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Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.
A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.
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Identification of CACNB1 protein as an actionable therapeutic target for hepatocellular carcinoma via metabolic dysfunction analysis in liver diseases: An integrated bioinformatics and machine learning approach for precise therapy.
In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive analysis of the metabolic landscape is urgently needed to categorize patients into distinct subgroups for precise treatment. In this study, a total of 806 NAFLD and 267 normal liver samples were comprehensively analyzed. Alterations in 114 metabolic pathways were investigated and two distinct metabolic clusters were identified. Single-cell RNA sequencing (scRNA-seq) analysis was utilized to decipher the metabolic activities within the microenvironment of NAFLD-derived liver cirrhosis. A refined fibrosis prediction model was developed using a Gaussian Mixture Model (GMM), demonstrating superior performance in fibrosis discrimination across multiple independent cohorts. Additionally, using The Cancer Genome Atlas (TCGA), CACNB1 protein was identified as a promising therapeutic target for hepatocellular carcinoma (HCC) patients with elevated metabolic dysfunction scores (MBDS). Machine learning algorithms were applied to MBDS-related genes to select an optimal prognostic model for HCC. All the models were trained in an HCC cohort obtained from the Gene Expression Omnibus (GEO), and the best model was validated in two independent HCC datasets: the TCGA-HCC cohort and LIRI-JP cohort. Overall, we provide insights of metabolic molecular subtyping and its potential clinical applicability in risk stratification for NAFLD and HCC individuals.
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Strategy of Patient-Specific Therapeutics in Cardiovascular Disease Through Single-Cell RNA Sequencing.
Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.
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Lipid metabolism classification of gliomas.
Lipid metabolic reprogramming represents a fundamental oncogenic mechanism. However, clinical relevance of lipid metabolism (LM) alterations in gliomas remain to be fully elucidated. LM-based classification and subsequent correlative analyses were performed using glioma bulk RNA-seq datasets retrieved from The Cancer Genome Atlas (TCGA). Radiomic models were constructed and validated using magnetic resonance imaging (MRI) datasets from The Cancer Imaging Archive (TCIA) and our in-house glioma cohort. Immunohistochemical (IHC) staining was employed to detect the protein expression of the key gene in glioma tissue samples. Single cell RNA-seq datasets from the GBMap database were used to characterize the distribution and functional roles of the key gene in the tumor microenvironment (TME) of gliomas. Consensus clustering based on LM pathways identified three distinct subtypes, respectively dominated by steroid metabolism (ST-type), triglyceride metabolism (TC-type), and sphingolipid metabolism (SP-type). The SP-type was independently associated with poorer prognosis and displayed enhanced activity in pathways linked to aggressive tumor phenotypes and radiotherapy resistance. Radiomic features enabled accurate identification of SP-type gliomas, thereby offering a non-invasive strategy for predicting this subtype. Genes GLA, GBL1, and HSD3B7 were identified as signature genes of the SP-type. Higher expression of HSD3B7 was associated with poor prognosis and exerted functional effects on multiple signaling pathways across various cellular components of the TME, which may contribute to glioma progression. Gliomas exhibited marked heterogeneity in LM and can be classified into three subtypes, with the SP-type exhibiting the most aggressive clinical behavior.
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TDO2-Associated Tryptophan Metabolism Correlates with Impaired Tertiary Lymphoid Structure Maturation and Reduced B Cell Class Switching in Breast Cancer.
BACKGROUND: Tertiary lymphoid structures (TLSs) promote antitumor immunity and predict favorable immunotherapy outcomes in breast cancer. The study aimed to investigate how Tryptophan 2,3-dioxygenase (TDO2)-associated tryptophan metabolism influences TLS maturation and B cell class switching in breast cancer. METHODS: Bulk transcriptomic data from The Cancer Genome Atlas-Breast Invasive Carcinoma (TCGA-BRCA, n = 1055) were analyzed using Gene Set Variation Analysis (GSVA)-based metabolic scoring, immune deconvolution, and TLS quantification. Single-cell RNA sequencing (scRNA-seq, n = 26) and spatial transcriptomics (n = 1) were applied to map TDO2 expression and TLS spatial organization. Validation was performed by immunohistochemistry (n = 38) and multiplex immunofluorescence (n = 12). RESULTS: We identified that elevated tryptophan metabolism was predominantly enriched in the Luminal A subtype and delineates an immune-cold phenotype with less immunogenicity, associated with a distinct immune-dominant cellular microenvironment, particularly enriched in T and plasma cells. High expression of the tryptophan-metabolizing enzyme TDO2 was significantly enriched in TLS-low tumors and negatively correlated with TLS maturation signatures. Functional enrichment revealed suppressed B cell class switching and attenuated C-X-C motif chemokine ligand 9 (CXCL9) expression in TLS-deficient tumors. Spatial transcriptomics and hotspot analysis demonstrated an inverse spatial correlation between TDO2 expression and TLS core components. Tumors with high tryptophan metabolism showed decreased cluster of differentiation 20 (CD20)+ and CXCL9+ cell infiltration within TLS zones. Tumors with strong TDO2-kynurenine activity displayed impaired TLS organization and attenuated humoral immunity. Conditional spatial co-occurrence modeling confirmed reduced proximity between tryptophan metabolism hotspots and TLS-related immune features. CONCLUSION: In conclusion, our findings suggest that TDO2-associated tryptophan metabolism is linked to impaired TLS maturation and suppressed B cell class switching in breast cancer. Targeting the TDO2-kynurenine axis may represent a promising strategy to restore TLS formation and enhance immunotherapy responsiveness in breast cancer.
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FOntCell: Fusion of Ontologies of Cells.
High-throughput cell-data technologies such as single-cell RNA-seq create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information. However, one needs to merge them to synergistically combine their information. The main difficulty in merging is to match the ontologies since they use different naming conventions. Therefore, we developed an algorithm that merges ontologies by integrating the name matching between class label names with the structure mapping between the ontology elements based on graph convolution. Since the structure mapping is a time consuming process, we designed two methods to perform the graph convolution: vectorial structure matching and constraint-based structure matching. To perform the vectorial structure matching, we designed a general method to calculate the similarities between vectors of different lengths for different metrics. Additionally, we adapted the slower Blondel method to work for structure matching. We implemented our algorithms into FOntCell, a software module in Python for efficient automatic parallel-computed merging/fusion of ontologies in the same or similar knowledge domains. FOntCell can unify dispersed knowledge from one domain into a unique ontology in OWL format and iteratively reuse it to continuously adapt ontologies with new data endlessly produced by data-driven classification methods, such as of the Human Cell Atlas. To navigate easily across the merged ontologies, it generates HTML files with tabulated and graphic summaries, and interactive circular Directed Acyclic Graphs. We used FOntCell to merge the CELDA, LifeMap and LungMAP Human Anatomy cell ontologies into a comprehensive cell ontology. We compared FOntCell with tools used for the alignment of mouse and human anatomy ontologies task proposed by the Ontology Alignment Evaluation Initiative (OAEI) and found that the Fβ alignment accuracies of FOntCell are above the geometric mean of the other tools; more importantly, it outperforms significantly the best OAEI tools in cell ontology alignment in terms of Fβ alignment accuracies.
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Highlighting fibroblast plasticity in lung fibrosis: the WI-38 cell line as a model for investigating the myofibroblast and lipofibroblast switch.
Background: Myofibroblasts (MYFs) are generally considered the principal culprits in excessive extracellular matrix deposition and scar formation in the pathogenesis of lung fibrosis. Lipofibroblasts (LIFs), on the other hand, are defined by their lipid-storing capacity and are predominantly found in the alveolar regions of the lung. They have been proposed to play a protective role in lung fibrosis. We previously reported that a LIF to MYF reversible differentiation switch occurred during fibrosis formation and resolution. In this study, we tested whether WI-38 cells, a human embryonic lung fibroblast cell line, could be used to study fibroblast differentiation towards the LIF or MYF phenotype and whether this could be relevant for idiopathic pulmonary fibrosis (IPF). Methods: Using WI-38 cells, Fibroblast (FIB) to MYF differentiation was triggered using TGF-β1 treatment and FIB to LIF differentiation using Metformin treatment. We also analyzed the MYF to LIF and LIF to MYF differentiation by pre-treating the WI-38 cells with TGF-β1 or Metformin respectively. We used IF, qPCR and bulk RNA-Seq to analyze the phenotypic and transcriptomic changes in the cells. We correlated our in vitro transcriptome data from WI-38 cells (obtained via bulk RNA sequencing) with the transcriptomic signature of LIFs and MYFs derived from the IPF cell atlas as well as with our own single-cell transcriptomic data from IPF patients-derived lung fibroblasts (LF-IPF) cultured in vitro. We also carried out alveolosphere assays to evaluate the ability of the proposed LIF and MYF cells to support the growth of alveolar epithelial type 2 cells. Results: WI-38 cells and LF-IPF display similar phenotypical and gene expression responses to TGF-β1 and Metformin treatment. Bulk RNA-Seq analysis of WI-38 cells and LF-IPF treated with TGF-β1, or Metformin indicate similar transcriptomic changes. We also show the partial conservation of the LIF and MYF signature extracted from the Habermann et al. scRNA-seq dataset in WI-38 cells treated with Metformin or TGF-β1, respectively. Alveolosphere assays indicate that LIFs enhance organoid growth, while MYFs inhibit organoid growth. Finally, we provide evidence supporting the MYF to LIF and LIF to MYF reversible switch using WI-38 cells. Conclusions: WI-38 cells represent a versatile and reliable model to study the intricate dynamics of fibroblast differentiation towards the MYF or LIF phenotype associated with lung fibrosis formation and resolution, providing valuable insights to drive future research.
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Rheumatoid arthritis, systemic lupus erythematosus and primary Sjögren's syndrome shared megakaryocyte expansion in peripheral blood.
OBJECTIVES: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share many clinical manifestations and serological features. The aim of this study was to identify the common transcriptional profiling and composition of immune cells in peripheral blood in these autoimmune diseases (ADs). METHODS: We analysed bulk RNA-seq data for enrichment of biological processes, transcription factors (TFs) and deconvolution-based immune cell types from peripheral blood mononuclear cells (PBMCs) in 119 treatment-naive patients (41 RA, 38 pSS, 28 SLE and 12 polyautoimmunity) and 20 healthy controls. The single-cell RNA-seq (scRNA-seq) and flow cytometry had been performed to further define the immune cell subsets on PBMCs. RESULTS: Similar transcriptional profiles and common gene expression signatures associated with nucleosome assembly and haemostasis were identified across RA, SLE, pSS and polyautoimmunity. Distinct TF ensembles and gene regulatory network were mainly enriched in haematopoiesis. The upregulated cell-lineage-specific TFs PBX1, GATA1, TAL1 and GFI1B demonstrated a strong gene expression signature of megakaryocyte (MK) expansion. Gene expression-based cell type enrichment revealed elevated MK composition, specifically, CD41b+CD42b+ and CD41b+CD61+ MKs were expanded, further confirmed by flow cytometry in these ADs. In scRNA-seq data, MKs were defined by TFs PBX1/GATA1/TAL1 and pre-T-cell antigen receptor gene, PTCRA. Cellular heterogeneity and a distinct immune subpopulation with functional enrichment of antigen presentation were observed in MKs. CONCLUSIONS: The identification of MK expansion provided new insights into the peripheral immune cell atlas across RA, SLE, pSS and polyautoimmunity. Aberrant regulation of the MK expansion might contribute to the pathogenesis of these ADs.
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Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level.
BACKGROUND: Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain. RESULTS: Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not. The results showed increased infiltration of immune cells such as monocyte macrophages, T cells, and natural killer (NK) cells in patients who did not respond to estrogen therapy. Our findings indicate that distinct fibroblast subsets are implicated in the modulation of the Wnt, Hippo, and Hedgehog signaling pathways, as evidenced by functional enrichment analyses. This may have implications for the therapeutic efficacy in patients with IUA. Furthermore, we delineated the markers and transcriptional status of different macrophage subsets and identified two cell clusters, CXCL10high and CCL4L2high macrophage subsets, which are intimately associated with inflammation and fibrosis. The state of fibrosis and inflammatory response in human endometrial tissues with disparate treatment outcomes is revealed, and providing evidence to clarify the underlying determinants of sensitivity to estrogen therapy. CONCLUSIONS: We described the transcriptional status of different cell subtypes in the two groups of patients, providing new ideas for exploring the molecular mechanism of the difference in the effectiveness of estrogen therapy in patients, and providing theoretical basis for providing precise and individualized treatment plans for IUA patients.
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Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis.
We provide a single-cell atlas of idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease, by profiling 312,928 cells from 32 IPF, 28 smoker and nonsmoker controls, and 18 chronic obstructive pulmonary disease (COPD) lungs. Among epithelial cells enriched in IPF, we identify a previously unidentified population of aberrant basaloid cells that coexpress basal epithelial, mesenchymal, senescence, and developmental markers and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells, we identify an ectopically expanded cell population transcriptomically identical to bronchial restricted vascular endothelial cells in IPF. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells, we identify IPF myofibroblasts and invasive fibroblasts with partially overlapping cells in control and COPD lungs. Last, we confirm previous findings of profibrotic macrophage populations in the IPF lung. Our comprehensive catalog reveals the complexity and diversity of aberrant cellular populations in IPF.