Partition-Less Digital Immunoassay Using Configurable Topographic Nanoarrays for Extracellular Vesicle Diagnosis of Ewing Sarcoma

• Published in: ACS nano Vol. 19; no. 12; pp. 11973 - 11986
• Main Authors: Wen, Yunjie, Li, Yutao, Cheng, Shibo, Crow, Jennifer, Samuel, Glenson, Vishwakarma, Vikalp, Turaga, Soumya M., Bantis, Leonidas, Godwin, Andrew K., Zeng, Yong
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.04.2025
• Subjects: Child; Enzyme-Linked Immunosorbent Assay - methods; Extracellular Vesicles - chemistry; Extracellular Vesicles - metabolism; Humans; Immunoassay - methods; Sarcoma, Ewing - diagnosis; digital bioassays; partition-less dELISA; single-molecule signal amplification; extracellular vesicle; Ewing sarcoma diagnosis
• ISSN: 1936-0851; 1936-086X; 1936-086X
• DOI: 10.1021/acsnano.4c16904

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.