Cheng Xuanbing, Li Zongqi, Zhu Jialun, Wang Jingyu, Huang Ruyi, Yu Lewis W, Lin Shuyu, Forman Sarah, Gromilina Evelina, Puri Sameera, Patel Pritesh, Bahramian Mohammadreza, Tan Jiawei, Hojaiji Hannaneh, Jelinek David, Voisin Laurent, Yu Kristie B, Zhang Ao, Ho Connie, Lei Lei, Coller Hilary A, Hsiao Elaine Y, Reyes Beck L, Matsumoto Joyce H, Lu Daniel C, Liu Chong, Milla Carlos, Davis Ronald W, Emaminejad Sam
Interconnected and Integrated Bioelectronics Lab (I²BL), Department of Electrical and Computer Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095.
Department of Materials Science and Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095.
Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2425526122. doi: 10.1073/pnas.2425526122. Epub 2025 Feb 27.
Mimicking metabolic pathways on electrodes enables in vivo metabolite monitoring for decoding metabolism. Conventional in vivo sensors cannot accommodate underlying complex reactions involving multiple enzymes and cofactors, addressing only a fraction of enzymatic reactions for few metabolites. We devised a single-wall-carbon-nanotube-electrode architecture supporting tandem metabolic pathway-like reactions linkable to oxidoreductase-based electrochemical analysis, making a vast majority of metabolites detectable in vivo. This architecture robustly integrates cofactors, self-mediates reactions at maximum enzyme capacity, and facilitates metabolite intermediation/detection and interference inactivation through multifunctional enzymatic use. Accordingly, we developed sensors targeting 12 metabolites, with 100-fold-enhanced signal-to-noise ratio and days-long stability. Leveraging these sensors, we monitored trace endogenous metabolites in sweat/saliva for noninvasive health monitoring, and a bacterial metabolite in the brain, marking a key milestone for unraveling gut microbiota-brain axis dynamics.
在电极上模拟代谢途径能够实现体内代谢物监测以解读新陈代谢。传统的体内传感器无法适应涉及多种酶和辅因子的潜在复杂反应,只能针对少数代谢物处理一小部分酶促反应。我们设计了一种单壁碳纳米管电极架构,支持与基于氧化还原酶的电化学分析相联系的串联代谢途径样反应,从而使绝大多数代谢物能够在体内被检测到。这种架构能稳健地整合辅因子,以最大酶活性自我介导反应,并通过多功能酶的使用促进代谢物的中介作用/检测以及干扰失活。因此,我们开发了针对12种代谢物的传感器,其信噪比提高了100倍,且具有长达数天的稳定性。利用这些传感器,我们监测了汗液/唾液中的痕量内源性代谢物以进行无创健康监测,还监测了大脑中的一种细菌代谢物,这标志着在揭示肠道微生物群 - 脑轴动态方面的一个关键里程碑。