使用Bio-Rad ddSEQ单细胞3'RNA-Seq技术剖析卵巢癌模型中的肿瘤微环境与细胞适应

Ovarian cancer remains one of the most lethal gynecologic malignancies due to its complex biology and the challenges associated with early detection and effective treatment. Advances in single-cell RNA sequencing technologies have enabled researchers to dissect cellular heterogeneity within tumors and gain deeper insight into the dynamic interactions between cancer cells and their microenvironment. By leveraging innovative single-cell analysis tools, analysis of gene expression profiles at single-cell resolution can be performed across various cancer models, providing valuable information on tumor evolution, therapeutic response, and mechanisms driving tumorigenesis.Single-cell and single-nucleus RNA sequencing were performed using the Bio-Rad ddSEQ 3' Single-Cell RNA-Seq Kit on multiple cancer models-including a primary high-grade serous ovarian tumor, a patient-derived cancer cell line (PDCC), and a patient-derived xenograft (PDX)-all originating from the same patient. The objective of this study is to investigate the role of the tumor microenvironment in modulating gene expression patterns that support the persistence and adaptation of cancer cells in various patient-derived models. By comparing single-cell transcriptomic profiles across these models, we aim to identify key regulatory pathways and microenvironmental signals that influence cellular behavior. These insights may advance our understanding of tumor cell adaptation mechanisms and facilitate the development of more physiologically relevant models for cancer research. Integration of single-cell data from various cancer models showed concordance of distinct clusters suggesting changes in gene expression specific to the cancer model utilized. We identified cancer cells as well as fibroblasts, endothelial cells, T and B cells, and myeloid cells in the primary tumor sample. Additionally, enrichment of cancer cells expressing increased levels of PHGDH and PSAT1 , genes associated in serine metabolism pathway that is crucial in tumorigenesis was observed in PDCC samples - data which were previously confirmed with bulk RNA-Seq. The Bio-Rad ddSEQ 3' RNA-Seq kit enables robust analysis of various in vitro and in vivo cancer models due to the capability of running many samples simultaneously, generating high-quality single-cell data. The observed concordance of distinct expression profiles-particularly the upregulation of serine metabolism genes in enriched cancer cell populations-highlights the power of single-cell approaches to uncover both cellular diversity and model-specific molecular features. These findings underscore the value of high-throughput single-cell technologies for advancing our understanding of ovarian cancer biology, with implications for improving cancer models and identifying potential therapeutic targets.