Dataset Information

Single-cell transcriptomic analysis in a mouse model deciphers cell transition states in the multistep development of esophageal cancer.

ABSTRACT: Esophageal squamous cell carcinoma (ESCC) is prevalent in some geographical regions of the world. ESCC development presents a multistep pathogenic process from inflammation to invasive cancer; however, what is critical in these processes and how they evolve is largely unknown, obstructing early diagnosis and effective treatment. Here, we create a mouse model mimicking human ESCC development and construct a single-cell ESCC developmental atlas. We identify a set of key transitional signatures associated with oncogenic evolution of epithelial cells and depict the landmark dynamic tumorigenic trajectories. An early downregulation of CD8⁺ response against the initial tissue damage accompanied by the transition of immune response from type 1 to type 3 results in accumulation and activation of macrophages and neutrophils, which may create a chronic inflammatory environment that promotes carcinogen-transformed epithelial cell survival and proliferation. These findings shed light on how ESCC is initiated and developed.

SUBMITTER: Yao J

PROVIDER: S-EPMC7381637 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Single-cell transcriptomic analysis in a mouse model deciphers cell transition states in the multistep development of esophageal cancer.

Yao Jiacheng J Cui Qionghua Q Fan Wenyi W Ma Yuling Y Chen Yamei Y Liu Tianyuan T Zhang Xiannian X Xi Yiyi Y Wang Chengcheng C Peng Linna L Luo Yingying Y Lin Ai A Guo Wenjia W Lin Lin L Lin Yuan Y Tan Wen W Lin Dongxin D Wu Chen C Wang Jianbin J

Nature communications 20200724 1

Esophageal squamous cell carcinoma (ESCC) is prevalent in some geographical regions of the world. ESCC development presents a multistep pathogenic process from inflammation to invasive cancer; however, what is critical in these processes and how they evolve is largely unknown, obstructing early diagnosis and effective treatment. Here, we create a mouse model mimicking human ESCC development and construct a single-cell ESCC developmental atlas. We identify a set of key transitional signatures ass ...[more]

PMID: 32709844

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Project description:Background and aimsThe early diagnosis and intervention of oesophageal squamous cell carcinoma (ESCC) are particularly important because of the lack of effective therapies and poor prognosis. Comprehensive research on early ESCC at the single-cell level is rare due to the need for fresh and high-quality specimens obtained from ESD. This study aims to systematically describe the cellular atlas of human intramucosal ESCC.MethodsFive paired samples of intramucosal ESCC, para-ESCC oesophageal tissues from endoscopically resected specimens and peripheral blood mononuclear cells were adopted for scRNA-seq analysis. Computational pipeline scMetabolism was applied to quantify the metabolic diversity of single cells.ResultsA total of 164 715 cells were profiled. Epithelial cells exhibited high intra-tumoural heterogeneity and two evolutionary trajectories during ESCC tumorigenesis initiated from proliferative cells, and then through an intermediate state, to two different terminal states of normally differentiated epithelial cells or malignant cells, respectively. The abundance of CD8+ TEX s, Tregs and PD1+ CD4+ T cells suggested an exhausted and suppressive immune microenvironment. Several genes in immune cells, such as CXCL13, CXCR5 and PADI4, were identified as new biomarkers for poor prognosis. A new subcluster of malignant cells associated with metastasis and angiogenesis that appeared at an early stage compared with progressive ESCC was also identified in this study. Intercellular interaction analysis based on ligand-receptor pairs revealed the subcluster of malignant cells interacting with CAFs via the MDK-NCL pathway, which was verified by cell proliferation assay and IHC. This indicates that the interaction may be an important hallmark in the early change of tumour microenvironment and serves as a sign of CAF activation to stimulate downstream pathways for facilitating tumour invasion.ConclusionThis study demonstrates the changes of cell subsets and transcriptional levels in human intramucosal ESCC, which may provide unique insights into the development of novel biomarkers and potential intervention strategies.

Project description:Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery. David McKellar et al. integrate single-cell and -nuclear transcriptomic analyses of mouse skeletal muscle in homeostatic conditions or following injury. The resulting transcriptomic model of myogenesis identified rare, transitional states and cell subtypes that are poorly represented in individual datasets, providing a valuable resource for the field.

Dataset Information

Single-cell transcriptomic analysis in a mouse model deciphers cell transition states in the multistep development of esophageal cancer.

Publications

Single-cell transcriptomic analysis in a mouse model deciphers cell transition states in the multistep development of esophageal cancer.

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