Data from The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment

肿瘤微环境 计算机科学 转录组 生物 计算生物学 癌症研究 肿瘤细胞 基因 遗传学 基因表达
作者
Jintong Shi,Xia Wei,Zhenzhen Xun,Xinyu Ding,Yao Liu,Lianxin Liu,Youqiong Ye
标识
DOI:10.1158/0008-5472.c.7181280
摘要

<div>Abstract<p>The tumor microenvironment (TME) represents a complex network in which tumor cells communicate not only with each other but also with stromal and immune cells. The intercellular interactions in the TME contribute to tumor initiation, progression, metastasis, and treatment outcome. Recent advances in spatial transcriptomics (ST) have revolutionized the molecular understanding of the TME at the spatial level. A comprehensive interactive analysis resource specifically designed for characterizing the spatial TME could facilitate further advances using ST. In this study, we collected 296 ST slides covering 19 cancer types and developed a computational pipeline to delineate the spatial structure along the malignant–boundary–nonmalignant axis. The pipeline identified differentially expressed genes and their functional enrichment, deconvoluted the cellular composition of the TME, reconstructed cell type–specific gene expression profiles at the sub-spot level, and performed cell–cell interaction analysis. Finally, the user-friendly database SpatialTME (<a href="http://www.spatialtme.yelab.site/" target="_blank">http://www.spatialtme.yelab.site/</a>) was constructed to provide search, visualization, and downloadable results. These detailed analyses are able to reveal the heterogeneous regulatory network of the spatial microenvironment and elucidate associations between spatial features and tumor development or response to therapy, offering a valuable resource to study the complex TME.</p>Significance:<p>SpatialTME provides spatial structure, cellular composition, expression, function, and cell–cell interaction information to enable investigations into the tumor microenvironment at the spatial level to advance understanding of cancer development and treatment.</p></div>
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
碧蓝访风完成签到,获得积分10
1秒前
EscX完成签到,获得积分10
1秒前
vulgar发布了新的文献求助10
2秒前
zc完成签到,获得积分10
2秒前
茶暖桉呀发布了新的文献求助10
2秒前
冯露瑶发布了新的文献求助10
3秒前
sw发布了新的文献求助10
4秒前
yangqi完成签到,获得积分10
4秒前
5秒前
5秒前
陶醉的明雪完成签到,获得积分10
6秒前
失眠的霸完成签到,获得积分10
6秒前
黄河学者完成签到,获得积分10
6秒前
张开心应助翕然采纳,获得10
9秒前
无23223发布了新的文献求助10
10秒前
cocodu应助眯眯眼的清炎采纳,获得100
10秒前
10秒前
11秒前
Wslby发布了新的文献求助10
13秒前
科研通AI6.2应助贾大大采纳,获得10
16秒前
Orange应助贾大大采纳,获得10
16秒前
16秒前
111发布了新的文献求助10
17秒前
好好学习完成签到,获得积分0
18秒前
不嘻嘻完成签到,获得积分10
18秒前
洁净的冬日完成签到,获得积分10
20秒前
20秒前
自觉的K发布了新的文献求助10
21秒前
21秒前
21秒前
ASH应助邢夏之采纳,获得10
21秒前
zzf完成签到,获得积分20
23秒前
23秒前
桐桐应助凡迪亚比采纳,获得10
24秒前
liuyc完成签到,获得积分10
25秒前
Ava应助三无采纳,获得10
25秒前
lx发布了新的文献求助10
26秒前
26秒前
dangdang完成签到,获得积分10
26秒前
Verne完成签到,获得积分10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Management and the Arts 310
Teaching Social and Emotional Learning in Physical Education 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7635976
求助须知:如何正确求助?哪些是违规求助? 9209919
关于积分的说明 19753945
捐赠科研通 7203733
什么是DOI,文献DOI怎么找? 3275343
关于科研通互助平台的介绍 2437151
邀请新用户注册赠送积分活动 2272446