Ilse Katz Institute for Nanoscale Science and Technology, Ben Gurion University of the Negev, Beer Sheva 84105, Israel.
Sensors (Basel). 2012;12(5):5572-85. doi: 10.3390/s120505572. Epub 2012 May 2.
Non-invasive detection and monitoring of lethal diseases, such as cancer, are considered as effective factors in treatment and survival. We describe a new disease diagnostic approach, denoted "reactomics", based upon reactions between blood sera and an array of vesicles comprising different lipids and polydiacetylene (PDA), a chromatic polymer. We show that reactions between sera and such a lipid/PDA vesicle array produce chromatic patterns which depend both upon the sera composition as well as the specific lipid constituents within the vesicles. The chromatic patterns were processed through machine-learning algorithms, and the bioinformatics analysis could distinguish both between cancer-bearing and healthy patients, respectively, as well between two types of cancers. Size-separation and enzymatic digestion experiments indicate that lipoproteins are the primary components in sera which react with the chromatic biomimetic vesicles. This colorimetric reactomics concept is highly generic, robust, and does not require a priori knowledge upon specific disease markers in sera. Therefore, it could be employed as complementary or alternative approach for disease diagnostics.
非侵入性检测和监测致命疾病，如癌症，被认为是治疗和生存的有效因素。我们描述了一种新的疾病诊断方法，称为“反应组学”，它基于血清与包含不同脂质和聚二乙炔（PDA）的囊泡阵列之间的反应，PDA 是一种显色聚合物。我们表明，血清与这种脂质/PDA 囊泡阵列之间的反应产生依赖于血清组成以及囊泡内特定脂质成分的显色图案。通过机器学习算法处理显色图案，生物信息学分析可以区分癌症患者和健康患者，以及两种类型的癌症。大小分离和酶消化实验表明，脂蛋白是与显色仿生囊泡反应的血清中的主要成分。这种比色反应组学概念具有高度通用性、稳健性，并且不需要血清中特定疾病标志物的先验知识。因此，它可以用作疾病诊断的补充或替代方法。
