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A prognostic model based on Scissor+ cancer associated fibroblasts identified from bulk and single cell RNA sequencing data in head and neck squamous cell carcinoma.
Head and neck squamous cell carcinoma (HNSCC) is one of the most lethal diseases in the world, which often recur after multimodality treatment approaches, leading to a poor prognosis. Fibroblasts, a heterogeneous component of the tumor microenvironment, can modulate numerous aspects of tumor biology and have been increasingly acknowledged in dictating the clinical outcome of patients with HNSCC. However, the subpopulation of fibroblasts that are related to the prognosis of HNSCC has not yet been fully explored. To do so, we combined a single-cell RNA sequencing (scRNA-seq) dataset and bulk RNA-sequencing dataset with clinical information, identifying the fibroblast population that are related to poor prognosis of HNSCC. We found these specific population of fibroblasts are less differentiated. In addition, to identify the prognostic signatures of HNSCC, bioinformatics analysis included least absolute shrinkage and selection operator (LASSO) analyses and univariate cox and were performed. We selected 12 prognosis-related genes for constructing a risk model using The Cancer Genome Atlas (TCGA). The AUC values and calibration plots of this model indicated good prognostic prediction efficacy. This model also was validated in two Gene Expression Omnibus (GEO) datasets. In conclusion, we constructed an optimal model that was derived from single cell RNA-seq and bulk RNA-seq to predict the survival probability of HNSCC patients. Among this model, AKR1C3 higher expression in cancer associated fibroblasts (CAFs) of HNSCC has been confirmed by preliminary experiments.
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Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing.
High-throughput single-cell RNA sequencing (scRNA-seq) is becoming a cornerstone of developmental research, providing unprecedented power in understanding dynamic processes. Here, we present a high-resolution scRNA-seq expression atlas of the Arabidopsis root composed of thousands of independently profiled cells. This atlas provides detailed spatiotemporal information, identifying defining expression features for all major cell types, including the scarce cells of the quiescent center. These reveal key developmental regulators and downstream genes that translate cell fate into distinctive cell shapes and functions. Developmental trajectories derived from pseudotime analysis depict a finely resolved cascade of cell progressions from the niche through differentiation that are supported by mirroring expression waves of highly interconnected transcription factors. This study demonstrates the power of applying scRNA-seq to plants and provides an unparalleled spatiotemporal perspective of root cell differentiation.
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Mast Cells Drive Ferroptosis in Gastric Tumors as Key Players in the Tumor Immune Microenvironment.
Ferroptosis, an iron-dependent form of oxidative cell death, plays a critical role in cancer progression and immune regulation. However, the functional connections of ferroptosis with specific immune cell types remain poorly defined, limiting the future possibilities to harness ferroptosis for cancer biology, diagnosis, and treatment. To address this knowledge gap, we conducted an integrated transcriptomic analysis to investigate ferroptosis-related immune dynamics in gastric cancer (GC). We utilized GC datasets from The Cancer Genome Atlas-stomach adenocarcinoma (n = 412) and the GSE66229 (n = 300) that were clustered into three GC immune subtypes based on single-sample Gene Set Enrichment Analysis scores of 29 immune gene sets. Bulk RNA-seq analysis revealed that the immune-inflamed subtype (HIS) of tumor samples in both GC datasets exhibited the highest ferroptosis enrichment and showed a positive correlation with activated mast cells and neutrophils. Given the regulatory role of mast cells in the tumor microenvironment (TME), particularly in recruiting neutrophils, we further examined their link to ferroptosis. In a fibroblast-mast cell coculture RNA-seq data (GSE223179), fibroblasts exhibited increased ferroptosis enrichment, supporting a mast cell-mediated influence. Single-cell RNA-seq data confirmed stronger interactions between mast cells and fibroblasts in GC compared to normal tissues. Specifically, they revealed a positive correlation between mast cell activity and ferroptosis enrichment in tumor-associated fibroblasts. In conclusion, these findings suggest that mast cells may promote ferroptosis in the TME through paracrine signaling, possibly via annexin and cyclophilin A. By uncovering this novel pathophysiological axis, our study reveals a previously unrecognized role of mast cells in regulating ferroptosis within the TME. The findings call for translational and experimental medical research and have potential implications for innovation toward GC diagnostics and therapeutics.
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Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis.
BACKGROUND: Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor. METHODS: We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications. RESULTS: Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment. CONCLUSION: In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development.
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A single-cell RNA sequencing atlas of the healthy canine lung: a foundation for comparative studies.
Single cell RNA sequencing (scRNA-seq) can be used to resolve the cellular and molecular heterogeneity within a tissue by identifying cell populations with an unprecedented granularity along with their transcriptional signatures. Yet, the single cell gene expression profiles of cell populations in the healthy canine lung tissue remain unexplored and such analysis could reveal novel cell populations or markers lacking in dogs and facilitate comparisons with lung diseases. Using fresh healthy lung biopsies from four dogs, we conducted droplet-based scRNA-seq on 26,278 cells. We characterized 46 transcriptionally distinct cell subpopulations across all lung tissue compartments including 23 immune, 13 mesenchymal, five epithelial and five endothelial cell subpopulations. Of note, we captured rare cells such as unconventional T cells or Schwann cells. Differential gene expression profiles identified specific markers across all cell subpopulations. Fibroblasts clusters exhibited a marked transcriptional heterogeneity, some of which might exert immune regulatory functions. Finally, the integration of canine lung cells with an annotated human lung atlas highlighted many similarities in gene expression profiles between species. This study thus provides an extensive molecular cell atlas of the healthy canine lung, expanding our knowledge of lung cell diversity in dogs, and providing the molecular foundation for investigating lung cell identities and functions in canine lung diseases. Besides, the occurrence of spontaneous lung diseases in pet dogs, with phenotypes closely resembling those in humans, may provide a relevant model for advancing research into human lung diseases.
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From cell atlases to mechanisms: bridging scRNA-seq discovery with in vivo genetics.
Single-cell RNA sequencing has uncovered vast cellular heterogeneity, yet the in vivo functions of most newly identified subpopulations remain unclear, risking an “atlasing valley of death”, in which descriptive catalogs remain untethered to mechanism. To address this gap, I propose a genetic Sequential Intersectional Recombinase Approach (SIRA) that pairs a cell-type-specific Dre driver with a subpopulation-enriched gene-driven rox-Stop-rox-CreER allele. SIRA restricts Cre activity to cells at the intersection of two genetic markers, allowing precise lineage tracing, targeted ablation, fate manipulation, and floxed-allele perturbation in defined subpopulations. By operationalizing intersectional genetics downstream of discovery-based omics, SIRA provides a practical path from atlas to mechanism in vivo.
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scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.
Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.
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Patch-seq: Advances and Biological Applications.
Multimodal analysis of gene-expression patterns, electrophysiological properties, and morphological phenotypes at the single-cell/single-nucleus level has been arduous because of the diversity and complexity of neurons. The emergence of Patch-sequencing (Patch-seq) directly links transcriptomics, morphology, and electrophysiology, taking neuroscience research to a multimodal era. In this review, we summarized the development of Patch-seq and recent applications in the cortex, hippocampus, and other nervous systems. Through generating multimodal cell type atlases, targeting specific cell populations, and correlating transcriptomic data with phenotypic information, Patch-seq has provided new insight into outstanding questions in neuroscience. We highlight the challenges and opportunities of Patch-seq in neuroscience and hope to shed new light on future neuroscience research.
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Single-cell transcriptome profiling of the vaginal wall in women with severe anterior vaginal prolapse.
Anterior vaginal prolapse (AVP) is the most common form of pelvic organ prolapse (POP) and has deleterious effects on women's health. Despite recent advances in AVP diagnosis and treatment, a cell atlas of the vaginal wall in AVP has not been constructed. Here, we employ single-cell RNA-seq to construct a transcriptomic atlas of 81,026 individual cells in the vaginal wall from AVP and control samples and identify 11 cell types. We reveal aberrant gene expression in diverse cell types in AVP. Extracellular matrix (ECM) dysregulation and immune reactions involvement are identified in both non-immune and immune cell types. In addition, we find that several transcription factors associated with ECM and immune regulation are activated in AVP. Furthermore, we reveal dysregulated cell-cell communication patterns in AVP. Taken together, this work provides a valuable resource for deciphering the cellular heterogeneity and the molecular mechanisms underlying severe AVP.
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Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma.
Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. To comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, we employed single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, and identified diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, in PDAC. We found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, we found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, our findings provide a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.