Lamarre Mathieu, Boudreau Denis
Department of Chemistry, Pavillon Alexandre-Vachon, 1045, avenue de la Médecine, Université Laval, Quebec City, Quebec G1 V0A6, Canada.
Centre d'optique, photonique et lasers (COPL), Pavillon d'Optique-Photonique, 2375 rue de la Terrasse, Université Laval, Quebec City, Quebec G1 V0A6, Canada.
ACS Omega. 2025 Apr 7;10(15):15610-15620. doi: 10.1021/acsomega.5c00867. eCollection 2025 Apr 22.
The detection and classification of lipopolysaccharides (LPS), pivotal constituents of Gram-negative bacteria, are fundamental to the progression of biosensing technologies in fields such as healthcare, environmental monitoring, and food safety. This study presents an innovative approach utilizing a panel of glycan-selective lectins in conjunction with surface plasmon resonance (SPR) providing a novel perspective on the evolution of biosensors within the context of the ongoing tension between the highly selective, one-probe-one-target methodology and the broader, resource-intensive approach that integrates complex and costly technological tools into the biosensing discipline. Guided by the principles of lean development, we employed a panel of lectins to construct multiprobe detection profiles, thereby facilitating the precise classification of LPS variants while minimizing both variability and resource expenditure. Advanced machine learning techniques were applied to optimize feature selection and enhance classification accuracy, demonstrating that a minimal set of four lectins sustains exceptional predictive performance. This synergy between traditional affinity techniques and data science enhances assay engineering efficiency, scalability, and integration into routine workflows, supporting frontline pathogen monitoring. This innovative approach holds promise for addressing global health challenges, providing more profound insights into biosensing methodologies, and expanding pathogen screening networks closer to the public and health safety management bodies.
脂多糖（LPS）作为革兰氏阴性菌的关键成分，其检测与分类对于医疗保健、环境监测和食品安全等领域生物传感技术的发展至关重要。本研究提出了一种创新方法，利用一组聚糖选择性凝集素结合表面等离子体共振（SPR），在高选择性的单探针单靶点方法与将复杂且昂贵的技术工具整合到生物传感学科中的更广泛、资源密集型方法之间持续存在的紧张关系背景下，为生物传感器的发展提供了新视角。在精益开发原则的指导下，我们使用一组凝集素来构建多探针检测图谱，从而在最小化变异性和资源消耗的同时，促进LPS变体的精确分类。应用先进的机器学习技术来优化特征选择并提高分类准确性，结果表明最少只需四种凝集素就能维持出色的预测性能。传统亲和技术与数据科学之间的这种协同作用提高了检测工程的效率、可扩展性以及融入常规工作流程的能力，支持一线病原体监测。这种创新方法有望应对全球健康挑战，为生物传感方法提供更深刻的见解，并扩展更贴近公众和健康安全管理机构的病原体筛查网络。
