Endoscopic ultrasound · 2026
Establishing a single-cell RNA sequencing workflow using EUS-guided fine-needle aspiration for pancreatic ductal adenocarcinoma.
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Zhang Yao, He Xiangyi, Lu Xinchen, Liu Zonghao, Gong Tingting, Zhang Tianyu, Liu Lei, Zhang Ling, Zhang Minmin, Zhou Chunhua, Zou Duowu
Аннотация
BACKGROUND AND OBJECTIVES: No unified consensus exists regarding standardized protocols for single-cell RNA sequencing (scRNA-seq) analysis of samples obtained via EUS-guided fine-needle aspiration (EUS-FNA). This study aimed to evaluate the feasibility of applying scRNA-seq to EUS-FNA-derived pancreatic ductal adenocarcinoma (PDAC) samples and to compare their quality metrics and cellular composition with those of surgical resection-derived samples.
METHODS: PDAC samples obtained via EUS-FNA were subjected to scRNA-seq using the 10× Genomics platform. These data were integrated with publicly available PDAC scRNA-seq datasets derived from EUS-FNA and surgical resection samples. Quality-control metrics, doublet estimates, ambient RNA contamination scores, and cell-type composition were compared between sampling methods.
RESULTS: Compared with surgical resection-derived samples, EUS-FNA-derived samples showed lower transcript abundance and lower mitochondrial transcript percentages, while exhibiting comparable detected gene numbers, estimated doublet proportions, and ambient RNA contamination scores. These findings suggest that EUS-FNA-derived samples can generate analyzable single-cell transcriptomic profiles under the workflow used in this study. Differences in recovered cell-type composition were observed between sampling methods: EUS-FNA-derived samples showed relatively higher proportions of immune cells, whereas surgical resection-derived samples showed relatively higher recovery of several nonimmune cell populations. These differences may reflect both biological characteristics and sampling-related technical effects.
CONCLUSIONS: This study supports the feasibility of scRNA-seq analysis using EUS-FNA-derived PDAC samples and suggests that EUS-FNA and surgical resection samples may provide complementary information. Further validation in larger, preferably paired prospective cohorts is warranted.
METHODS: PDAC samples obtained via EUS-FNA were subjected to scRNA-seq using the 10× Genomics platform. These data were integrated with publicly available PDAC scRNA-seq datasets derived from EUS-FNA and surgical resection samples. Quality-control metrics, doublet estimates, ambient RNA contamination scores, and cell-type composition were compared between sampling methods.
RESULTS: Compared with surgical resection-derived samples, EUS-FNA-derived samples showed lower transcript abundance and lower mitochondrial transcript percentages, while exhibiting comparable detected gene numbers, estimated doublet proportions, and ambient RNA contamination scores. These findings suggest that EUS-FNA-derived samples can generate analyzable single-cell transcriptomic profiles under the workflow used in this study. Differences in recovered cell-type composition were observed between sampling methods: EUS-FNA-derived samples showed relatively higher proportions of immune cells, whereas surgical resection-derived samples showed relatively higher recovery of several nonimmune cell populations. These differences may reflect both biological characteristics and sampling-related technical effects.
CONCLUSIONS: This study supports the feasibility of scRNA-seq analysis using EUS-FNA-derived PDAC samples and suggests that EUS-FNA and surgical resection samples may provide complementary information. Further validation in larger, preferably paired prospective cohorts is warranted.