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Comprehensive single-cell RNA analysis reveals intertumoral microenvironment heterogeneity and hub niche of carcinogenesis in thyroid cancer.
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving cancer progression, we performed single-cell RNA sequencing (scRNA-seq) analysis, enabling a comprehensive exploration of cellular diversity and molecular dynamics at single-cell resolution. We employed Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and subsequent identification of cellular clusters. Differential gene expression analysis across subclusters was conducted using the FindAllMarkers function, while the DoHeatmap function was utilized to visualize the distribution of differentially expressed genes. The AUCell algorithm was applied to evaluate pathway enrichment within specific cell subtypes. To decipher cellular communication networks, we integrated the CellChat and NicheNet algorithms, which revealed intricate intercellular signaling interactions. Finally, multiplex immunohistochemistry (mIHC) was performed to validate key cellular interactions identified in silico. By analyzing 405,077 single cells from 50 thyroid cancer samples (including papillary, anaplastic, and metastatic tumors) and 14 normal thyroid tissues, we identified four major cellular subpopulations through unbiased clustering based on gene expression patterns and representative cellular markers. The TME was found to encompass diverse immune, endothelial, and mesenchymal cell subtypes, including novel populations such as CD4 + HSPA1A + T cells. Functional pathway enrichment analysis highlighted the roles of abundant cell types in tumor progression. Cell-cell communication analysis uncovered potential immunotherapeutic targets and revealed critical crosstalk among hub niche cells, including APOE+ macrophages, EMT-like cancer-associated fibroblasts (CAFs), and RBP7+ endothelial cells. These findings were further validated by multiplex immunohistochemistry, confirming the spatial organization and interactions of these cell populations within the TME. Our study provides a comprehensive single-cell transcriptomic atlas of thyroid cancer, offering profound insights into tumor heterogeneity, the functional roles of key niche cells, and potential biomarkers for anticancer therapy. These findings not only enhance our understanding of thyroid cancer biology but also pave the way for the development of novel therapeutic strategies targeting the TME.
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Identification of endothelial INSR as an osteosarcoma-related biomarker and therapeutic target based on weighted gene co-expression network analysis.
BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor characterized by a high rate of metastasis. The clinical efficacy of current targeted drugs for OS is limited, highlighting the urgent need to identify novel therapeutic targets. The study aimed to identify potential drug targets for OS by combining weighted gene co-expression network analysis (WGCNA) with single-cell analysis. METHODS: OS-related genes were obtained from the osteosarcoma database, and prognosis-related genes were identified using The Cancer Genome Atlas (TCGA) data. Gene expression profiles and corresponding clinical data were extracted from TCGA, Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Univariate and multivariate Cox analyses were used to screen the genes associated with OS prognosis. WGCNA was applied to detect genes linked to clinical characteristics in OS patients. Differentially expressed genes (DEGs) were analyzed using the GEO dataset. Single-cell RNA sequencing (scRNA-seq) data were employed to assess the expression pattern of the crucial genes in different types of cells in the tumor microenvironment. RESULTS: A total of 911 OS-related genes were retrieved from the osteosarcoma database. Prognosis analysis identified 110 prognosis-associated genes (P < 0.05). WGCNA revealed 295 genes related to OS clinical characteristics within the turquoise module. DEG analysis across GEO datasets (GSE12865, GSE16088, and GSE14359) identified 220 DEGs (|log2 (FC)| >1 and P < 0.05). Integration of these analyses identified 16 key genes in OS. Based on the expression profiles of these genes, OS patients were classified into three distinct clusters that exhibited significant differences in survival time, immune scores, and T-cell CD4 memory resting levels (P < 0.05). Insulin receptor (INSR) was identified as an independent risk factor predominantly expressed in endothelial cells. CONCLUSION: INSR expression in OS tissues is associated with poor clinical outcomes. Activation or dysregulation of endothelial INSR contributes to OS progression, suggesting that endothelial INSR may represent a novel therapeutic target for OS.
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Single-nucleus RNA sequencing reveals a key regulator RsHB8 affecting vascular cambium activity and xylem differentiation during radish taproot thickening.
Radish taproot thickening is primarily driven by vascular cambium (VC) activity. However, the transcriptional regulatory mechanisms underlying taproot thickening at a single-cell resolution remain unclear. In this study, single-nucleus isolation coupled with fluorescence-activated cell sorting was used to construct a cell atlas of radish taproots using microdroplet-based single-nucleus RNA sequencing. Through canonical marker gene profiling and validation using laser capture microdissection transcriptome data, 12 cell types were identified, and the first single-nucleus transcriptomic atlas was constructed for radish taproots. VC cells exhibited heterogeneous transcriptional states with varying levels of expression of cell cycle-associated genes. Pseudotime analysis inferred a transcriptional trajectory from VC cells toward xylem parenchyma cells in radish taproots. Integrating pseudotime analysis with predicted protein-protein interaction networks highlighted HOMEOBOX 8 (RsHB8) as a candidate regulator of the inferred VC-to-xylem parenchyma (XP) trajectory. RsHB8m overexpression increased taproot width and promoted xylem differentiation, whereas RsHB8-silenced plants exhibited a reduced taproot width and narrower xylem region. Xyloglucan endotransglucosylase/hydrolase 22 (RsXTH22) was identified as a target gene of RsHB8. LOB domain-containing protein 21 (RsLBD21) interacted with RsHB8, inhibited its transcriptional activity, and attenuated the RsHB8-mediated transcriptional activation of RsXTH22. Silencing RsLBD21 significantly increased taproot and xylem width, whereas silencing RsXTH22 resulted in the opposite phenotypes. Together, these findings establish a single-nucleus transcriptomic atlas of radish taproot development and provide insights into the transcriptional regulation of VC differentiation during taproot thickening.
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Isolation of human ESC-derived cardiac derivatives and embryonic heart cells for population and single-cell RNA-seq analysis.
The combination of population and single-cell RNA sequencing analysis using human embryonic stem cell (hESC) differentiation and developmental tissues is a powerful approach to elucidate an organ-specific cellular and molecular atlas in human embryogenesis. This protocol describes (1) cardiac-directed differentiation and isolation of hESC-derived cardiac derivatives with fluorescence-activated cell sorting, (2) isolation of human embryonic heart-derived single cardiac cells, and (3) construction of cDNA libraries with Smart-seq2. These allow for the preparation of human developmental samples for comprehensive transcriptional analysis. For complete details on the use and execution of this protocol, please refer to Sahara et al. (2019).
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Investigating PPT2's role in ovarian cancer prognosis and immunotherapy outcomes.
Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.
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Application of Computational Biology to Decode Brain Transcriptomes.
The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.
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The brain's dark transcriptome: Sequencing RNA in distal compartments of neurons and glia.
Transcriptomic approaches are powerful strategies to map the molecular diversity of cells in the brain. Single-cell genomic atlases have now been compiled for entire mammalian brains. However, complementary techniques are only just beginning to map the subcellular transcriptomes from distal cellular compartments. We review single-cell datasets alongside subtranscriptome data from the mammalian brain to explore the development of cellular and subcellular diversity. We discuss how single-cell RNA-seq misses transcripts localized away from cell bodies, which form the 'dark transcriptome' of the brain: a collection of subtranscriptomes in dendrites, axons, growth cones, synapses, and endfeet with important roles in brain development and function. Recent advances in subcellular transcriptome sequencing are beginning to reveal these elusive pools of RNA. We outline the success stories to date in uncovering the constituent subtranscriptomes of neurons and glia, as well as present the emerging toolkit that is accelerating the pace of subtranscriptome discovery.
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Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.
Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.
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FZD5 drives macrophage-mediated immunomodulation and predicts prognosis in glioma: evidence from single-cell sequencing.
BACKGROUND: Gliomas are highly malignant brain tumors characterized by an immunosuppressive microenvironment, which limits therapeutic efficacy and contributes to poor clinical outcomes. The WNT/β-catenin signaling pathway is critically involved in tumor progression, and FZD5, a key receptor within this pathway, may participate in immune regulation. However, its specific role and underlying mechanisms in glioma remain unclear. METHODS: RNA-seq and microarray datasets from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA), together with single-cell RNA sequencing (scRNA-seq) datasets from GEO, were comprehensively analyzed. The Seurat package was used to identify macrophage-related clusters and mitophagy-associated pathways. Cox and LASSO regression analyses, along with a prognostic nomogram, were applied to evaluate the prognostic significance of FZD5. Immune infiltration, functional enrichment, and immunotherapy response analyses were conducted, followed by validation using spatial transcriptomics, immunohistochemistry, and in vitro assays. RESULTS: In bulk glioma transcriptomes, FZD5 emerged as an independent predictor of poor prognosis. Crucially, single-cell and spatial analyses revealed that the biologically significant FZD5 signal originated predominantly within tumor-associated macrophages (TAMs), where it colocalized with the M2 marker CD163. Consistently, elevated FZD5 levels correlated with increased myeloid infiltration and an immunosuppressive tumor microenvironment. Functionally, macrophage-expressed FZD5 was associated with mitophagy-related programs and promoted an M2-skewed phenotype, thereby enhancing glioma cell proliferation, migration, and invasion via macrophage-glioma crosstalk. CONCLUSION: FZD5 is a TAM-enriched marker in glioma tissues and a potential regulator of macrophage-associated immunosuppressive programs, supporting its utility as a prognostic biomarker and a candidate target for microenvironment-oriented interventions in glioma.
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Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival.
BACKGROUND: Hepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival. METHODS: We combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients' tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University. RESULTS: We discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC. CONCLUSIONS: By integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.