Single-cell omics in inflammatory bowel disease: recent insights and future clinical applications

组学 疾病 细胞 间质细胞 炎症性肠病 溃疡性结肠炎 生物信息学 炎症 医学 计算生物学 炎症性肠病 结肠炎 电池类型 生物 免疫学 病理 遗传学
作者
Victòria Gudiño,Raquel Bartolomé-Casado,Azucena Salas
出处
期刊:Gut [BMJ]
卷期号:74 (8): 1335-1345 被引量:34
标识
DOI:10.1136/gutjnl-2024-334165
摘要

Inflammatory bowel diseases (IBDs), which include ulcerative colitis (UC) and Crohn's disease (CD), are chronic conditions characterised by inflammation of the intestinal tract. Alterations in virtually all intestinal cell types, including immune, epithelial and stromal cells, have been described in these diseases. The study of IBD has historically relied on bulk transcriptomics, but this method averages signals across diverse cell types, limiting insights. Single-cell omic technologies overcome the intrinsic limitations of bulk analysis and reveal the complexity of multicellular tissues at a cell-by-cell resolution. Within healthy and inflamed intestinal tissues, single-cell omics, particularly single-cell RNA sequencing, have contributed to uncovering novel cell types and cell functions linked to disease activity or the development of complications. Collectively, these results help identify therapeutic targets in difficult-to-treat complications such as fibrostenosis, creeping fat accumulation, perianal fistulae or inflammation of the pouch. More recently, single-cell omics have gradually been adopted in studies to understand therapeutic responses, identify mechanisms of drug failure and potentially develop predictors with clinical utility. Although these are early days, such studies lay the groundwork for the implementation in clinical practice of new technologies in diagnostics, monitoring and prediction of disease prognosis. With this review, we aim to provide a comprehensive survey of the studies that have applied single-cell omics to the study of UC or CD, and offer our perspective on the main findings these studies contribute. Finally, we discuss the limitations and potential benefits that the integration of single-cell omics into clinical practice and drug development could offer.
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
qqsaosa发布了新的文献求助10
1秒前
2秒前
2580852qwe发布了新的文献求助10
2秒前
许是城陌完成签到,获得积分10
3秒前
ANG完成签到,获得积分10
3秒前
科研通AI6.2应助投石问路采纳,获得10
3秒前
bzlish发布了新的文献求助10
4秒前
w1发布了新的文献求助10
5秒前
wenwei发布了新的文献求助10
5秒前
5秒前
7秒前
7秒前
8秒前
10秒前
朴素豪发布了新的文献求助10
11秒前
跳跃的语风完成签到,获得积分10
11秒前
小橙子发布了新的文献求助10
11秒前
11秒前
Ellalala完成签到 ,获得积分10
12秒前
传奇3应助2580852qwe采纳,获得10
13秒前
orixero应助常常嘻嘻采纳,获得10
13秒前
cxy0714发布了新的文献求助10
14秒前
ANG发布了新的文献求助10
16秒前
长雁发布了新的文献求助10
17秒前
活泼平凡完成签到,获得积分10
17秒前
18秒前
w1完成签到,获得积分10
18秒前
20秒前
土豆丝完成签到,获得积分10
21秒前
23秒前
xlTAN发布了新的文献求助10
23秒前
有趣的银完成签到,获得积分10
24秒前
常常嘻嘻发布了新的文献求助10
24秒前
27秒前
v0id应助qq采纳,获得10
28秒前
vchen0621发布了新的文献求助10
28秒前
汉堡包应助奋斗的灵松采纳,获得10
29秒前
情怀应助小橙子采纳,获得10
29秒前
123完成签到,获得积分20
30秒前
lucky发布了新的文献求助10
31秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
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 7584626
求助须知:如何正确求助?哪些是违规求助? 9163194
关于积分的说明 19610092
捐赠科研通 7166370
什么是DOI,文献DOI怎么找? 3266472
关于科研通互助平台的介绍 2431470
邀请新用户注册赠送积分活动 2258135