Spatially resolved transcriptomics of benign and malignant peripheral nerve sheath tumors

良性和恶性周围神经鞘瘤的空间分辨转录组学

阅读:1

Abstract

BACKGROUND: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates the diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single-cell sequencing data has shown dissimilar differentiation states of tumor cells. Here, we aimed to determine the spatial and biological heterogeneity of PNSTs. METHODS: We performed spatial transcriptomics on formalin-fixed paraffin-embedded diseased peripheral nerve tissue. We used spatial clustering and weighted correlation network analysis to construct niche-similarity networks and gene expression modules. We determined differential expression in primary pathologies, analyzed pathways to investigate the biological significance of identified meta-signatures, integrated the transcriptional data with histological features and existing single-cell data, and validated expression data by immunohistochemistry. RESULTS: We identified distinct transcriptional signatures differentiating PNSTs. Immune cell infiltration, APOD, and perineurial fibroblast marker expression highlighted the neurofibroma component of hybrid PNSTs (HPNSTs). While APOD was evenly expressed in neurofibromatous tumor tissue in both, HPNST and pure neurofibromas, perineurial fibroblast markers were evenly expressed in HPNST, but restricted to the periphery in plexiform neurofibromas. Furthermore, we provide a spatial cellular differentiation map for MPNST, locating Schwann cell precursor and neural crest-like cells as well as those with mesenchymal transition. CONCLUSIONS: This pilot study shows that applying spatial transcriptomics to PNSTs provides important insight into their biology. It helps establish new markers and provides spatial information about the cellular composition and distribution of cellular differentiation states. By integrating morphological and high-dimensional molecular data it can improve PNSTs classification in the future.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。