Department of Chemistry , University at Albany, State University of New York , 1400 Washington Avenue , Albany , New York 12222 , United States.
Anal Chem. 2018 Apr 17;90(8):5322-5328. doi: 10.1021/acs.analchem.8b00414. Epub 2018 Mar 27.
Sweat is a biological fluid present on the skin surface of every individual and is known to contain amino acids as well as other low molecular weight compounds. (1) Each individual is inherently different from one another based on certain factors including, but not limited to, his/her genetic makeup, environment, and lifestyle. As such, the biochemical composition of each person greatly differs. The concentrations of the biochemical content within an individual's sweat are largely controlled by metabolic processes within the body that fluctuate regularly based on attributes such as age, sex, and activity level. Therefore, the concentrations of these sweat components are person-specific and can be exploited, as presented here, to differentiate individuals based on trace amounts of sweat. For this concept, we analyzed three model compounds-lactate, urea, and glutamate. The average absorbance change from each compound in sweat was determined using three separate bioaffinity-based systems: lactate oxidase coupled with horseradish peroxidase (LOx-HRP), urease coupled with glutamate dehydrogenase (UR-GlDH), and glutamate dehydrogenase alone (GlDH). After optimization of a linear dependence for each assay to its respective analyte, analysis was performed on 50 mimicked sweat samples. Additionally, a collection and extraction method was developed and optimized by our group to evaluate authentic sweat samples from the skin surface of 25 individuals. A multivariate analysis of variance (MANOVA) test was performed to demonstrate that these three single-analyte enzymatic assays were effectively used to identify each person in both sample sets. This novel sweat analysis approach is capable of differentiating individuals, without the use of DNA, based on the collective responses from the chosen metabolic compounds in sweat. Applications for this newly developed, noninvasive analysis can include the field of forensic science in order to differentiate between individuals as well as the fields of homeland security and cybersecurity for personal authentication via unlocking mechanisms in smart devices that monitor metabolites. Through further development and analysis, this concept also has the potential to be clinically applicable in monitoring the health of individuals based on particular biomarker combinations.
汗液是每个人皮肤表面存在的一种生物液体,已知其含有氨基酸和其他低分子量化合物。(1) 每个人因其遗传构成、环境和生活方式等因素而与其他人不同。因此,每个人的生化组成都有很大的差异。个体汗液中的生化成分浓度主要由体内代谢过程控制,这些代谢过程会根据年龄、性别和活动水平等属性定期波动。因此,这些汗液成分的浓度因人而异,可以像这里展示的那样,利用微量汗液来区分个体。对于这一概念,我们分析了三种模型化合物——乳酸、尿素和谷氨酸。使用三种独立的生物亲和性系统:辣根过氧化物酶偶联的乳酸氧化酶(LOx-HRP)、谷氨酸脱氢酶偶联的脲酶(UR-GlDH)和单独的谷氨酸脱氢酶(GlDH),确定了汗液中每种化合物的平均吸光度变化。在对每种分析物的每个测定进行线性依赖关系的优化之后,对 50 个模拟汗液样本进行了分析。此外,我们小组还开发并优化了一种收集和提取方法,以评估来自 25 个人皮肤表面的真实汗液样本。多元方差分析(MANOVA)测试表明,这三种单一分析物酶测定法可有效用于识别两个样本集中的每个人。这种新的汗液分析方法能够在不使用 DNA 的情况下,基于汗液中选择的代谢化合物的集体反应来区分个体。这种新开发的非侵入性分析方法的应用可以包括法医学领域,以区分个体,以及国土安全和网络安全领域,通过监控代谢物的智能设备解锁机制进行个人认证。通过进一步的开发和分析,这一概念还有可能在基于特定生物标志物组合监测个体健康方面具有临床应用潜力。