avenues for understanding OA pathogenesis and developing novel targeted therapies.