微生物学报

Research content	Traditional methods	Single-cell Raman spectroscopy technology
Classification and identification	1. Cultivation to the logarithmic growth phase requires several days or weeks 2. Complex technical procedures such as biochemical reactions and sequencing increase costs 3. Differentiation down to genus or species level 4. Dependence on the technical proficiency of personnel	1. No culture required 2. Capable of distinguishing down to the strain level 3. Direct spectral acquisition following water washing, with straightforward operation
Pathogenicity research	1. Require large quantities of pure cultures 2. Molecular biology methods take 2-3 hours and yield inconsistent results 3. Animal models are costly and time-consuming	1. Direct detection of virulence factors within less than one hour 2. High sensitivity with detection limits at the nanogram level, enhanced to the picogram level via SERS 3. Low cost
Antimicrobial resistance research	1. Cultures must be maintained until the logarithmic growth phase and further observation is required 2. Blurred zones in the paper disc diffusion method may lead to misinterpretation 3. The broth dilution method is unsuitable for detecting low concentrations of bacteria 4. Only qualitative determination is possible	1. Directly assesses phenotypic expression 2. Provide insights into drug resistance mechanisms
Activity evaluation	1. The plate count method relies on cultivation, is operationally complex, allows for quantification, but requires several days 2. The fluorescent dye method is susceptible to interference, has poor specificity, but is rapid 3. It cannot detect viable but non-culturable bacteria (VBNC bacteria)	1. Capable of distinguishing VBNC bacteria 2. Enable dynamic monitoring of bacterial activity 3. Heavy water incubation requires 2-4 hours
Multi-omics research	1. The procedure is complex and destructive, failing to preserve the original morphology of bacteria 2. The experimental cycle is lengthy, requiring several days or weeks 3. Unknown metabolites can be identified	1. No need to lyse bacteria, preserving their original morphology 2. Enable linking of metabolic phenotypes to genotypes 3. Cannot directly identify metabolite structures; requires combination with other methods

Research content	Traditional methods	Single-cell Raman spectroscopy technology
Classification and identification	1. Cultivation to the logarithmic growth phase requires several days or weeks 2. Complex technical procedures such as biochemical reactions and sequencing increase costs 3. Differentiation down to genus or species level 4. Dependence on the technical proficiency of personnel	1. No culture required 2. Capable of distinguishing down to the strain level 3. Direct spectral acquisition following water washing, with straightforward operation
Pathogenicity research	1. Require large quantities of pure cultures 2. Molecular biology methods take 2-3 hours and yield inconsistent results 3. Animal models are costly and time-consuming	1. Direct detection of virulence factors within less than one hour 2. High sensitivity with detection limits at the nanogram level, enhanced to the picogram level via SERS 3. Low cost
Antimicrobial resistance research	1. Cultures must be maintained until the logarithmic growth phase and further observation is required 2. Blurred zones in the paper disc diffusion method may lead to misinterpretation 3. The broth dilution method is unsuitable for detecting low concentrations of bacteria 4. Only qualitative determination is possible	1. Directly assesses phenotypic expression 2. Provide insights into drug resistance mechanisms
Activity evaluation	1. The plate count method relies on cultivation, is operationally complex, allows for quantification, but requires several days 2. The fluorescent dye method is susceptible to interference, has poor specificity, but is rapid 3. It cannot detect viable but non-culturable bacteria (VBNC bacteria)	1. Capable of distinguishing VBNC bacteria 2. Enable dynamic monitoring of bacterial activity 3. Heavy water incubation requires 2-4 hours
Multi-omics research	1. The procedure is complex and destructive, failing to preserve the original morphology of bacteria 2. The experimental cycle is lengthy, requiring several days or weeks 3. Unknown metabolites can be identified	1. No need to lyse bacteria, preserving their original morphology 2. Enable linking of metabolic phenotypes to genotypes 3. Cannot directly identify metabolite structures; requires combination with other methods

单细胞拉曼技术在细菌领域应用的研究进展

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宋晓慧 , 魏强 ^*

微生物学报 | 综述 2025,65(12): 5283-5293

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微生物学报 | 综述 2025, 65(12): 5283-5293

单细胞拉曼技术在细菌领域应用的研究进展

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宋晓慧, 魏强^*

作者信息

中国疾病预防控制中心(中国预防医学科学院)，国家病原微生物资源库/国家病原微生物保藏中心，北京

Research progress in the application of single-cell Raman technology in the field of bacteria

Xiaohui SONG, Qiang WEI^*

Affiliations

National Pathogen Resource Center, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China

出版时间: 2025-12-04 doi: 10.13343/j.cnki.wsxb.20250463

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摘要

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近年来，随着细菌耐药性问题日益突出，细菌感染给疾病防控工作带来了严峻挑战。快速、准确地鉴别细菌及其基本特征对于疾病防控、医学诊断以及科学研究而言极其重要。相较于平板培养计数法、PCR以及三磷酸腺苷(adenosine triphosphate bioluminescence, ATP)生物发光法等传统检测方法，单细胞拉曼光谱技术在细菌分类与鉴定、细菌致病性与耐药性检测以及细菌活性评价方面展现出一定优势和应用前景。本文综述了单细胞拉曼技术在细菌领域的应用，为从事细菌研究和拉曼研究的人员提供了技术和应用参考。

关键词

单细胞 / 拉曼光谱 / 细菌 / 检测

Abstract

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In recent years, as the antibiotic resistance of bacteria is aggravating, bacterial infections have brought severe challenges to disease prevention and control. Rapid and accurate identification of bacteria and their basic characteristics is extremely important for disease prevention and control, medical diagnosis, and scientific research. Compared with conventional detection methods such as plate culture counting, polymerase chain reaction (PCR), and adenosine triphosphate bioluminescence (ATP) bioluminescence, single-cell Raman spectroscopy has shown advantages and broad application prospects in bacterial classification and identification, bacterial pathogenicity and antibiotic resistance detection, and bacterial viability evaluation. This paper reviews the application of single-cell Raman technology in the field of bacteria, aiming to provide technical and application reference for practitioners engaged in the research on bacteria and Raman technology.

Key words

single cell / Raman spectroscopy / bacteria / detection

引用本文

宋晓慧, 魏强. 单细胞拉曼技术在细菌领域应用的研究进展. 微生物学报, 2025 , 65 (12) : 5283 -5293 . DOI: 10.13343/j.cnki.wsxb.20250463

Xiaohui SONG, Qiang WEI. Research progress in the application of single-cell Raman technology in the field of bacteria[J]. Acta Microbiologica Sinica, 2025 , 65 (12) : 5283 -5293 . DOI: 10.13343/j.cnki.wsxb.20250463

正文

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细菌是一类单细胞生物，广泛分布于土壤和水中，或与其他生物共生。一些细菌可感染人和动物并导致疾病，这类细菌被称为病原细菌(pathogenic bacteria)。根据全球疾病负担2021数据统计，2021年全球约有471万例死亡与细菌耐药相关，其中114万例死亡直接归因于细菌耐药，全球因细菌导致的死亡情况位居前列^[1]。因此快速发现和鉴定细菌、研究细菌致病机制、抗生素耐药性(antimicrobial resistance, AMR)以及细菌活性具有重要意义。

细菌具有细胞壁、细胞膜、细胞质和核质等基本结构，依据结构差异可分为革兰氏阳性菌和革兰氏阴性菌、需氧菌和厌氧菌、抗生素抗性菌和抗生素敏感菌等。目前，细菌鉴定通常采用伯杰氏鉴定细菌学手册、革兰氏染色、药敏试验、生化实验以及基因测序等方法^[2-6]；细菌致病力检测主要有基因测序、PCR等分子生物学方法，酶联免疫吸附测定(enzyme-linked immunosorbent assay, ELISA)等免疫学方法，以及基于平板培养的方法^[7-10]；抗生素耐药监测主要有Kirby-Bauer纸片法(K-B法)、微量琼脂稀释法、微量肉汤稀释法和自动药敏检测系统等定性和定量方法^[11-15]；检测细菌活性常用平板培养计数法、三磷酸腺苷(adenosine triphosphate bioluminescence, ATP)生物发光法、氧化还原法、PCR法和流式细胞仪等方法^[16-18]。传统检测方法存在试剂耗材昂贵、操作繁琐、耗时较长、对工作人员操作要求较高等问题。与传统检测方法相比，单细胞拉曼技术能够直接对单个细菌进行高精度分析，揭示细胞间的差异，因而具有快速、灵敏、无损且样本前处理简单等优势。本文综述了单细胞拉曼技术在细菌分类与鉴定、细菌致病性、抗生素耐药性和细菌活性等方面的应用，以期为从事细菌研究和拉曼研究的人员提供技术和应用参考。

1 单细胞拉曼技术

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1923年，Smekal^[19]依据经典量子理论预测了分子对光的拉曼散射现象，1928年，Raman和Krishnan通过实验验证了这一现象^[20]。拉曼散射产生的光谱被称为拉曼光谱(Raman spectroscopy, RS)。拉曼光谱技术是一种基于单色光非弹性散射的光学方法。当激光光源照射样品时，入射光子会与样品中的分子发生碰撞并产生散射；其中大多数散射光的频率与入射光频率相同，无法提供有用信息，这种散射被称为弹性散射，也称瑞利散射；极少数散射光的频率与入射光频率不同，被称为非弹性散射，也称拉曼散射；在拉曼散射中，频率低于入射光频率的散射光被称为斯托克斯线，频率高于入射光频率的散射光则称为反斯托克斯线，斯托克斯线和反斯托克斯线统称为拉曼谱线^[21]。散射发生前后的能量差即为拉曼位移，通过拉曼位移可识别出造成散射的物质，从而获取样品的详细信息^[22]。

拉曼光谱是一种散射光谱，它是由化合物中分子键被激发到虚能态后尚未恢复到原始能态，导致入射光被散射后频率发生变化而产生的现象。光谱信号对应化学键的振动，凭借激发光的非弹性散射和分子共振，可对单个细胞产生独特的“指纹图谱”^[23-26]。单细胞拉曼光谱技术是一种快速无损的技术，通过微流控芯片、显微操作技术或单细胞拉曼分选等方法分离单细胞，对单个细菌细胞进行重水(D₂O)标记，或将其固定在氟化钙等不同材质的玻片上后进行拉曼检测，经主成分分析(principal component analysis, PCA)等降维处理、K-均值(K-means)等无监督聚类、支持向量机(support vector machine, SVM)等有监督聚类，并通过混淆矩阵(Confusion Matrix)给出准确率、特异性等关键指标，最终提供丰富的表型信息(图1)。因此单细胞拉曼技术能够实现单细胞精度的测量，且可进行非标记的原位检测，是实现高精度单细胞代谢功能探测的重要手段^[27]。

2 细菌的分类与鉴定

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细菌可依据其形态、结构、染色情况、生理生化特征以及遗传特征等进行分类。目前，基于培养的方法被视为细菌分类和鉴定的“黄金标准”^[28]。细菌培养能够提供大量的微生物细胞用于进一步测试，也是一种从混合培养物中分离不同微生物的方法，但基于培养的方法相对耗时、费力。单细胞拉曼技术(single-cell Raman technology, SCRS)是基于拉曼光谱分析原理来检测物质结构的方法。它通过收集检测物的生物学信息(包括蛋白质、核酸、脂质等)构建生物体特有的“指纹图谱”，并结合不同的分析方法实现细菌的快速、准确区分。阮真等^[29]将拉曼与机器学习相结合实现了6种非结核分枝杆菌(Mycobacterium tuberculosis)的区分。Liu等^[30]将单细胞拉曼光谱与卷积神经网络(convolutional neural network, CNN)模型相结合用于金黄色葡萄球菌(Staphylococcus aureus)表型特征鉴定，CNN模型对产肠毒素菌株、耐甲氧西林金黄色葡萄球菌和不同生长阶段的识别准确率分别为93.90%、98.73%和98.66%，其灵敏度虽逊色于质谱，但在免培养、免破坏以及代谢通路分析方面具有一定优势。Locke等^[31]提出将光学相干断层成像术和拉曼光谱技术相结合以检测流感嗜血杆菌(Haemophilus influenzae)、肺炎链球菌(Streptococcus pneumoniae)、黏膜炎莫拉氏菌(Moraxella catarrhalis)和铜绿假单胞菌(Pseudomonas aeruginosa)的折射率、光学衰减和生化组成的差异，从而实现中耳炎的4种主要致病菌的区分。Cui等^[32]利用相干反斯托克斯拉曼散射(coherent anti-Stokes Raman scattering, CARS)非线性显微成像技术检测1 589 cm^-1处的拉曼信号，可准确区分革兰氏阴性细菌和革兰氏阳性细菌。同时，依据非线性光信号穿透性强的特点，有望实现对细菌感染的原位实时检测和分类。

单细胞拉曼光谱技术能够提供细菌细胞的“分子指纹”，机器学习等算法可从大量的拉曼光谱数据中高效、准确地识别细菌种类。目前的研究大多基于纯培养后细菌进行鉴别和分类，这需要大量时间和特定的培养条件。开发便携式设备有利于将单细胞拉曼技术应用于环境微生物检测和食品安全等现场监测，以便在真实环境中使用^[33]。由于拉曼散射是一种非弹性散射，入射光照射样品时只有极少数的光子会发生拉曼散射，导致拉曼信号较弱，表面增强拉曼技术(surface-enhanced Raman spectroscopy, SERS)通过金纳米棒阵列产生的局域表面等离激元效应从而实现pg/mL级别的痕量检测^[34-36]；CARS则利用皮秒脉冲激光诱导的三阶非线性极化过程增强信号，Gudavičius等^[37]首次实现了1 089个焦点同时激发的CARS成像，实现了0.3 Hz帧率的百万像素CARS图像采集，为超快成像提供了基础。前者保证了定量灵敏度，后者提供了时空分辨能力。然而目前缺乏统一的标准和规范来指导单细胞拉曼技术在细菌分类和鉴定中的应用，导致不同研究之间的结果难以对比和验证。因此研究人员应推广标准化操作流程、数据分析方法以及标准化拉曼光谱数据库，从而确保拉曼光谱数据的准确性、可比性和可重复性。

3 致病性研究

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细菌的致病性是指细菌突破宿主的防御系统并在机体内繁殖引起疾病的特性。细菌接触机体后能否致病与其毒力有关。拉曼光谱可以识别与毒力相关的代谢产物及信号分子。Wang等^[38]使用改良的表面增强拉曼技术免疫传感器检测微孔板上的葡萄球菌肠毒素B。Zhang等^[39]将拉曼光谱与机器学习相结合，在其他2种蛋白质干扰的情况下，快速、准确地区分相思豆毒素、蓖麻毒素、葡萄球菌肠毒素B和银环蛇毒素，准确率达到100%。

生物膜的形成与细菌感染密切相关。细菌生物膜(bacterial biofilm)是指细菌黏附于接触表面，分泌多糖基质、纤维蛋白、脂质蛋白等，将自身包绕其中而形成的大量细菌聚集膜状物。细菌生物膜是细菌为适应自然环境、有利于生存的一种生命现象，对抗生素和吞噬作用有很好的保护作用，从而有效抵御抗生素的攻击^[40]。目前，人们特别关注在医用植入物、手术固定物和人工组织、血管替代物表面形成的细菌生物膜，这种生物膜是导致感染的关键因素之一，其复杂结构和抗药性增加了治疗难度^[41]。拉曼技术能够监测生物膜的形成过程，分析其结构和成分，帮助理解细菌的生存策略。Samek等^[42]结合光镊和拉曼显微光谱技术，在单细胞水平上以非接触的方式实时区分生物膜阳性和生物膜阴性的表皮葡萄球菌。Shakeel等^[43]基于表面增强拉曼技术和机器学习表征了非、中、强生物膜形成的表皮葡萄球菌菌株。有研究证明，拉曼显微光谱是检测人骨移植物中表皮葡萄球菌的一种很有前途的诊断工具^[44]。拉曼显微光谱技术可用于生物被膜的原位检测、可视化、鉴定和化学表征。Sandt等^[45]应用共聚焦显微拉曼光谱技术研究了在流动池中生长的完全水合生物膜的组成、结构和发育，并监测生物膜成分在完全水合的未受干扰生物膜中随时间的分布。他们利用细胞和胞外聚合物的拉曼特征来表征生物膜成分。

单细胞拉曼技术可以分析细菌在感染过程中内部成分及其构成比的变化，包括核酸、蛋白质、脂类等，以揭示其致病机制^[46]。拉曼技术对生物膜的成功检测与表征证实了生物膜的物理存在与空间结构，也表明其内部微环境为毒素的聚集、稳定乃至强化提供了理想场所。这种被生物膜包裹和修饰的毒素其生物学活性与游离状态相比通常发生显著改变。因此研究内毒素如何从生物膜中释放需从静态的检测转向动态的机制研究。单细胞拉曼技术也可以提供细胞内代谢物的详细图谱，从而帮助观测毒素诱导下细胞内代谢通路的变化，为理解毒素致病机制提供数据支持。尽管已有研究涉及多种毒素，但该领域的研究仍相对较新，还有许多细菌内毒素和外毒素及其机制尚未被充分探索。以S. aureus为例，现有的单细胞拉曼研究集中在S. aureus与其他菌的分类上，对其产生的肠毒素、α溶血素、血浆凝固酶等的拉曼研究较少。今后的研究可以聚焦于区分产不同毒素类型的菌株，实现细菌毒素的定性和定量区分。

4 抗生素耐药研究

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抗生素耐药性是指病原体(细菌、病毒、真菌等)无法被抗生素及其他抗感染药物有效杀灭的抵御能力。在临床诊断中常用的检测方法有生化检测和基于质谱的方法。由于细菌耐药性问题日益严峻，通常还需要进行额外的抗菌药物敏感性试验(antimicrobial susceptibility testing, AST)。传统的AST方法包括纸片扩散法、梯度扩散法、微量稀释法和浓度梯度纸条扩散法(E-test法)。这些方法均需进行细菌培养，而基于单细胞分子表型的快速药敏检测可省去细菌增殖所需的时间^[11]。目前，基于单细胞水平快速分子表型的药敏检测主要依赖于活细菌对重水(D₂O)的代谢摄入，通过检测碳-氘(C-D)峰的强度能够实现抗菌药物最低抑菌浓度(minimum inhibitory concentration, MIC)的快速检测。

C-D峰位于拉曼光谱的2 040-2 300 cm^-1波段，该波段在未进行D₂O标记的细菌中通常无可检测到的拉曼峰，具有高特异性。传统方法检测分离菌株对各类药物的最低抑菌浓度至少需要16-18 h，而拉曼快速药敏试验方法仅需4-5 h^[22]。Yang等^[47]开发了适用于尿液标本直接药敏检测的单细胞拉曼光谱技术，在C-D比值(C-D ratio)的基础上通过设置敏感/耐药截止值(susceptible/resistant, S/R)进行药敏结果判读，实现了从接收尿液标本到结果读取总检测时间缩短至2.5 h且准确度高的效果。研究表明重水拉曼技术可在2 h内快速识别呼吸道病原体的耐药性^[48]。刘育含等^[49]采用肉汤稀释法和重水拉曼技术，通过MIC和基于代谢活性的最低抑制代谢浓度(minimum inhibitory concentration based on metabolic activity, MIC-MA)定量评价了抑菌剂对粪肠球菌的生长和代谢抑制作用。Yi等^[50]开发了一种快速拉曼辅助抗生素药敏试验(fast Raman-assisted antibiotic susceptibility test, FRAST)，并将其应用于9个尿液感染性样本和3个脓毒症样本的实际临床分析，使报告结果时间分别缩短至3 h和21 h。这与传统药敏检测动辄数天的时间相比大大缩短了报告时间，且检测结果与基质辅助激光解吸飞行时间质谱鉴定和常规药敏试验结果一致。在临床诊断方面，Zhao等^[51]通过结合微流清洗与FRAST，在3 h内完成了尿液感染样本的药敏测试，揭示了大肠杆菌、粪肠球菌(Enterococcus faecalis)和S. aureus尿液样本的耐药谱，结果与肉汤微量稀释法一致。单细胞拉曼技术不仅具有快速准确的优势，还可对细菌耐药谱以及抗生素抗性进化过程进行表征。Dastgir等^[52]研究证明SERS可用于快速鉴别结核病阳性利福平耐药样本和结核病阳性利福平敏感样本。Ogunlade等^[53]将拉曼光谱技术和机器学习相结合，实现了对25 000株对4种主要抗结核药物(异烟肼、利福平、莫西沙星和阿米卡星)之一耐药的结核分枝杆菌复合群卡介苗菌株(Bacillus Calmette-Guérin, BCG)的抗生素耐药谱分类，其中干燥样本的准确率达到98%，痰液样本的准确率达到79%。单细胞拉曼技术能够动态追踪抗生素敏感菌向抗生素抗性菌变化的过程。Yang等^[54]将大肠杆菌多次循环暴露于临床治疗剂量的氨苄青霉素，通过单细胞拉曼-氘同位素-多元统计分析等多种技术联用的方法，实现了细菌抗生素抗性进化过程中表型生理轨迹的快速原位追踪。同时发现，细菌在抗生素处理时活性随处理循环数逐渐增加，说明其表型耐受性逐渐提高。

单细胞拉曼技术能够实时监测细胞对抗生素的应答过程，研究细菌在抗药性环境下的生长和适应机制对于理解耐药性的发生与发展至关重要。单细胞拉曼技术用于AST正走向临床转化阶段，但目前存在抗生素谱覆盖有限的问题。现有研究多集中在替加环素、多黏菌素B或万古霉素，β-内酰胺类和新型抗菌肽等仍缺乏研究^[55]；对于脓毒血症等特殊样本，由于菌含量低仍需培养环节，且存在混合感染和基质干扰的情况，因此可以结合微流控技术同时精进算法做进一步研究。未来单细胞拉曼将朝着高通量和自动化方向发展，进一步缩短检测时间，提高检测效率。

5 细菌活性评价

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微生物的活性是微生物生物合成能力与功能活性的总和。评价细菌活性的指标包括生长曲线、菌落形成单位(colony forming unit, CFU)、光密度(optical density, OD)、细胞膜完整性以及细胞形态等。中国科学院徐健团队的一项研究表明，单细胞拉曼技术能够揭示阳离子和阴离子表面活性剂对S. aureus的抗菌作用机制，在中性pH条件下表面活性剂能有效破坏S. aureus的细胞膜完整性，导致细胞内主要成分(核酸、蛋白质和细胞色素)的泄漏^[56]；该团队还证明类胡萝卜素的碳13掺入率可用于测量不同基因型单个蓝藻细胞的生长速率和碳固定速率，从而大幅加快不同生长时期细胞的筛选过程^[57]。杨鸿博^[58]采用拉曼光谱结合偏最小二乘回归(partial least squares regression, PLSR)方法对宰后初期2种不同包装方式(真空包装和50% O₂气调包装)牛肉的pH值进行预测，该方法能够准确预测贮藏第0天和第21天测定的菌落总数和乳酸菌数，这表明拉曼光谱结合PLSR模型在预测贮藏开始和结束时微生物数量方面具有良好能力。Liu等^[59]通过使用D₂O标记的单细胞拉曼光谱结合机器学习技术，实现了对S. aureus代谢活性的检测和分类；支持向量机模型对活的和死的S. aureus的分类准确率达99.82%，而线性判别分析模型对具有不同代谢活性的S. aureus的分类准确率为99.92%。一项研究对多种类型的癌细胞进行了稳健的单细胞分析，并在年轻和年老的成纤维细胞中进行了基于衰老的单细胞分析，确定了1 571 cm^-1处的拉曼峰为衰老标志物相关的波段特征^[60]。受此启发，可关注与细菌活性相关的拉曼特征峰，利用特征峰对应的物质筛选不同活性细菌的代谢物，从而确定导致细菌活性差异的生物标志物。

单细胞拉曼技术能够反映细菌内代谢物的变化，为研究细菌代谢途径提供信息，同时揭示细菌的生长和繁殖机制，在病原微生物鉴定、活性检测和标准菌株评价方面具有广阔的应用前景。在微生物保藏方面，单细胞拉曼技术有望用于监测微生物保藏过程中微生物细胞的生理代谢状态，评价细菌活性和稳定性。在标准菌株评价方面，单细胞拉曼“化学指纹”通过单细胞分辨率和非侵入性化学分析，弥补了传统菌株保藏质控中群体平均掩盖个体异常、依赖培养导致滞后的不足，有望成为菌株质量控制的有效工具。标准菌株的光谱数据库可建立“化学指纹”参考，通过机器学习等算法比对候选菌株的光谱差异，确保微生物遗传和活性的稳定性。

6 拉曼光谱与多组学

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拉曼光谱可实现一次检测获取单细胞的生理生化特征，其中蕴含了基因组学、转录组学、蛋白质组学和代谢组学的丰富信息。传统的组学技术在提供分子机制的同时会对细菌造成不同程度的破坏。拉曼光谱作为一种无损、非标记的技术，与多组学形成互补。随着组学的发展，拉曼组技术平台(Ramanome technology platform, RTP)的概念被提出，有望应用于细胞功能筛选和菌株开发^[61]。Kobayashi-Kirschvink等^[62]开发了一种从活细胞拉曼光谱推断单细胞表达谱的无标记无损框架(Raman2RNA)，推断了不同细胞状态的表达谱，将拉曼光谱与转录组联系起来。Qin等^[63]将拉曼技术与基因组学和蛋白质组学结合，揭示了受铜绿假单胞菌外囊泡影响的巨噬细胞的代谢改变。SERS已成为基于拉曼的定量代谢组学的主要方法，能够在复杂的生物混合物中定量/识别超低浓度的分子，在液体活检、单细胞药敏、微生物功能研究中已可与质谱形成互补^[64]。然而，拉曼技术结合机器学习解释多组学机制尚处于初步阶段，发展更先进的化学计量学算法和人工智能模型有利于从相关性深入洞察因果机制^[65]。

7 总结与展望

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综上所述，拉曼光谱可实现细菌微生物的识别与鉴定，反映微生物代谢与活性，在病原菌鉴定、致病性、抗生素耐药性研究等方面有广泛应用，与传统方法相比展现出一定优势(表1)。单细胞拉曼光谱技术在一次检测中能够获取丰富的多维度信息，可根据特征峰的位置、强度和形状精准识别物质的化学键和官能团等分子构成，进而揭示细菌抗生素敏感和耐药的动态变化过程。单细胞拉曼技术与基因组学、转录组学等结合形成多组学的综合研究^[63-67]，有助于更全面地理解细菌的耐药性和致病性机制。

目前，单细胞拉曼技术的操作流程、数据解读和算法支持尚未完全标准化，不同实验室之间的结果可比性较低。随着拉曼技术的发展、计算方法和标准化流程的不断深入研究，公开共享的标准化数据库将提供更全面、准确和可比的数据资源，从而推动相关领域的研究和应用发展。越来越多的研究将尝试和探索微生物的原位拉曼光谱检测，最终应用于真实环境中的微生物检测研究^[25,68]。相信随着标准化拉曼光谱数据库的建立、数据算法的推进以及人工智能的发展，单细胞拉曼技术终将在临床诊断、微生物学研究和环境监测等领域实现突破性进展，带来微生物学的新时代^[65]。

致谢

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感谢中国科学院微生物研究所付钰研究员、中国科学院青岛生物能源与过程研究所徐健研究员、中国疾病预防控制中心传染病预防控制所闫笑梅研究员和王多春研究员的指导。

作者贡献声明

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宋晓慧：论文构思和撰写；魏强：论文构思和指导。

作者利益冲突公开声明

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作者声明不存在任何可能会影响本文所报告工作的已知经济利益或个人关系。

基金

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国家重点研发计划(2022YFC2602200)
国家重点研发计划(2022YFC2602301)
国家重点研发计划(2021YFC2302002)

参考文献

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2025年第65卷第12期

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doi: 10.13343/j.cnki.wsxb.20250463

接收时间：2025-06-13
首发时间：2025-12-08
出版时间：2025-12-04

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收稿日期：2025-06-13
录用日期：2025-08-27

基金

National Key Research and Development Program of China(2022YFC2602200)

国家重点研发计划(2022YFC2602200)

National Key Research and Development Program of China(2022YFC2602301)

国家重点研发计划(2022YFC2602301)

National Key Research and Development Program of China(2021YFC2302002)

国家重点研发计划(2021YFC2302002)

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中国疾病预防控制中心(中国预防医学科学院)，国家病原微生物资源库/国家病原微生物保藏中心，北京

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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Research content	Traditional methods	Single-cell Raman spectroscopy technology
Classification and identification	1. Cultivation to the logarithmic growth phase requires several days or weeks 2. Complex technical procedures such as biochemical reactions and sequencing increase costs 3. Differentiation down to genus or species level 4. Dependence on the technical proficiency of personnel	1. No culture required 2. Capable of distinguishing down to the strain level 3. Direct spectral acquisition following water washing, with straightforward operation
Pathogenicity research	1. Require large quantities of pure cultures 2. Molecular biology methods take 2-3 hours and yield inconsistent results 3. Animal models are costly and time-consuming	1. Direct detection of virulence factors within less than one hour 2. High sensitivity with detection limits at the nanogram level, enhanced to the picogram level via SERS 3. Low cost
Antimicrobial resistance research	1. Cultures must be maintained until the logarithmic growth phase and further observation is required 2. Blurred zones in the paper disc diffusion method may lead to misinterpretation 3. The broth dilution method is unsuitable for detecting low concentrations of bacteria 4. Only qualitative determination is possible	1. Directly assesses phenotypic expression 2. Provide insights into drug resistance mechanisms
Activity evaluation	1. The plate count method relies on cultivation, is operationally complex, allows for quantification, but requires several days 2. The fluorescent dye method is susceptible to interference, has poor specificity, but is rapid 3. It cannot detect viable but non-culturable bacteria (VBNC bacteria)	1. Capable of distinguishing VBNC bacteria 2. Enable dynamic monitoring of bacterial activity 3. Heavy water incubation requires 2-4 hours
Multi-omics research	1. The procedure is complex and destructive, failing to preserve the original morphology of bacteria 2. The experimental cycle is lengthy, requiring several days or weeks 3. Unknown metabolites can be identified	1. No need to lyse bacteria, preserving their original morphology 2. Enable linking of metabolic phenotypes to genotypes 3. Cannot directly identify metabolite structures; requires combination with other methods

Research content

Traditional methods

Single-cell Raman spectroscopy technology

Classification and identification

1. Cultivation to the logarithmic growth phase requires several days or weeks

2. Complex technical procedures such as biochemical reactions and sequencing increase costs

3. Differentiation down to genus or species level

4. Dependence on the technical proficiency of personnel

1. No culture required

2. Capable of distinguishing down to the strain level

3. Direct spectral acquisition following water washing, with straightforward operation

Pathogenicity research

1. Require large quantities of pure cultures

2. Molecular biology methods take 2-3 hours and yield inconsistent results

3. Animal models are costly and time-consuming

1. Direct detection of virulence factors within less than one hour

2. High sensitivity with detection limits at the nanogram level, enhanced to the picogram level via SERS

3. Low cost

Antimicrobial resistance research

1. Cultures must be maintained until the logarithmic growth phase and further observation is required

2. Blurred zones in the paper disc diffusion method may lead to misinterpretation

3. The broth dilution method is unsuitable for detecting low concentrations of bacteria

4. Only qualitative determination is possible

1. Directly assesses phenotypic expression

2. Provide insights into drug resistance mechanisms

Activity evaluation

1. The plate count method relies on cultivation, is operationally complex, allows for quantification, but requires several days

2. The fluorescent dye method is susceptible to interference, has poor specificity, but is rapid

3. It cannot detect viable but non-culturable bacteria (VBNC bacteria)

1. Capable of distinguishing VBNC bacteria

2. Enable dynamic monitoring of bacterial activity

3. Heavy water incubation requires 2-4 hours

Multi-omics research

1. The procedure is complex and destructive, failing to preserve the original morphology of bacteria

2. The experimental cycle is lengthy, requiring several days or weeks

3. Unknown metabolites can be identified

1. No need to lyse bacteria, preserving their original morphology

2. Enable linking of metabolic phenotypes to genotypes

3. Cannot directly identify metabolite structures; requires combination with other methods