RESUMEN
Hepatic cystadenoma is a rare disease, accounting for about 5% of all cystic lesions, with a high tendency of malignant transformation. The preoperative diagnosis of cystadenoma is difficult, and some cystadenomas are easily misdiagnosed as hepatic cysts at first. Hepatic cyst is a relatively common liver disease, most of which are benign, but large hepatic cysts can lead to pressure on the bile duct, resulting in abnormal liver function. To better understand the difference between the microenvironment of cystadenomas and hepatic cysts, we performed single-nuclei RNA-sequencing on cystadenoma and hepatic cysts samples. In addition, we performed spatial transcriptome sequencing of hepatic cysts. Based on nucleus RNA-sequencing data, a total of seven major cell types were identified. Here we described the tumor microenvironment of cystadenomas and hepatic cysts, particularly the transcriptome signatures and regulators of immune cells and stromal cells. By inferring copy number variation, it was found that the malignant degree of hepatic stellate cells in cystadenoma was higher. Pseudotime trajectory analysis demonstrated dynamic transformation of hepatocytes in hepatic cysts and cystadenomas. Cystadenomas had higher immune infiltration than hepatic cysts, and T cells had a more complex regulatory mechanism in cystadenomas than hepatic cysts. Immunohistochemistry confirms a cystadenoma-specific T-cell immunoregulatory mechanism. These results provided a single-cell atlas of cystadenomas and hepatic cyst, revealed a more complex microenvironment in cystadenomas than in hepatic cysts, and provided new perspective for the molecular mechanisms of cystadenomas and hepatic cyst.
Asunto(s)
Cistoadenoma , Quistes , Neoplasias Hepáticas , Microambiente Tumoral , Humanos , Quistes/genética , Quistes/patología , Microambiente Tumoral/genética , Cistoadenoma/genética , Cistoadenoma/patología , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/metabolismo , Transcriptoma/genética , Análisis de Secuencia de ARN , Análisis de la Célula Individual/métodos , Hígado/patología , Hígado/metabolismo , Femenino , HepatopatíasRESUMEN
Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.