Proteomic biomarker evaluation using antibody microarrays: association between analytical methods such as microarray and ELISA

作者
Nadezhda G. Gumanova,Natalya L. Bogdanova,V. A. Metelskaya
出处
期刊:Labmedicine [Oxford University Press]
卷期号:55 (3): 325-333 被引量:7
标识
DOI:10.1093/labmed/lmad083
摘要

OBJECTIVE: To evaluate the associations between analytical methods, such as microarray and enzyme-linked immunosorbent assay (ELISA); expedient cutoffs; and the lowest possible number of microarrays in analysis for target biomarker estimation in case-control studies. METHODS: This study included 321 serum specimens, gathered in different case-control studies to test for atherosclerosis and atrial fibrillation. Among them, 48 serum specimens were analyzed using microarray technology. We used ELISA and commercial kits for confirmation of the results. RESULTS: Three proteins-cadherin-P, neuronal nitric oxide synthase, and adenovirus fiber-were shown to have distinctly different values in the case group vs the control group. As a result, we used those proteins as the target for confirmation using our alternative analytical method. Also, these protein values represented the limiting range between the highest and lowest differences in case-control groups. The results of microarray assay were confirmed using ELISA and commercial kits in the same specimens, in which microarray profiling was performed, and also in separate large case-control groups. CONCLUSIONS: A 1.5-fold difference in the protein content, as measured using microarray technology, was shown to be sufficient for further investigation of the candidate proteins. As few as 3 microarrays were considered sufficient for perspective evaluation of the target proteins. Microarray serum profiling, therefore, provides semiquantitative determination of protein in serum.

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
整齐的忆彤完成签到,获得积分10
刚刚
拉长的元芹完成签到,获得积分10
刚刚
刚刚
always发布了新的文献求助10
刚刚
难啊难发布了新的文献求助10
刚刚
刚刚
YXCT发布了新的文献求助20
1秒前
1秒前
jl发布了新的文献求助10
1秒前
不知道完成签到,获得积分20
3秒前
cdercder应助寒烟采纳,获得10
3秒前
zuoshoubo发布了新的文献求助10
3秒前
carza发布了新的文献求助50
3秒前
Ava应助mayamaya采纳,获得10
4秒前
4秒前
4秒前
凡士林完成签到,获得积分10
4秒前
yiku发布了新的文献求助30
4秒前
生成完成签到,获得积分10
4秒前
栗子发布了新的文献求助10
5秒前
Owen应助lina采纳,获得10
6秒前
lanjinglin发布了新的文献求助10
6秒前
李lin发布了新的文献求助10
6秒前
彭于晏应助司徒明雪采纳,获得10
6秒前
叶伏天完成签到,获得积分10
6秒前
叶水之完成签到,获得积分10
6秒前
心灵美平彤完成签到 ,获得积分10
7秒前
hgy16完成签到,获得积分10
7秒前
合适雅绿发布了新的文献求助10
7秒前
8秒前
菠菜应助火星上的惜霜采纳,获得10
8秒前
yaya完成签到,获得积分10
9秒前
9秒前
xiaohan完成签到,获得积分10
9秒前
半世千秋完成签到,获得积分10
9秒前
jl完成签到,获得积分20
10秒前
10秒前
迅速的孤菱完成签到,获得积分10
10秒前
甜甜若冰发布了新的文献求助10
11秒前
11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
How to Use Machine Learning in Chemistry: An Introduction 1000
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
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 7582430
求助须知:如何正确求助?哪些是违规求助? 9161451
关于积分的说明 19602939
捐赠科研通 7164627
什么是DOI,文献DOI怎么找? 3266138
关于科研通互助平台的介绍 2431010
邀请新用户注册赠送积分活动 2257338