Data_Sheet_2_Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.pdf

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

在单细胞分辨率下实现病原细菌与抗生素耐药性的快速、精准、无标记检测，是临床诊断领域面临的一项技术难题。突破病原细菌繁琐的培养流程与耗时的药敏试验环节，将极大助力临床早期诊断，并优化抗生素的临床使用方案。拉曼光谱（Raman spectroscopy）能够以无标记、无需培养的方式获取病原细菌的分子指纹图谱，适配单细胞分辨率下的病原体检诊需求。本研究提出一种结合拉曼光谱与机器学习的方法，可在单细胞分辨率下快速精准地鉴定病原细菌并检测其抗生素耐药性。研究结果显示，该机器学习方法对12种常见病原细菌的平均鉴定准确率为90.73±9.72%。利用该机器学习模型，对从住院患者体内分离得到的鲍曼不动杆菌（Acinetobacter baumannii）的敏感株与耐药株的区分准确率可达99.92±0.06%。同时本研究发现，敏感菌株的核酸/蛋白比值更高，而耐药菌株的蛋白质中含有丰富的酰胺II（amide II）结构。本研究表明，拉曼光谱是一种有望实现病原微生物快速鉴定与药敏检测的可靠技术手段。