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Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy.
BACKGROUND: Musculoskeletal tissue degeneration impairs the life quality and motor function of many people, especially seniors and athletes. Tendinopathy is one of the most common diseases associated with musculoskeletal tissue degeneration, representing a major global healthcare burden that affects both athletes and the general population, with the clinical presentation of long-term recurring chronic pain and decreased tolerance to activity. The cellular and molecular mechanisms at the basis of the disease process remain elusive. Here, we use a single-cell and spatial RNA sequencing approach to provide a further understanding of cellular heterogeneity and molecular mechanisms underlying tendinopathy progression. RESULTS: To explore the changes in tendon homeostasis during the tendinopathy process, we built a cell atlas of healthy and diseased human tendons using single-cell RNA sequencing of approximately 35,000 cells and explored the variations of cell subtypes' spatial distributions using spatial RNA sequencing. We identified and localized different tenocyte subpopulations in normal and lesioned tendons, found different differentiation trajectories of tendon stem/progenitor cells in normal/diseased tendons, and revealed the spatial location relationship between stromal cells and diseased tenocytes. We deciphered the progression of tendinopathy at a single-cell level, which is characterized by inflammatory infiltration, followed by chondrogenesis and finally endochondral ossification. We found diseased tissue-specific endothelial cell subsets and macrophages as potential therapeutic targets. CONCLUSIONS: This cell atlas provides the molecular foundation for investigating how tendon cell identities, biochemical functions, and interactions contributed to the tendinopathy process. The discoveries revealed the pathogenesis of tendinopathy at single-cell and spatial levels, which is characterized by inflammatory infiltration, followed by chondrogenesis, and finally endochondral ossification. Our results provide new insights into the control of tendinopathy and potential clues to developing novel diagnostic and therapeutic strategies.
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KYNU as a Biomarker of Tumor-Associated Macrophages and Correlates with Immunosuppressive Microenvironment and Poor Prognosis in Gastric Cancer.
BACKGROUND: Kynureninase (KYNU) is a potential prognostic marker for various tumor types. However, no reports on the biological effects and prognostic value of KYNU in gastric cancer (GC) exist. METHODS: GC-associated single-cell RNA sequencing and bulk RNA sequencing (bulk-seq) data were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. The differential expression of KYNU between GC and normal gastric tissues was first analyzed based on the bulk-seq data, followed by an exploration of the relationship between KYNU and various clinicopathological features. The Kaplan-Meier survival and Cox regression analyses were performed to determine the prognostic value of KYNU. The relationship between KYNU expression and immune cell infiltration and immune checkpoints was also explored. The biological function of KYNU was further examined at the single-cell level, and in vitro experiments were performed to examine the effect of KYNU on GC cell proliferation and invasion. RESULTS: KYNU expression was significantly elevated in GC samples. Clinical features and survival analysis indicated that high KYNU expression was associated with poor clinical phenotypes and prognosis, whereas Cox analysis showed that KYNU was an independent risk factor for patients with GC. Notably, high expression of KYNU induced a poor immune microenvironment and contributed to the upregulation of immune checkpoints. KYNU-overexpressing macrophages drove GC progression through unique ligand-receptor pairs and transcription factors and were associated with adverse clinical phenotypes in GC. KYNU was overexpressed in GC cells in vitro, and KYNU knockout significantly inhibited GC cell proliferation and invasion. CONCLUSION: High KYNU expression promotes an adverse immune microenvironment and low survival rates in GC. KYNU and KYNU-related macrophages may serve as novel molecular targets in the treatment of GC.
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Single-cell transcriptome analysis reveals a cancer-associated fibroblast marker gene signature in hepatocellular carcinoma that predicts prognosis.
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death. Multi-pathway combination therapy is used to treat HCC, and immunotherapy is also a routine part of treatment. As a major component of the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) actively participate in cancer progression through complex functions. However, because CAFs dynamically change during cancer development, most of the current treatment strategies targeting CAFs fail. We created a prognostic CAF marker gene signature (CAFMGS) to investigate the utility of CAFs as a prognostic factor and therapeutic target. METHODS: Gene Expression Omnibus (GEO) single-cell RNA sequencing (Sc-RNA-seq) data were analyzed to identify CAF marker genes in HCC. The Cancer Genome Atlas (TCGA) database was used as a training cohort to construct the CAFMGS model and the International Cancer Genome Consortium (ICGC) dataset was used to validate the CAFMGS. RESULTS: Marker genes in the CAFMGS model were (0.0001-SPP1), (0.0084-VCX3A), (0.0015-HMGA1), (0.0082-PLOD2), and (0.0075-CACYBP). The CAFMGS_score was separated into high-risk and low-risk groups based on the median of the patients' OS. Univariate and multivariate analyses confirmed that CAFMGS_score was an independent prognostic factor in the training group. CAFMGS_score was a more accurate prognostic indicator compared with clinicopathological score and tumor mutational burden score. CONCLUSION: CAFMGS offers a fresh perspective on stromal cell marker genes in HCC prognosis and expands our knowledge of CAF heterogeneity and functional diversity, perhaps paving the way for CAF-targeted immunotherapy in HCC patients.
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Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP.
Understanding spatial organization of cells is critical for deciphering tissue function and disease. Single-cell RNA-sequencing (scRNA-seq) profiles transcriptomes at scale but loses spatial context, while spatial transcriptomics (ST) preserves spatial information but is constrained by cost and gene coverage. Here, we present REMAP, a deep learning framework that integrates gene expression with neighborhood-level gene-gene covariance to reconstruct multi-scale spatial organization of scRNA-seq data using one or multiple ST references. Across 2D and 3D mouse brain, human fetal cortex, and seven human cancer types, REMAP consistently outperformed existing approaches. Applied to a human multiple sclerosis atlas, REMAP resolved microglial neighborhood heterogeneity, and identified a rare pro-inflammatory microglia-astrocyte subpopulation. Across diverse cancers, REMAP recovered conserved spatially-defined cancer-associated fibroblast subtypes with known prognostic significance. By transforming cost-efficient single-cell datasets into spatially interpretable tissue maps, REMAP enables spatial hypothesis generation, microenvironment discovery, and population-scale inference of conserved and perturbed architectural principles in human disease.
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Single-Cell Atlas of Adult Testis in Protogynous Hermaphroditic Orange-Spotted Grouper, Epinephelus coioides.
Spermatogenesis is a process of self-renewal and differentiation in spermatogonial stem cells. During this process, germ cells and somatic cells interact intricately to ensure long-term fertility and accurate genome propagation. Spermatogenesis has been intensely investigated in mammals but remains poorly understood with regard to teleosts. Here, we performed single-cell RNA sequencing of ~9500 testicular cells from the male, orange-spotted grouper. In the adult testis, we divided the cells into nine clusters and defined ten cell types, as compared with human testis data, including cell populations with characteristics of male germ cells and somatic cells, each of which expressed specific marker genes. We also identified and profiled the expression patterns of four marker genes (calr, eef1a, s100a1, vasa) in both the ovary and adult testis. Our data provide a blueprint of male germ cells and supporting somatic cells. Moreover, the cell markers are candidates that could be used for further cell identification.
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Single-cell transcriptomic analysis reveals distinct plasma cell populations in chronic thromboembolic pulmonary hypertension.
BACKGROUND: Chronic thromboembolic pulmonary hypertension (CTEPH) presents challenges due to its complex pathobiology. Although numerous studies have reported heterogeneous cell types by single-cell RNA sequencing, the atlas and characteristics of plasma cells remain poorly understood. OBJECTIVES: To identify the altered phenotype and differentiation patterns of plasma cells in CTEPH. METHODS: We performed single-cell RNA sequencing on pulmonary endarterectomy tissue from 5 patients and 6 normal pulmonary arteries. Serum immunoglobulins (Igs) were measured using protein electrophoresis among 273 CTEPH patients, 259 idiopathic pulmonary arterial hypertension (IPAH) patients, and 251 healthy controls. RESULTS: The percentage of plasma cells was significantly increased from less than 1% in healthy controls to 15% in CTEPH patients. We identified 1 B cell cluster and 5 distinct mature plasma cell clusters, including IGHG1, HSPA1A, AHNAK, IGLC3, and IGKV4. Notably, the AHNAK and IGLC3 subclusters are newly identified. GeneSwitches analysis indicated early activation of IGHG1 and early deactivation of HLA-DPA1. The trajectory of AHNAK cluster was earlier than that of IGLC3 cluster, with an enrichment for pathways responsive to lipopolysaccharide. The IGLC3 cluster revealed lower differentiation potential and was predominantly associated with Ig production. Furthermore, Igα2 levels in CTEPH patients were lower than in controls but higher than in IPAH patients. Significantly, Igγ levels were markedly elevated in CTEPH patients compared with IPAH patients and controls, better distinguishing CTEPH patients from controls and IPAH patients. CONCLUSION: Plasma cells of CTEPH had a distinctive landscape and heterogeneity. The newly identified clusters represented excessive Ig production but lacked immune response function. These findings highlight that targeted plasma cells can be used to develop novel CTEPH treatments.
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Identification of ferroptosis-related gene signature for tuberculosis diagnosis and therapy efficacy.
Diagnosis of tuberculosis remains a challenge when microbiological tests are negative. Immune cell atlas of patients with tuberculosis and healthy controls were established by single-cell transcriptome. Through integrated analysis of scRNA-seq with microarray and bulk RNA sequencing data, a ferroptosis-related gene signature containing ACSL4, CTSB, and TLR4 genes that were associated with tuberculosis disease was identified. Four gene expression datasets from blood samples of patients with tuberculosis, latent tuberculosis infection, and healthy controls were used to assess the diagnostic value of the gene signature. The areas under the ROC curve for the combined gene signature were 1.000, 0.866, 0.912, and 0.786, respectively, in differentiating active tuberculosis from latent infection. During anti-tuberculosis treatment, the expression of the gene signature decreased significantly in cured patients with tuberculosis. In conclusion, the ferroptosis-related gene signature was associated with tuberculosis treatment efficacy and was a promising biomarker for differentiating active tuberculosis from latent infection.
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Six Novel Biomarkers for Diagnosis and Prognosis of Esophageal squamous cell carcinoma: validated by scRNA-seq and qPCR.
Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide. ESCC has a generally poor prognosis and there is a lack of available biomarkers for diagnosis and prognosis. The aim of the study was to identify novel biomarkers for ESCC. We screened the overlapping differentially expressed genes (DEGs) acquired from six Gene Expression Omnibus (GEO) ESCC datasets and The Cancer Genome Atlas (TCGA) ESCC datasets. Subsequently, protein-protein interaction network analysis was performed to identify the key hub genes. Then, Kaplan Meier survival and receiver operating curve (ROC) analysis were utilized to clarify the diagnostic and prognostic role of these hub genes. The UALCAN database, single cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (qPCR) were performed to confirm the expression levels of identified hub genes. Finally, immune infiltration analysis was conducted to investigate the role of these genes in the pathogenesis of ESCC. The results showed that PBK, KIF2C, NUF2, KIF20A, RAD51AP1, and DEPDC1 effectively distinguish ESCC tissues from normal samples, and all of them were significantly correlated with overall survival. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in ESCC were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed that infiltration of dendritic cells was significantly negatively correlated with PBK, KIF2C, NUF2, RAD51AP1, and DEPDC1 expression levels. In conclusion, our results suggest that PBK, KIF2C, NUF2, KIF20A, RAD51AP1 and DEPDC1 are all potential biomarkers for ESCC diagnosis and prognosis may also be potential therapeutic targets for ESCC.
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Spatial transcriptomic characterization of pathologic niches in IPF.
Despite advancements in antifibrotic therapy, idiopathic pulmonary fibrosis (IPF) remains a medical condition with unmet needs. Single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of IPF but lacks the cellular tissue context and gene expression localization that spatial transcriptomics provides. To bridge this gap, we profiled IPF and control patient lung tissue using spatial transcriptomics, integrating the data with an IPF scRNA-seq atlas. We identified three disease-associated niches with unique cellular compositions and localizations. These include a fibrotic niche, consisting of myofibroblasts and aberrant basaloid cells, located around airways and adjacent to an airway macrophage niche in the lumen, containing SPP1+ macrophages. In addition, we identified an immune niche, characterized by distinct lymphoid cell foci in fibrotic tissue, surrounded by remodeled endothelial vessels. This spatial characterization of IPF niches will facilitate the identification of drug targets that disrupt disease-driving niches and aid in the development of disease relevant in vitro models.
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Spatial multi-omics sequencing for fixed tissue via DBiT-seq.
This protocol describes the use of the deterministic barcoding in tissue for spatial omics sequencing platform to construct a multi-omics atlas on fixed frozen tissue samples. This approach uses a microfluidic-based method to introduce combinatorial DNA oligo barcodes directly to the cells in a tissue section fixed on a glass slide. This technique does not directly resolve single cells but can achieve a near-single-cell resolution for spatial transcriptomics and spatial analysis of a targeted panel of proteins. For complete details on the use and execution of this protocol, please refer to Liu et al. (2020).