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A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. Analysis of scRNA-seq data routinely involves machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amount of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. To address this, we developed a new hybrid approach, deep unsupervised single-cell clustering (DUSC), which integrates feature generation based on a deep learning architecture by using a new technique to estimate the number of latent features, with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to a single-cell transcriptomics data set obtained from a triple-negative breast cancer tumor to identify potential cancer subclones accentuated by copy-number variation and investigate the role of clonal heterogeneity. Our method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.
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High-resolution single-cell RNA sequencing using canFam4 reveals novel immune subsets and checkpoint programs in healthy dogs.
INTRODUCTION: Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of immune heterogeneity. Although previous studies have mapped the single-cell transcriptomic atlases of peripheral leukocytes in healthy dogs, the identification and functional characterization of distinct immune subsets remain incomplete. METHODS: We constructed a single-cell atlas of peripheral leukocytes from six healthy small-breed dogs using the 10x Genomics platform and the updated canFam4 genome. RESULTS AND DISCUSSION: Analysis of 30,040 high-quality transcriptomes revealed 51 distinct immune subsets, including CD14 + CD33 + monocytes, XCR1 + CD1D + dendritic cells, CEACAM1 + CD24 + neutrophils, and IL32 + BATF + regulatory T cells, which were underrepresented in canFam3.1-based studies. Interferon-enriched CD14 + monocytes and CD4 + T subsets associated with myxomatous mitral valve disease were also identified. Functional enrichment analyses suggested that PDCD1 is associated with attenuated TCR signaling, whereas LAG3 was associated with malate metabolism pathways in CD4 + T cells and reduced TBX21 expression in CD8 + T cells linked to antiviral responses. CD274, which encodes PD-L1 was linked to IL-10 production in neutrophils, and CTLA4 represented an initial activation of double-negative T subsets. T cell exhaustion scores and proliferative fractions varied across cohorts, reflecting differences in environmental antigenic exposures. CONCLUSION: To our knowledge, this study represents the first comprehensive, gene-resolved single-cell analysis to reveal immunoregulatory checkpoint mechanisms underlying immune homeostasis in healthy dogs. Our dataset will serve as a valuable resource for future comparative and translational immunology research in dogs.
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A single-cell RNA sequencing atlas of circulating leukocytes from healthy and osteosarcoma affected dogs.
Translationally relevant animal models are essential for the successful translation of basic science findings into clinical medicine. While rodent models are widely accessible, there are numerous limitations that prevent the extrapolation of findings to human medicine. One approach to overcome these limitations is to use animal models that are genetically diverse and naturally develop disease. For example, pet dogs spontaneously develop diseases that recapitulate the natural progression seen in humans and live in similar environments alongside humans. Thus, dogs represent a useful animal model for many areas of research. Despite the value of the canine model, species specific reagent limitations have hampered in depth characterization of canine immune cells, which constrains the conclusions that can be drawn from canine immunotherapy studies. To address this need, we used single-cell RNA sequencing to characterize the heterogeneity of circulating leukocytes in healthy dogs (n = 7) and osteosarcoma (OS) affected dogs (n = 10). We present a cellular atlas of leukocytes in healthy dogs, then employ the dataset to investigate the impact of primary OS tumors on the transcriptome of circulating leukocytes. We identified 36 unique cell populations amongst dog circulating leukocytes, with a remarkable amount of heterogeneity in CD4 T cell subtypes. In our comparison of healthy dogs and dogs with OS, we identified relative increases in the abundances of polymorphonuclear (PMN-) and monocytic (M-) myeloid-derived suppressor cells (MDSCs), as well as aberrations in gene expression within myeloid cells. Overall, this study provides a detailed atlas of canine leukocytes and investigates how the presence of osteosarcoma alters the transcriptional profiles of circulating immune cells.
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A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast.
To examine global changes in breast heterogeneity across different states, we determined the single-cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/- tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post-menopausal women. Single-cell profiling of 34 treatment-naive primary tumors, including estrogen receptor (ER)+ , HER2+ , and triple-negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1+/- tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple-negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor-associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large-scale integration of patient samples provides a high-resolution map of cell diversity in normal and cancerous human breast.
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SPP1 + macrophages facilitate pancreatic cancer progression via ITGB6-mediated interactions: evidence from integrated multi-omics analysis and experimental validation.
Basement membranes (BMs) and tumor-associated macrophages (TAMs) are crucial stromal components in pancreatic cancer (PC), critically influencing disease progression. Bulk and single-cell RNA-seq (scRNA-seq) data were acquired from publicly available databases. Through integration of multiple machine learning algorithms, we developed and validated a BM-related subtype and prognostic signature in PC cohorts. The expression profiles of BM-related genes in PC were verified using experimental approaches. We further investigated the functional mechanisms of a core gene in PC progression. Additionally, we characterized the TAM landscape in PC, revealing distinct TAM subsets associated with tumor progression and their dynamic interactions with BM components. Based on BM-related gene expression profiles, PC samples were stratified into two distinct subtypes. Our integrated prognostic signature combining LASSO and survival-SVM algorithms demonstrated robust performance in predicting PC outcomes and clinical characteristics. LAMA3, ITGA3, and ITGB6 showed higher expression in PC specimens versus normal controls. Functional experiments confirmed that ITGB6 knockdown markedly suppressed PC progression. Through integrative analysis of multiple scRNA-seq datasets of PC, we established a single-cell landscape of TAMs and ductal cells, respectively. SPP1 + TAMs correlated with poor PC prognosis and facilitated tumor progression through ITGB6-mediated interactions. In this study, we established novel PC subtypes and constructed a prognostic signature based on BM-related genes. An atlas of TAMs was constructed in PC. SPP1 + macrophages drove pancreatic cancer progression via ITGB6-mediated interactions.
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A transcriptome atlas and interactive analysis platform for autoimmune disease.
With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell-cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available. Database URL: http://galaxy.ustc.edu.cn/IAAA.
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Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.
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A single cell transcriptome atlas of the developing zebrafish hindbrain.
Segmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at boundaries and segment centres, in part mediated by Fgf20 signalling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.
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Single-cell RNA sequencing atlas of peripheral blood mononuclear cells from subjects with coronary artery disease.
BACKGROUND: The landscape of specific peripheral circulating immune cell subsets at the single-cell level in the occurrence and development of coronary artery disease (CAD) remains poorly understood. METHODS: We conducted single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from subjects with CAD (n = 3), and controls (n = 3), as well as downstream analysis including cell- and gene-level approaches. This explored the characteristics of peripheral circulating immune cells between CADs and controls by means of Uniform manifold approximation and projection (UMAP), Monocle3 package, CellPhoneDB, and single-cell regulatory network inference and clustering (SCENIC). PBMCs were used as clinical samples for validating our findings by qRT-PCR. RESULTS: We identified 33 cell clusters among 67,447 cells, including monocytes, T cells, B cells, NK cells, and platelets. The significant difference in the abundance of the 33 clusters of cell type between CADs group and controls group was not found. The JUN was shared in cluster 0, 11,13, and 24 from differential expression genes analysis and SCENIC analysis in monocyte clusters between CAD and controls. Besides, JUN was validated to be significantly upregulated in the CAD group (p = 0.018) and may act as a potential diagnostic biomarker and independent predictor of CAD. CONCLUSIONS: Our study offered a detailed profiling of single-cell RNA sequencing of PBMCs from subjects with CADs and controls. These data provide a line of evidence that the JUN signaling pathway may be a potential diagnostic and therapeutic molecule target for CAD.
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A single-cell meta-analysis evidences transposable element dysregulation in sex-based differences in Parkinson's disease.
Transposable elements (TEs) (mobile genetic elements comprising ∼45% of the human genome) have recently emerged as potential contributors to Parkinson's disease (PD); however their role and sex-specific impact remain poorly understood. Here, we present the first integrative meta-analysis of TE expression across 4 substantia nigra single-nucleus RNA-seq datasets, comprising a total of 66 donors, generating a cell-type-resolved atlas of TE dysregulation in PD. We identified widespread TE activation across major brain cell types (i.e. neurons, astrocytes, oligodendrocytes and microglia), with marked upregulation of L1s in neurons and HERVs in oligodendrocytes. Sex-stratified analyses revealed distinct male- and female-biased TE signatures, indicating regulatory programs uniquely affected in each sex, including MIR elements in microglia and Alu subfamilies in neurons. Correlation and genomic proximity analyses also uncovered TE-gene associations linked to important PD pathways such as neuroinflammation or myelination. Collectively, our study positions TEs as potential sex-modulated contributors to PD pathology and also provides a public web resource (PATOSS) to explore PD-associated TE transcriptional deregulation.