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The molecular classification of cancer-associated fibroblasts on a pan-cancer single-cell transcriptional atlas.
BACKGROUND: Cancer-associated fibroblasts (CAFs), integral to the tumour microenvironment, are pivotal in cancer progression, exhibiting either pro-tumourigenic or anti-tumourigenic functions. Their inherent phenotypic and functional diversity allows for the subdivision of CAFs into various subpopulations. While several classification systems have been suggested for different cancer types, a unified molecular classification of CAFs on a single-cell pan-cancer scale has yet to be established. METHODS: We employed a comprehensive single-cell transcriptomic atlas encompassing 12 solid tumour types. Our objective was to establish a novel molecular classification and to elucidate the evolutionary trajectories of CAFs. We investigated the functional profiles of each CAF subtype using Single-Cell Regulatory Network Inference and Clustering and single-cell gene set enrichment analysis. The clinical relevance of these subtypes was assessed through survival curve analysis. Concurrently, we employed multiplex immunofluorescence staining on tumour tissues to determine the dynamic changes of CAF subtypes across different tumour stages. Additionally, we identified the small molecule procyanidin C1 (PCC1) as a target for matrix-producing CAF (matCAF) using molecular docking techniques and further validated these findings through in vitro and in vivo experiments. RESULTS: In our investigation of solid tumours, we identified four molecular clusters of CAFs: progenitor CAF (proCAF), inflammatory CAF (iCAF), myofibroblastic CAF (myCAF) and matCAF, each characterised by distinct molecular traits. This classification was consistently applicable across all nine studied solid tumour types. These CAF subtypes displayed unique evolutionary pathways, functional roles and clinical relevance in various solid tumours. Notably, the matCAF subtype was associated with poorer prognoses in several cancer types. The targeting of matCAF using the identified small molecule, PCC1, demonstrated promising antitumour activity. CONCLUSIONS: Collectively, the various subtypes of CAFs, particularly matCAF, are crucial in the initiation and progression of cancer. Focusing therapeutic strategies on targeting matCAF in solid tumours holds significant potential for cancer treatment.
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Single-cell-resolved transcriptional dynamics of human subcutaneous adipose tissue during lifestyle- and bariatric surgery-induced weight loss.
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 show how abdominal subcutaneous adipose tissue (SAT) in men and women with severe obesity adapts to modest lifestyle-induced (8-10%) weight loss followed by substantial bariatric surgery-induced (20-45%) weight loss, using single-nucleus RNA sequencing (snRNA-seq) combined with bulk RNA-seq, and three-dimensional light-sheet fluorescence microscopy. To enable interactive exploration, all snRNA-seq data are available in a browsable format on the Single Cell Portal ( SCP2849 ). 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.
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Single-cell RNA sequencing analysis reveals cellular heterogeneity and immune microenvironment dynamics in colorectal cancer.
BackgroundDespite treatment advances, prognoses of patients with advanced colorectal cancer remain poor. The limited effectiveness of programmed cell death protein 1 (PD-1) blockade immunotherapy in a subset of patients necessitates a deeper understanding of the colorectal cancer microenvironment. This study aimed to identify new therapeutic targets and potential biomarkers by investigating CD8 + CD101hiTim3+ (CCT) T cells and their progenitors in colorectal cancer, and their association with immunotherapy response at the transcriptomic level.MethodsWe used single-cell sequencing data and The Cancer Genome Atlas database for comprehensive bioinformatics analysis, including single-cell sequencing data analysis, Gene Set Variation Analysis, pseudotime analysis, cell communication analysis, and construction and validation of a prognostic model.ResultsKey findings include the identification and annotation of various T-cell subtypes in colorectal cancer, construction of a pseudotime trajectory of CCT T cells and their progenitors showing dynamic gene expression changes, and enhanced interactions between CCT T cell progenitors and other cells in the PD-1 immunotherapy group. We developed and validated a prognostic model comprising 15 gene features with strong prognostic stratification performance, revealing that high-risk patients exhibit transcriptomic associations with potentially reduced responses to immune checkpoint inhibitors (ICIs). Pathway enrichment analysis highlighted critical pathways, including leukocyte adhesion and T cell activation, indicating complex influences on disease progression and treatment responses within the colorectal cancer immune landscape.ConclusionsThis study emphasizes the potential importance of targeted therapies that modulate immune interactions and functional states to improve colorectal cancer prognosis, particularly in high-risk patients who show an association at the transcriptomic level with reduced ICI responsiveness.
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Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas.
We employed Stereo-seq combined with single-nucleus RNA sequencing (snRNA-seq) to investigate the gene expression and cell composition changes in human hippocampus with or without Alzheimer's disease (AD). The transcriptomic map, with single-cell precision, unveiled AD-associated alterations with spatial specificity, which include the following: (1) elevated synapse pruning gene expression in the fimbria of AD, with disrupted microglia-astrocyte communication likely leading to disorganized synaptic structure; (2) a globally increased energy generation in the cornu ammonis (CA) region, with varying degrees across its subregions; (3) a significant reduction in the number of CA1 neurons in AD, while CA4 neurons remained largely unaffected, potentially due to gene alterations in CA4 conferring resilience to AD; and (4) aggravated amyloid-beta (Aβ) plaques in CA1 and stratum lucidum, radiatum, and moleculare (SLRM), and integration of Stereo-seq map with Aβ staining revealed a sequential enrichment of microglia and astrocytes around Aβ plaques. Finally, reduced brain-derived extracellular vesicles carrying cholecystokinin (CCK) and peripheral myelin protein 2 (PMP2) in AD plasma highlighted their diagnostic potential for clinical applications.
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Single-cell transcriptomic profiling reveals diet-dependent dynamics of glucosinolate sulfatases expression and cellular origin in the midgut of Plutella xylostella.
The diamondback moth (Plutella xylostella), a globally destructive pest, has Brassicaceae as its long-term co-evolved host and can also utilize Fabaceae as an alternative field host. The primary differential factor between these plant families is glucosinolates (GLs). Conventional transcriptome data revealed high midgut expression of glucosinolate sulfatases (GSSs) in response to glucosinolates. However, due to the inability of conventional transcriptomics to resolve cellular heterogeneity, it remained unclear which specific cell types express GSSs and whether exposure to different field hosts triggers distinct differentiation fates in these cells. To address these questions, we constructed a high-resolution atlas of 28,451 midgut cells from larvae reared on radish and pea leaves. Marker gene-based clustering identified 16 distinct cell types, including enterocytes (ECs), enteroendocrine cells (EEs), and intestinal stem cells (ISCs). ScRNA-seq and qRT-PCR analyses revealed host-dependent differential expression patterns of three GSS genes (GSS1-GSS3) among ECs, EEs, and ISCs. Notably, elevated GSS3 protein levels in radish-fed larvae were confirmed by Western blot (WB) and Immunohistochemistry (IHC), while GSS1 and GSS2 exhibited more variable expression patterns. Pseudo-time trajectory analysis further demonstrated that all three GSSs followed differentiation pathways from ISCs into EEs or ECs, but dietary conditions led to distinct differentiation trajectories. This study elucidates the diet-dependent regulatory landscape of GSSs in P. xylostella and delineates the differentiation trajectory of GSS-expressing cells.
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Amelioration of psoriasis-like skin lesions by human amniotic mesenchymal stem cells: insights from multiomics profiling in mice.
BACKGROUND: Psoriasis is a multifactorial, chronic inflammatory skin disease. Current treatment modalities are limited by suboptimal patient responses and high recurrence rates after discontinuation. Consequently, there is an urgent need to develop novel therapeutic strategies for psoriasis. METHODS: An imiquimod-induced mouse model of psoriasis was established, and human amniotic mesenchymal stem cell (hAMSC) were subsequently administered to evaluate their therapeutic efficacy. Bioinformatic analyses of Gene Expression Omnibus (GEO) datasets GSE39035 and GSE97311 were performed to identify potential hAMSC therapeutic target genes. Using data from GSE228421, a single-cell transcriptomic atlas of psoriasis was constructed to examine the distribution and functional roles of these target genes across different cell populations. RESULTS: By integrating an imiquimod-induced murine model with comprehensive bioinformatic analyses of GEO datasets, we demonstrated that hAMSC administration significantly ameliorated psoriasis-like skin lesions, restored epidermal architecture, and reduced PASI and Baker scores. This therapeutic efficacy was accompanied by the alleviation of splenomegaly and a systemic reduction in inflammatory cytokines (IL-17 and TNF-α) without inducing hepatotoxicity. In vitro experiments further confirmed that hAMSCs inhibited TNF-α-induced keratinocyte proliferation and reactive oxygen species (ROS) generation. Transcriptomic profiling identified key immune-related targets, revealing that hAMSCs significantly modulated the expression of genes such as MMP9, S100A9, and BACH2. It is worth noting that single-cell atlas analysis has revealed that S100A9 and MMP9 play significant roles respectively in psoriasis-related CD8-IL17A T cells and M2-like macrophages, and further clarified the functional characteristics of S100A9 in the temporal development process of psoriasis fibroblasts and keratinocytes. CONCLUSIONS: In summary, our findings confirm the efficacy and safety of hAMSCs in the treatment of psoriasis and elucidate the underlying mechanisms of their therapeutic action.
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Single-cell Transcriptional Atlas of Human Hematopoiesis Reveals Genetic and Hierarchy-Based Determinants of Aberrant AML Differentiation.
UNLABELLED: Therapeutic targeting of acute myeloid leukemia (AML) is hampered by intra- and inter-tumoral cell state heterogeneity. To develop a more precise understanding of AML cell states, we constructed a reference atlas of human hematopoiesis from 263,159 single-cell transcriptomes spanning 55 cellular states. Using this atlas, we mapped more than 1.2 million cells spanning 318 leukemia samples, revealing 12 recurrent patterns of aberrant differentiation in AML. Notably, this uncovered unexpected AML cell states resembling lymphoid and erythroid progenitors that were prognostic within the clinically heterogeneous context of normal karyotype AML, independent of genomic classifications. Systematic mapping of genotype-to-phenotype associations revealed specific differentiation landscapes associated with more than 45 genetic drivers. Importantly, distinct cellular hierarchies can arise from samples sharing the same genetic driver, potentially reflecting distinct cellular origins for disease-sustaining leukemia stem cells. Thus, precise mapping of malignant cell states provides insights into leukemogenesis and refines disease classification in acute leukemia. SIGNIFICANCE: We present a single-cell reference atlas of human hematopoiesis and a computational tool for rapid mapping and classification of healthy and leukemic cells. Applied to AML, this has enabled single-cell analysis at the scale of hundreds of patient samples, revealing the full breadth of derailment of differentiation in AML. See related commentary by Berger and Penter, p. 280.
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Single-cell transcriptome analysis uncovers the molecular and cellular characteristics of thin endometrium.
Infertility is a social and medical problem around the world and the incidence continues to rise. Thin endometrium (TE) is a great challenge of infertility treatment, even by in vitro fertilization and embryo transfer. It is widely believed that TE impairs endometrium receptivity. However, only a few studies have explained the molecular mechanism. Herein, in order to reveal the possible mechanism, we sampled endometrium from a TE patient and a control volunteer and got a transcriptomic atlas of 18 775 individual cells which was constructed using single-cell RNA sequencing, and seven cell types have been identified. The cells were acquired during proliferative and secretory phases, respectively. The proportion of epithelial cells and stromal cells showed a significant difference between the TE group and the control group. In addition, differential expressed genes (DEGs) in diverse cell types were revealed, the enriched pathways of DEGs were found closely related to the protein synthesis in TE of both proliferative and secretory phases. Some DEGs can influence cell-type ratio and impaired endometrial receptivity in TE. Furthermore, divergent expression of estrogen receptors 1 and progesterone receptors in stromal and epithelial cells were compared in the TE sample from the control. The cellular and molecular heterogeneity found in this study provided valuable information for disclosing the mechanisms of impaired receptivity in TE.
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An integrated bulk and single-cell transcriptomic analysis reveals stemness-driven immune regulation and therapeutic vulnerability in colorectal cancer.
Tumor stemness is increasingly recognized as a key contributor to tumor heterogeneity, immune regulation, and therapeutic resistance in colorectal cancer (CRC). In this study, we developed a stemness-based risk model using bulk transcriptomic data from The Cancer Genome Atlas and evaluated its prognostic and therapeutic relevance through integrative analyses. The proposed risk score robustly stratified patients into distinct prognostic groups and remained an independent predictor of overall survival after adjustment for clinicopathological variables. Stemness-high tumors exhibited altered immune infiltration patterns and coordinated upregulation of immune checkpoint-related genes. Although the association between stemness score and immune evasion potential was modest, its clinical relevance was supported by validation in independent immunotherapy-treated cohorts, where low-risk patients demonstrated improved survival and higher response rates. Single-cell RNA sequencing (scRNA-seq) analysis further revealed that enhanced stemness and dedifferentiation were predominantly localized within malignant epithelial cells. Together, these findings establish tumor stemness as a central determinant of prognosis, immune regulation, and therapeutic vulnerability in CRC.
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A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response.
We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.