Background
Despite extensive research, the papillary thyroid carcinoma (PTC) ecosystem is poorly characterized and, in particular, locoregional progression. Available evidence supports that single-cell transcriptome sequencing (Sc-RNA seq) can dissect tumor ecosystems.
Conclusions
Our data provide new insights into the PTC ecosystem and highlight the differences in ecosystems in PTC progression, which updates our understanding of PTC biology and will improve individualized patient treatment.
Methods
Tissue samples from one PTC patient, including matched primary tumor (Ca), lymph node (LN) metastasis, and paracancerous tissue (PCa), were subjected to Sc-RNA seq with 10×Genomics. Dual-label immunofluorescence and immunohistochemistry were used to confirm the existence of cell subtypes in a separate cohort.
Results
11,805 cell transcriptomes were profiled, cell landscapes of PTC were composed of malignant follicular epithelial cells (MFECs), CD8+ and CD4+ T cells, B cells, vascular endothelial cells, fibroblasts and cancer-associated fibroblasts (CAFs). Between Ca and LN ecosystems, the proportions of MFEC and interstitial cells were similar, less than 1/25(229/6,694, 361/3,895), while the proportion of normal follicular epithelial cells (NFECs) and interstitial cells was > 2 in PCa (455/171). NFECs in PCa formed a separate cluster, while MFECs in Ca and LN exhibited a profound transcriptional overlap, and the interstitial cells among these samples had an overall concordance in their identity and representation, albeit with some distinctions in terms of the cell percentage per subset. A fraction of the B cell subpopulation in Ca expressed inhibitory receptors, while their respective ligand genes were clearly transcribed in T cell and malignant epithelial cell clusters, while some CD8+ T cells in both Ca and LN produced high levels of inhibitory receptors whose respective ligands were overexpressed in some CD4+ T cells. Three CAF subtypes in Ca and LN were identified, which may be due to mutual transitions. Conclusions: Our data provide new insights into the PTC ecosystem and highlight the differences in ecosystems in PTC progression, which updates our understanding of PTC biology and will improve individualized patient treatment.