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Single-cell sequencing reveals the potential oncogenic expression atlas of human iPSC-derived cardiomyocytes.
Human induced pluripotent stem cells (iPSCs) are important source for regenerative medicine. However, the links between pluripotency and oncogenic transformation raise safety issues. To understand the characteristics of iPSC-derived cells at single-cell resolution, we directly reprogrammed two human iPSC lines into cardiomyocytes and collected cells from four time points during cardiac differentiation for single-cell sequencing. We captured 32,365 cells and identified five molecularly distinct clusters that aligned well with our reconstructed differentiation trajectory. We discovered a set of dynamic expression events related to the upregulation of oncogenes and the decreasing expression of tumor suppressor genes during cardiac differentiation, which were similar to the gain-of-function and loss-of-function patterns during oncogenesis. In practice, we characterized the dynamic expression of the TP53 and Yamanaka factor genes (OCT4, SOX2, KLF4 and MYC), which were widely used for human iPSCs lines generation; and revealed the co-occurrence of MYC overexpression and TP53 silencing in some of human iPSC-derived TNNT2+ cardiomyocytes. In summary, our oncogenic expression atlas is valuable for human iPSCs application and the single-cell resolution highlights the clues potentially associated with the carcinogenic risk of human iPSC-derived cells.
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Dissecting the interplay between dedicator of cytokinesis 2 and neutrophil extracellular traps in sculpting the immune landscape and clinical outcome of melanoma using a single-cell atlas.
Melanoma is an aggressive skin cancer with a rising incidence, posing clinical challenges because of limited prognostic tools and therapeutic resistance. Over recent years, neutrophil extracellular traps (NETs) have gained increasing attention in cancer biology for their role in tumor progression and immune modulation across various malignancies. The role of NETs in the progression of melanoma remains unclear. This study aimed to construct a NET-related prognostic model and elucidate the functional role of the key gene dedicator of cytokinesis 2 ( DOCK2 ) in the pathogenesis of melanoma. We integrated bulk RNA sequencing data from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and the Gene Expression Omnibus, along with single-cell RNA sequencing data. A set of NET-related differentially expressed genes (NETRDEGs) was identified and proven to be enriched in immune-related pathways. A prognostic risk model based on NETRDEGs accurately predicted 1-, 3-, and 5-year survival (area under the curve > 0.7), with high-risk patients having worse outcomes. DOCK2 was identified as an overexpressed biomarker associated with a favorable prognosis and drug sensitivity. Low DOCK2 expression correlated with a higher tumor mutation burden and increased drug resistance. At the single-cell level, NETRG-associated transcriptional enrichment was prominent in macrophages; we characterized this heterogeneity and identified five novel subclusters. Analysis revealed a critical link between NETs and melanoma, identified a 235-gene NET signature, and established DOCK2 as a prognostic marker associated with the immune microenvironment. Furthermore, the expression of DOCK2 was correlated with the immune microenvironment and drug sensitivity, suggesting that targeting NET pathways could provide novel treatment strategies for melanoma.
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Decoding Alzheimer's Disease One Cell Class at a Time.
Multimodal imaging-based on single-cell genomics and spatial transcriptomics has shed new light on the taxonomy of genetically defined cell clusters in the mammalian brain. While transcriptomic approaches have revolutionized our ability to classify brain cells, their true value emerges when they are interpreted in conjunction with anatomical, physiological, and translational frameworks. Accordingly, significant progress has been made to elucidate relationships between gene expression, electrical and morphological properties of some of these clusters. This rapidly growing body of work shows not only that the cell cluster composition varies across brain regions but also evolves over time and changes during the progression of disease states like Alzheimer's disease. Given this complexity, integrating transcriptomic, structural, and functional data is now becoming essential for drawing meaningful comparisons across studies. In this review, we summarize these findings and discuss how this knowledge base is shifting towards more integrative approaches, quickly challenging current ideas regarding the genetic, molecular, and cellular underpinnings of Alzheimer's disease.
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Using single-cell technologies to map the human immune system - implications for nephrology.
Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.
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The expression characteristics of transmembrane protein genes in pancreatic ductal adenocarcinoma through comprehensive analysis of bulk and single-cell RNA sequence.
BACKGROUND: Transmembrane (TMEM) protein genes are a class of proteins that spans membranes and function to many physiological processes. However, there is very little known about TMEM gene expression, especially in cancer tissue. Using single-cell and bulk RNA sequence may facilitate the understanding of this poorly characterized protein genes in PDAC. METHODS: We selected the TMEM family genes through the Human Protein Atlas and characterized their expression by single-cell and bulk transcriptomic datasets. Identification of the key TMEM genes was performed through three machine learning algorithms: LASSO, SVM-RFE and RF-SRC. Then, we established TMEM gene riskscore and estimate its implication in predicting survival and response to systematic therapy. Additionally, we explored the difference and impact of TMEM gene expression in PDAC through immunohistochemistry and cell line research. RESULTS: 5 key TMEM genes (ANO1, TMEM59, TMEM204, TMEM205, TMEM92) were selected based on the single-cell analysis and machine learning survival outcomes. Patients stratified into the high and low-risk groups based on TMEM riskscore, were observed with distinct overall survival in internal and external datasets. Moreover, through bulk RNA-sequence and immunohistochemical staining we verified the protein expression of TMEM genes in PDAC and revealed TMEM92 as an essential regulator of pancreatic cancer cell proliferation, migration, and invasion. CONCLUSION: Our study on TMEM gene expression and behavior in PDAC has revealed unique characteristics, offering potential for precise therapeutic approaches. Insights into molecular mechanisms expand understanding of PDAC complexity and TMEM gene roles. Such knowledge may inform targeted therapy development, benefiting patients.
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Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer.
A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.
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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.
Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.
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Unraveling intratumoral complexity in metastatic dermatofibrosarcoma protuberans through single-cell RNA sequencing analysis.
Dermatofibrosarcoma protuberans (DFSP) stands as a rare and locally aggressive soft tissue tumor, characterized by intricated molecular alterations. The imperative to unravel the complexities of intratumor heterogeneity underscores effective clinical management. Herein, we harnessed single-cell RNA sequencing (scRNA-seq) to conduct a comprehensive analysis encompassing samples from primary sites, satellite foci, and lymph node metastases. Rigorous preprocessing of raw scRNA-seq data ensued, and employing t-distributed stochastic neighbor embedding (tSNE) analysis, we unveiled seven major cell populations and fifteen distinct subpopulations. Malignant cell subpopulations were delineated using infercnv for copy number variation calculations. Functional and metabolic variations of diverse malignant cell populations across samples were deciphered utilizing GSVA and the scMetabolism R packages. Additionally, the exploration of differentiation trajectories within diverse fibroblast subpopulations was orchestrated through pseudotime trajectory analyses employing CytoTRACE and Monocle2, and further bolstered by GO analyses to elucidate the functional disparities across distinct differentiation states. In parallel, we segmented the cellular components of the immune microenvironment and verified the presence of SPP1+ macrophage, which constituted the major constituent in lymph node metastases. Remarkably, the CellChat facilitated a comprehensive intercellular communication analysis. This study culminates in an all-encompassing single-cell transcriptome atlas, propounding novel insights into the multifaceted nature of intratumor heterogeneity and fundamental molecular mechanisms propelling metastatic DFSP.
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Dissection of cellular and molecular mechanisms of aristolochic acid-induced hepatotoxicity via single-cell transcriptomics.
BACKGROUND: Aristolochic acids (AAs), a class of carcinogenic and mutagenic natural products from Aristolochia and Asarum plants, are well-known to be responsible for inducing nephrotoxicity and urothelial carcinoma. Recently, accumulating evidence suggests that exposure to AAs could also induce hepatotoxicity and even hepatocellular carcinoma, though the mechanisms are poorly defined. METHODS: Here, we aimed to dissect the underlying cellular and molecular mechanisms of aristolochic acid I (AAI)-induced hepatotoxicity by using advanced single-cell RNA sequencing (scRNA-seq) and proteomics techniques. We established the first single-cell atlas of mouse livers in response to AAI. RESULTS: In hepatocytes, our results indicated that AAI activated NF-κB and STAT3 signaling pathways, which may contribute to the inflammatory response and apoptosis. In liver sinusoidal endothelial cells (LSECs), AAI activated multiple oxidative stress and inflammatory associated signaling pathways and induced apoptosis. Importantly, AAI induced infiltration of cytotoxic T cells and activation of proinflammatory macrophage and neutrophil cells in the liver to produce inflammatory cytokines to aggravate inflammation. CONCLUSIONS: Collectively, our study provides novel knowledge of AAs-induced molecular characteristics of hepatotoxicity at a single-cell level and suggests future treatment options for AAs associated hepatotoxicity.
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Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV.
The role of RNA modifications in biological processes has been the focus of an increasing number of studies in the last few years and is known nowadays as epitranscriptomics. Among others, N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have been described on mRNA molecules and may have a role in modulating cellular processes. Epitranscriptomics is thus a new layer of regulation that must be considered in addition to transcriptomic analyses, as it can also be altered or modulated by exposure to any chemical or biological agent, including viral infections. Here, we present a workflow that allows analysis of the joint cellular and viral epitranscriptomic landscape of the m6A and m5C marks simultaneously, in cells infected or not with the human immunodeficiency virus (HIV). Upon mRNA isolation and fragmentation from HIV- infected and non-infected cells, we used two different procedures: MeRIP-Seq, an RNA immunoprecipitation-based technique, to enrich for RNA fragments containing the m6A mark and BS-Seq, a bisulfite conversion-based technique, to identify the m5C mark at a single nucleotide resolution. Upon methylation-specific capture, RNA libraries are prepared for high-throughput sequencing. We also developed a dedicated bioinformatics pipeline to identify differentially methylated (DM) transcripts independently from their basal expression profile. Overall, the methodology allows exploration of multiple epitranscriptomic marks simultaneously and provides an atlas of DM transcripts upon viral infection or any other cell perturbation. This approach offers new opportunities to identify novel players and novel mechanisms of cell response, such as cellular factors promoting or restricting viral replication.