克拉斯
数字聚合酶链反应
胰腺癌
多路复用
多重聚合酶链反应
聚合酶链反应
癌症
突变体
突变
点突变
计算生物学
生物
基因
癌症研究
分子生物学
生物信息学
遗传学
作者
Qixin Hu,Fariha Kanwal,Weiyuan Lyu,Jiajie Zhang,Xu Liu,Kai Qin,Feng Shen
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2022-12-15
卷期号:8 (1): 114-121
被引量:12
标识
DOI:10.1021/acssensors.2c01776
摘要
Pancreatic cancer is a terminal disease with high mortality and very poor prognosis. A sensitive and quantitative analysis of KRAS mutations in pancreatic cancer provides a tool not only to understand the biological mechanisms of pancreatic cancer but also for diagnosis and treatment monitoring. Digital polymerase chain reaction (PCR) is a promising tool for KRAS mutation analysis, but current methods generally require a complex microfluidic handling system, which can be challenging to implement in routine research and point-of-care clinical diagnostics. Here, we present a droplet-array SlipChip (da-SlipChip) for the multiplex quantification of KRAS G12D, V, R, and C mutant genes with the wild-type (WT) gene background by dual color (FAM/ROX) fluorescence detection. This da-SlipChip is a high-density microwell array of 21,696 wells of 200 pL in 4 by 5424 microwell format with simple loading and slipping operation. It does not require the same precise alignment of microfeatures on the different plates that are acquired by the traditional digital PCR SlipChip. This device can provide accurate quantification of both mutant genes and the WT KRAS gene. We collected tumor tissue, paired normal pancreatic tissue, and other normal tissues from 18 pancreatic cancer patients and analyzed the mutation profiles of KRAS G12D, V, R, and C in these samples; the results from the multiplex digital PCR on da-SlipChip agree well with those of next-generation sequencing (NGS). This da-SlipChip moves digital PCR closer to the practical point-of-care applications not only for detecting KRAS mutations in pancreatic cancer but also for other applications that require precise nucleic acid quantification with high sensitivity.
科研通智能强力驱动
Strongly Powered by AbleSci AI