Krynicka Patrycja, Koulaouzidis George, Skonieczna-Żydecka Karolina, Marlicz Wojciech, Koulaouzidis Anastasios
Department of Gastroenterology, Pomeranian Medical University, 71-252 Szczecin, Poland.
Department of Biochemical Sciences, Pomeranian Medical University, 71-460 Szczecin, Poland.
Diagnostics (Basel). 2025 Jan 26;15(3):292. doi: 10.3390/diagnostics15030292.
The gut microbiota, a complex community of microorganisms, plays a crucial role in gastrointestinal (GI) health, influencing digestion, metabolism, immune function, and the gut-brain axis. Dysbiosis, or an imbalance in microbiota composition, is associated with GI disorders, including irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and colorectal cancer (CRC). Conventional microbiota analysis methods, such as next-generation sequencing (NGS) and nuclear magnetic resonance (NMR), provide valuable insights but are often expensive, time-consuming, and destructive. Raman spectroscopy (RS) is a non-invasive, cost-effective, and highly sensitive alternative. This analytical technique relies on inelastic light scattering to generate molecular "fingerprints", enabling real-time, marker-free analysis of microbiota composition and metabolic activity. This review explores the principles, sample preparation techniques, and advancements in RS, including surface-enhanced Raman spectroscopy (SERS), for microbiota research. RS facilitates identifying microbial species, analysing key metabolites like short-chain fatty acids (SCFA), and monitoring microbiota responses to dietary and therapeutic interventions. The comparative analysis highlights RS's advantages over conventional techniques, such as the minimal sample preparation, real-time capabilities, and non-destructive nature. The integration of RS with machine learning enhances its diagnostic potential, enabling biomarker discovery and personalised treatment strategies for GI disorders. Challenges, including weak Raman signals and spectral complexity, are discussed alongside emerging solutions. As RS technology advances, mainly through portable spectrometers and AI integration, its clinical application in microbiota diagnostics and personalised medicine is poised to transform GI healthcare, bridging microbiota research with practical therapeutic strategies.
肠道微生物群是一个复杂的微生物群落，在胃肠道（GI）健康中起着至关重要的作用，影响消化、代谢、免疫功能和肠脑轴。微生物群失调，即微生物群组成失衡，与胃肠道疾病有关，包括肠易激综合征（IBS）、炎症性肠病（IBD）和结直肠癌（CRC）。传统的微生物群分析方法，如下一代测序（NGS）和核磁共振（NMR），提供了有价值的见解，但通常成本高昂、耗时且具有破坏性。拉曼光谱（RS）是一种非侵入性、经济高效且高度灵敏的替代方法。这种分析技术依靠非弹性光散射来生成分子“指纹”，能够对微生物群组成和代谢活性进行实时、无标记分析。本文综述探讨了拉曼光谱用于微生物群研究的原理、样品制备技术及进展，包括表面增强拉曼光谱（SERS）。拉曼光谱有助于识别微生物种类、分析短链脂肪酸（SCFA）等关键代谢物以及监测微生物群对饮食和治疗干预的反应。对比分析突出了拉曼光谱相对于传统技术的优势，如样品制备最少、具备实时能力以及非破坏性。拉曼光谱与机器学习的整合增强了其诊断潜力，能够发现生物标志物并制定针对胃肠道疾病的个性化治疗策略。同时还讨论了包括拉曼信号微弱和光谱复杂性在内的挑战以及新出现的解决方案。随着拉曼光谱技术的进步，主要是通过便携式光谱仪和人工智能整合，其在微生物群诊断和个性化医学中的临床应用有望改变胃肠道医疗保健，将微生物群研究与实际治疗策略联系起来。
