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The immune cell atlas of human neuroblastoma.

ABSTRACT: Understanding the complete immune cell composition of human neuroblastoma (NB) is crucial for the development of immunotherapeutics. Here, we perform single-cell RNA sequencing (scRNA-seq) on 19 human NB samples coupled with multiplex immunohistochemistry, survival analysis, and comparison with normal fetal adrenal gland data. We provide a comprehensive immune cell landscape and characterize cell-state changes from normal tissue to NB. Our analysis reveals 27 immune cell subtypes, including distinct subpopulations of myeloid, NK, B, and T cells. Several different cell types demonstrate a survival benefit. In contrast to adult cancers and previous NB studies, we show an increase in inflammatory monocyte cell state when contrasting normal and tumor tissue, while no differences in cytotoxicity and exhaustion score for T cells, nor in Treg activity, are observed. Our receptor-ligand interaction analysis reveals a highly complex interactive network of the NB microenvironment from which we highlight several interactions that we suggest for future therapeutic studies.

SUBMITTER: Verhoeven BM

PROVIDER: S-EPMC9245004 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Our current knowledge of the different immune cells in neuroblastoma is based on in vitro and in vivo studies mainly focusing on a single cell type. Importantly, different studies have conveyed conflicting results. Moreover, a comprehensive immune cell overview at the single-cell level is still missing and understanding the complete immune cell composition of neuroblastoma will be crucial for the development of novel immunotherapeutics against the disease. In this study, we performed single-cell RNA-sequencing on nineteen human neuroblastoma samples coupled with multiplex immunohistochemistry and survival analysis using additional datasets to provide a comprehensive cellular and molecular immune cell landscape of human neuroblastoma. Further, we contrasted our data with single-cell RNA-sequencing data from normal fetal adrenal gland to characterize cell-state changes from normal tissue to cancerous neuroblastoma. Our analysis revealed 27 immune cell subtypes including distinct subpopulations of myeloid, NK, B and T cells not identified in neuroblastoma before. Several immune cell subtypes demonstrated a survival benefit such as inflammatory monocytes, tumor associated macrophages, various T cell populations, and Active NK cells. Furthermore, in contrast to adult cancers and previous neuroblastoma studies, we demonstrated an increase in inflammatory monocyte cell-state when contrasting normal and tumor tissue, while we do not observe differences in cytotoxicity and exhaustion score for cytotoxic T cells, nor in Treg activity. Finally, we performed a systemic receptor-ligand interaction analysis between tumor, stroma and immune cells, where we showed the neuroblastoma tumor microenvironment is highly complex and strongly correlated to survival. In addition, we highlighted several interactions that we suggest to be tested in future studies as a therapeutic option in human neuroblastoma. Taken together, our study significantly adds to the in depth understanding of the immune cell landscape, the complexity of the tumor microenvironment and it provides a resource for the development of novel immunotherapeutics for neuroblastoma.

Project description:The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine 'human liver' scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.

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The immune cell atlas of human neuroblastoma.

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