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使用基于量子点的微流控免疫组织化学自动测量多种癌症生物标志物。

Automated measurement of multiple cancer biomarkers using quantum-dot-based microfluidic immunohistochemistry.

作者信息

Kwon Seyong, Cho Chang Hyun, Lee Eun Sook, Park Je-Kyun

机构信息

†Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.

‡Research Institute and Hospital, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Republic of Korea.

出版信息

Anal Chem. 2015 Apr 21;87(8):4177-83. doi: 10.1021/acs.analchem.5b00199. Epub 2015 Apr 9.

Abstract

We report an automated multiple biomarker measurement method for tissue from cancer patients using quantum dot (QD)-based protein detection combined with reference-based protein quantification and autofluorescence (AF) removal. For multiplexed detection of biomarkers in tissue samples, visualization of QDs on cytokeratin was performed to create a multichannel microfluidic device on sites with dense populations of tumor cells. Three major breast cancer biomarkers (i.e., estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2) were labeled using QDs successively on cancer cells in tissue sections. For the automated measurement of biomarkers, a cytokeratin-based biomarker normalization method was used to measure the averaged expression of proteins. A novel AF-removal algorithm was developed, which normalizes the reference AF spectra reconstructed from unknown AF spectra based on random sampling. For accurate quantification of QDs, we automatically and accurately removed the AF signal from 344 spots of QD-labeled tissue samples using 240 reference AF spectra. Using analytical data with 10 tissue samples from breast cancer patients, the measured biomarker intensities were in good agreement with the results of conventional analyses.

摘要

我们报告了一种针对癌症患者组织的自动化多生物标志物测量方法,该方法使用基于量子点(QD)的蛋白质检测,并结合基于参考的蛋白质定量和自发荧光(AF)去除。为了对组织样本中的生物标志物进行多重检测,在细胞角蛋白上对量子点进行可视化处理,以在肿瘤细胞密集的部位创建一个多通道微流控装置。在组织切片中的癌细胞上依次使用量子点标记了三种主要的乳腺癌生物标志物(即雌激素受体、孕激素受体和人表皮生长因子受体2)。对于生物标志物的自动化测量,使用基于细胞角蛋白的生物标志物归一化方法来测量蛋白质的平均表达。开发了一种新颖的AF去除算法,该算法基于随机采样对从未知AF光谱重建的参考AF光谱进行归一化。为了准确量化量子点,我们使用240个参考AF光谱自动且准确地从344个量子点标记的组织样本斑点中去除了AF信号。使用来自乳腺癌患者的10个组织样本的分析数据,测得的生物标志物强度与传统分析结果高度一致。

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