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SNAI1 promotes epithelial-mesenchymal transition and maintains cancer stem cell-like properties in thymic epithelial tumors through the PIK3R2/p-EphA2 Axis.
BACKGROUND: Thymic epithelial tumors (TETs) are infrequent malignancies that arise from the anterior mediastinum. Therapeutic options for TETs, especially thymic carcinoma (TC), remain relatively constrained. This study aims to investigate the oncogenic hub gene and its underlying mechanisms in TETs, as well as to identify potential therapeutic targets. METHODS: Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were utilized to identify significant oncogenes using The Cancer Genome Atlas (TCGA) database. LASSO logistic regression analysis was performed to assess the association between hub genes and clinical parameters. The influence of the hub gene on promoting epithelial-mesenchymal transition (EMT), tumor progression, and regulating cancer stem cell-like properties was assessed both in vitro and in vivo. Single-cell RNA sequencing (scRNA-seq) was utilized to analyze the alterations in the tumor and its microenvironment following the administration of the hub gene's inhibitor. Multiplex immunohistochemistry (mIHC) was employed to validate the results. The potential mechanism was further elucidated through the utilization of Cleavage Under Targets and Tagmentation (CUT&Tag), RNA-sequencing, chromatin immunoprecipitation (ChIP), CUT&RUN, luciferase reporter assay, co-immunoprecipitation (Co-IP), mass spectrometry (MS) and phosphoproteomic assays. RESULTS: SNAI1 was identified as a hub transcription factor for TETs, and its positive correlation with the invasiveness of the disease was confirmed. Subsequent experiments revealed that the upregulation of SNAI1 augmented the migration, invasion, and EMT of TET cell lines. Furthermore, we observed that the overexpression of SNAI1 sustained cancer stem cell-like properties. ScRNA-seq demonstrated that the use of a SNAI1 inhibitor inhibited the transition of macrophages from M1 to M2 phenotype, a finding further validated by multiplex immunohistochemistry (mIHC). Phosphoinositide-3-kinase regulatory subunit 2 (PIK3R2) was identified as one of the downstream targets of SNAI1 through CUT&Tag and RNA-sequencing, a finding validated by ChIP-qPCR, CUT&RUN-qPCR, luciferase reporter and immunofluorescence assays. Co-IP, MS and phosphoproteomic assays further confirmed that PIK3R2 directly interacted with phosphorylated EphA2 (p-EphA2), facilitating downstream GSK3β/β-catenin signaling pathway. CONCLUSION: The tumorigenic role of SNAI1 through the PIK3R2/p-EphA2 axis was preliminarily validated in TETs. A potential therapeutic strategy for TETs may involve the inhibition of SNAI1.
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BrainSTEM: A single-cell multiresolution fetal brain atlas reveals transcriptomic fidelity of human midbrain cultures.
Protocols for deriving midbrain dopaminergic (mDA) neurons for Parkinson's disease (PD) modeling and therapy remain incompletely benchmarked against in vivo references. To establish transcriptomic standards, we generated an integrated human fetal whole-brain atlas and a midbrain subatlas. Whole-brain analysis revealed strong region-specific signatures, underscoring the need for global mapping before refined midbrain annotation. We implemented this two-tier strategy, BrainSTEM (Brain Single-cell Two tiEr Mapping), to systematically reassess published single-cell datasets of human midbrain culture models. BrainSTEM confirmed the presence of bona fide midbrain cell types ("on-target"), but also revealed substantial populations aligning with nonmidbrain regions ("off-target"), inflating reported mDA yields across protocols. This unbiased framework enables rigorous evaluation of differentiation outcomes, clarifies current limitations of midbrain-directed models, and provides a foundation for refining protocols toward more faithful in vitro systems for PD research and regenerative applications.
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A simple, scalable approach to building a cross-platform transcriptome atlas.
Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.
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Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens.
Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1-11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.
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Identification of cell types in a mouse brain single-cell atlas using low sampling coverage.
BACKGROUND: High throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. However, the efficient generation of such atlases will depend on sufficient sampling of diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. RESULTS: To examine the relationship between sampled cell numbers and transcriptional heterogeneity in the context of unbiased cell type classification, we explored the population structure of a publicly available 1.3 million cell dataset from E18.5 mouse brain and validated our findings in published data from adult mice. We propose a computational framework for inferring the saturation point of cluster discovery in a single-cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a "complexity index," which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether the detected biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells than the originally sampled, though technical saturation of rare populations such as Cajal-Retzius cells is not achieved. We additionally validated these findings with a recently published atlas of cell types across mouse organs and again find using subsampling that a much smaller number of cells recapitulates the cluster distinctions of the complete dataset. CONCLUSIONS: Together, these findings suggest that most of the biologically interpretable cell types from the 1.3 million cell database can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high "cellular coverage," cell atlas studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage and then further enriching for populations of interest. This strategy is ideal for scenarios where cost and time are limited, though extremely rare populations of interest (< 1%) may be identifiable only with much higher cell numbers.
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CellScope: high-performance cell atlas workflow with tree-structured representation.
Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels-such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research.
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SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.
It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.
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Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.
BACKGROUND: Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking. METHODS: This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings were then validated through the receiver operating characteristic (ROC) curve and Kaplan-Meier analysis, and were experimentally confirmed in GC samples by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Integrated analysis of TCGA and GEO databases, coupled with LASSO regression and RF algorithms, allowed us to identify 18 hub genes encoding differentially expressed secreted proteins in GC. The results of RT-PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan-Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. These findings were further confirmed by RT-PCR and ELISA, affirming the clinical utility of these four biomarkers. Additionally, CIBERSORT analysis indicated a potential correlation between the four biomarkers and the infiltration of B memory cells and Treg cells. CONCLUSION: This study unveiled four promising biomarkers present in the serum of patients with GC, which could serve as powerful indicators of GC and provide valuable insights for further research into GC pathogenesis.
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Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury.
An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.
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Comprehensive snRNA-Seq Datasets of Human and Mouse Podocytopathy Integrated with GWAS of Microalbuminuria.
Podocyte (POD) injury is a hallmark of glomerular diseases and a central cause of albuminuria and nephrotic syndrome. Despite the rapid advancements in single-cell sequencing technologies in recent years, POD capture remains limited, resulting in a lack of comprehensive single-cell datasets related to Podocytopathies (PCPs). In this study, we collected kidney tissues from patients with five types of PCP and established two mouse models of POD injury for single-nucleus RNA sequencing (sn-RNA seq). We successfully generated single-cell resolution kidney atlases of human and mouse PCPs and, in combination with microalbuminuria-associated genome wide association study (GWAS) dataset, identified potential therapeutic targets linked to POD injury. These findings provide valuable resources and a solid foundation for future research on PCPs, particularly on the molecular mechanisms underlying POD injury.