Establishment of organoid models based on a nested array chip for fast and reproducible drug testing in colorectal cancer therapy

类有机物 基质凝胶 结直肠癌 炸薯条 癌症 计算机科学 医学 内科学 生物 遗传学 电信 血管生成
作者
Yancheng Cui,Rongrong Xiao,Yushi Zhou,Jianchuang Liu,Yi Wang,Xiaodong Yang,Zhanlong Shen,Bin Liang,Kai Shen,Yi Li,Geng Xiong,Yingjiang Ye,Xiaoni Ai
出处
期刊:Bio-design and manufacturing [Springer Science+Business Media]
卷期号:5 (4): 674-686 被引量:16
标识
DOI:10.1007/s42242-022-00206-2
摘要

The conventional microwell-based platform for construction of organoid models exhibits limitations in precision oncology applications because of low-speed growth and high variability. Here, we established organoid models on a nested array chip for fast and reproducible drug testing using 50% matrigel. First, we constructed mouse small intestinal and colonic organoid models. Compared with the conventional microwell-based platform, the mouse organoids on the chip showed accelerated growth and improved reproducibility due to the nested design of the chip. The design of the chip provides miniaturized and uniform shaping of the matrigel that allows the organoid to grow in a concentrated and controlled manner. Next, a patient-derived organoid (PDO) model from colorectal cancer tissues was successfully generated and characterized on the chip. Finally, the PDO models on the chip, from three patients, were implemented for high-throughput drug screening using nine treatment regimens. The drug sensitivity testing on the PDO models showed good quality control with a coefficient of variation under 10% and a Z' factor of more than 0.7. More importantly, the drug responses on the chip recapitulate the heterogeneous response of individual patients, as well as showing a potential correlation with clinical outcomes. Therefore, the organoid model coupled with the nested array chip platform provides a fast and reproducible means for predicting drug responses to accelerate precise oncology.Graphic abstract
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
2秒前
3秒前
无花果应助vv采纳,获得10
4秒前
4秒前
5秒前
赵睿智发布了新的文献求助10
5秒前
爆米花应助龙子怡采纳,获得10
6秒前
6秒前
小蘑菇应助鲤鱼灵寒采纳,获得10
6秒前
7秒前
风之子完成签到,获得积分10
8秒前
刘47发布了新的文献求助10
9秒前
12112321312完成签到,获得积分10
9秒前
11秒前
共享精神应助12112321312采纳,获得30
12秒前
ll完成签到 ,获得积分10
12秒前
cera发布了新的文献求助10
13秒前
13秒前
13秒前
寸阴若岁完成签到,获得积分10
13秒前
李健的小迷弟应助hirono采纳,获得10
13秒前
醒醒完成签到,获得积分10
14秒前
15秒前
赵睿智完成签到,获得积分20
15秒前
16秒前
16秒前
16秒前
juston应助Thousand采纳,获得10
16秒前
17秒前
隐形曼青应助Hy采纳,获得10
17秒前
超帅花瓣应助333采纳,获得10
17秒前
18秒前
酷波er应助兴奋若山采纳,获得10
18秒前
hjhhjh完成签到,获得积分10
18秒前
My_magnum_opus应助Spark采纳,获得10
19秒前
klj发布了新的文献求助10
19秒前
20秒前
20秒前
Eunectes发布了新的文献求助10
20秒前
小蘑菇应助wikkk采纳,获得10
20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
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
Positive Obsession: The Life and Times of Octavia E. Butler 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 7698407
求助须知:如何正确求助?哪些是违规求助? 9258147
关于积分的说明 20012317
捐赠科研通 7273501
什么是DOI,文献DOI怎么找? 3293303
关于科研通互助平台的介绍 2448741
邀请新用户注册赠送积分活动 2299393