Wen Yunjie, Li Yutao, Cheng Shibo, Crow Jennifer, Samuel Glenson, Vishwakarma Vikalp, Turaga Soumya M, Bantis Leonidas, Godwin Andrew K, Zeng Yong
Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.
Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas 66103, United States.
ACS Nano. 2025 Apr 1;19(12):11973-11986. doi: 10.1021/acsnano.4c16904. Epub 2025 Mar 21.
Emerging digital bioassays provide opportunities for bioanalysis and clinical diagnostics due to their improved analytical performance. Prevailing digitization strategies rely on partitioning single molecules into discrete compartments for which bead-based capture may be used. Herein we report a partition-free digital enzyme-linked immunosorbent assay (dELISA) named microfluidic Topographically Intensified, Partition-less dELISA (μTIP-dELISA). Our method builds on a single-molecule signal amplification technique that employs a simple micropost device to generate a topographic nanogap array to significantly enhance surface-bound enzymatic reactions. Compared to existing dELISA methods, our approach features appreciable simplicity and enhanced adaptability as it obviates the needs for sophisticated device fabrication, complicated workflow for off-line immunomagnetic capture, and ultralow-volume compartmentalization. Moreover, μTIP-dELISA integrates the inherent advantages of microfluidics in improving the assay performance and throughput and enhancing the scalability and automation. As a proof-of-concept for potential biomedical applications, we adapted μTIP-dELISA to extracellular vesicle (EV)-based liquid biopsy diagnosis of a pediatric cancer, Ewing sarcoma (EWS). Our technology confers >300-fold improvement over the conventional ELISA in detecting four EWS protein biomarkers. We demonstrated highly sensitive and specific detection of EWS cases with a machine-learning-defined four-marker EV signature. The four EV markers were further tested to assess age as a prognostic factor, resulting in an overall accuracy of 97% for classifying the control, pediatric and adult subjects. Overall, we envision that our μTIP single-molecule signal amplification strategy could promote the development and adaptation of digital bioassays and that the μTIP-dELISA could provide a useful tool for clinical diagnostics for many malignancies.
新兴的数字生物测定法因其改进的分析性能为生物分析和临床诊断提供了机会。现有的数字化策略依赖于将单个分子分隔到离散的隔室中,为此可使用基于珠子的捕获方法。在此,我们报告了一种无分隔的数字酶联免疫吸附测定法(dELISA),即微流控拓扑增强无分隔dELISA(μTIP-dELISA)。我们的方法基于一种单分子信号放大技术,该技术采用简单的微柱装置生成拓扑纳米间隙阵列,以显著增强表面结合的酶促反应。与现有的dELISA方法相比,我们的方法具有明显的简单性和更高的适应性,因为它无需复杂的设备制造、离线免疫磁捕获的复杂工作流程以及超小体积的分隔。此外,μTIP-dELISA整合了微流控技术在提高测定性能和通量以及增强可扩展性和自动化方面的固有优势。作为潜在生物医学应用的概念验证,我们将μTIP-dELISA应用于基于细胞外囊泡(EV)的小儿癌症尤因肉瘤(EWS)的液体活检诊断。我们的技术在检测四种EWS蛋白生物标志物方面比传统ELISA提高了300倍以上。我们通过机器学习定义的四标志物EV特征证明了对EWS病例的高灵敏度和特异性检测。进一步测试了这四种EV标志物以评估年龄作为预后因素,在对对照、小儿和成人受试者进行分类时总体准确率达到97%。总体而言,我们设想我们的μTIP单分子信号放大策略可以促进数字生物测定法的开发和应用,并且μTIP-dELISA可以为许多恶性肿瘤的临床诊断提供有用工具。