A single-cell RNA-seq dataset describing macrophages in NSCLC tumor and peritumor tissues.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Aitian Li, Huishang Wang, Lei Zhang, Qitai Zhao, Yang Yang, Yi Zhang, Li Yang
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引用次数: 0

Abstract

Examining tumor-associated macrophages in the immune microenvironment of non-small cell lung cancer (NSCLC) is essential for gaining an understanding of the genesis and development of NSCLC as well as for identifying key clinical therapeutic targets. Although previous studies have reported the diverse phenotypes and functions of macrophages in tumor tissues, thereby highlighting their significant role in the tumor microenvironment, the characteristic differences and correlations between tumor and peritumor tissue-derived macrophages that are necessary for an understanding of NSCLC progression remain unclear. Based on single-cell RNA sequencing, we generated a comprehensive dataset of transcriptomes from NSCLC tumor and peritumor tissues, thereby facilitating comprehensive analysis and providing significant insights. In summary, our dataset will serve as a valuable transcriptomic resource for further studies investigating NSCLC development.

描述 NSCLC 肿瘤和肿瘤周围组织中巨噬细胞的单细胞 RNA 序列数据集。
研究非小细胞肺癌(NSCLC)免疫微环境中的肿瘤相关巨噬细胞对于了解 NSCLC 的起源和发展以及确定关键的临床治疗靶点至关重要。尽管之前的研究已经报道了巨噬细胞在肿瘤组织中的不同表型和功能,从而强调了它们在肿瘤微环境中的重要作用,但肿瘤和肿瘤周围组织衍生的巨噬细胞之间的特征差异和相关性对于了解 NSCLC 的进展仍不清楚。基于单细胞 RNA 测序,我们生成了 NSCLC 肿瘤和肿瘤周围组织转录组的综合数据集,从而促进了综合分析并提供了重要见解。总之,我们的数据集将为进一步研究 NSCLC 的发展提供宝贵的转录组资源。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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