单细胞拉曼光谱在微生物研究中的应用

拉曼显微光谱是一种能够提供0.5–1.0μm空间分辨率的单个微生物细胞内化学结构信息的研究技术。近几年来,拉曼显微光谱被越来越多地应用于微生物单细胞的研究中,它可以快速无损地检测微生物细胞内的特征化学组分。典型的单个微生物细胞的拉曼光谱包含核酸、蛋白质、碳水化合物、脂质和色素(例如类胡萝卜素)等信息,这些信息能够表征微生物细胞的基因型、表型和生理状态。所以单细胞拉曼显微光谱是一种可用于区分微生物样品的"全生物指纹"技术,它可用于研究单个微生物细胞生命阶段的转变、鉴定微生物单细胞中的色素及其他化合物的含量变化等。本文综述了目前拉曼显微光谱在微生物单细胞研究上的应用,主要包括与稳定同位素标记(stable isotope probing,SIP)、拉曼成像、光谱分类和细胞分选技术结合来探究微生物单细胞对物质吸收后特征峰的变化、推导物质循环过程、进行微生物分类鉴定和探索基因型与表型的关系。拉曼显微光谱作为微生物单细胞研究的手段之一,在代谢过程的研究、活细胞分选和细胞对物质的利用上具有广泛的应用前景。     

单细胞拉曼技术在病原微生物检测中的研究进展

单细胞拉曼技术是基于拉曼光谱分析原理实现非培养、无标签、快速、高效、低成本揭示物质本质的新方法。近年来,单细胞拉曼技术也开始在病原微生物检测领域崭露头角。本文结合单细胞拉曼技术基本原理这一理论基础,阐述该技术在病原微生物鉴定、药物敏感性检测中的研究进展及最新技术方向,并探讨其在临床实验室应用的可行性,为未来病原微生物检测技术提供新的方向。     

利用PCA-LDA和参数-CART方法对直肠癌血清拉曼光谱的分析

本文利用拉曼光谱技术检测由直肠癌的发生导致的血清成分的变化。比较了直肠癌患者和对照组之间血清拉曼光谱的差异,并对术后直肠癌患者血清拉曼光谱的变化也进行了比较,以监测术后治疗效果。结果表明在某些波数位置不同组的拉曼峰有统计学意义的变化,这些变化反应了血清中相应的生物物质的改变。之后,主成分分析(PCA)及峰强比参数这两种方法被用于原始拉曼光谱的特征变量的提取。将线性判别分析(LDA)和分类回归树(CART)两种判别分析法用于特征变量的判别分析。PCA-LDA和参数-CART方法的诊断准确率分别为87%和90%。     

拉曼光谱分析技术在资源微生物发酵性能评价中的应用

微生物发酵过程是细胞新陈代谢进行物质转化的过程,为了提高目标产物的转化率,需要对微生物发酵动态特性进行实时分析,以便实时优化发酵过程。拉曼光谱(Raman spectroscopy)量化测试作为一种有应用前景的在线过程分析技术,可以在避免微生物污染的条件下,实现精准监测,进而用于优化控制微生物发酵过程。【目的】以运动发酵单胞菌(Zymomonas mobilis)为例,建立微生物发酵过程中葡萄糖、木糖、乙醇和乳酸浓度拉曼光谱预测模型,并进行准确性验证。【方法】采用浸入式在线拉曼探头,收集运动发酵单胞菌发酵过程中多个组分的拉曼光谱,采用偏最小二乘法对光谱信号进行预处理和多元数据分析,结合离线色谱分析数据,对拉曼光谱进行建模分析和浓度预测。【结果】针对运动发酵单胞菌,首先实现拉曼分析仪对单一产品乙醇发酵过程的精准检测,其次基于多元变量分析,建立葡萄糖、乙醇和乳酸浓度变化的预测模型,实现对发酵过程中各成分浓度变化的准确有效分析。【结论】成功建立了一种评价资源微生物尤其是工业菌株发酵液多种组分的拉曼光谱分析方法。该方法为运动发酵单胞菌等工业菌株利用多组分底物工业化生产不同产物的实时检测,以及其他微生物尤其工业菌株的选育和过程优化提供了新方法。     

唇腺炎性病变组织的共聚焦显微拉曼光谱特征研究

目的:研究唇腺炎性病变组织的拉曼光谱指纹特征,为拉曼光谱技术临床鉴别诊断唇腺炎性病变提供理论基础。方法:收集舍格伦综合征病变唇腺30例、唇腺急性炎症组织18例及正常唇腺组织30例,应用激光共聚焦显微拉曼光谱仪对唇腺组织进行拉曼光谱检测。应用主成分分析法(Principal component analysis,PCA)及判别函数(Discrimination function analysis,DFA)对光谱数据进行分析,研究唇腺组织光谱指纹诊断价值。结果:唇腺炎症组织与正常组织光谱间存在光谱指纹差异,这些差异代表了某些蛋白、核酸及脂类物质等生物大分子发生改变。PCA-DFA分析发现这些差异性拉曼光谱具有鉴别诊断价值,可以区分不同唇腺组织,总体诊断准确率达91.8%,经交互验证后准确率为89.4%。结论:不同唇腺炎症组织及正常组织间拉曼光谱存在差异,不仅揭示生物大分子改变,还具有临床鉴别诊断价值。拉曼光谱技术在唇腺炎性病变组织鉴别诊断具有巨大应用潜力。     

基于树木年轮径切特征的卷积神经网络树种识别

卷积神经网络可以通过树木年轮样本构造特征图像实现物种识别的自动化。本研究通过建立树木年轮样本构造特征图像集,选用LeNet、AlexNet、GoogLeNet和VGGNet 4个卷积神经网络模型,实现基于树木年轮横切面的计算机自动化树种精准识别,进而确定各模型的树种识别准确率,明晰不同树种在自动识别中的混淆情况,探测不同模型识别结果的差异。结果表明: 本研究训练的用于树种识别的卷积神经网络模型具有较好的可信度;4个模型中GoogLeNet模型树种识别准确率最高,为96.7%,LeNet模型识别准确率最低(66.4%);不同模型对于所选树种的识别结果具有一致性,表现为对蒙古栎识别准确率最高(AlexNet模型识别率达到100%),对臭冷杉的识别准确率最低。本研究中也存在类似结构树种的识别混淆情况。模型在科和属水平的识别准确率高于种水平;阔叶树种因其显著的结构差异容易区分,阔叶树树种的识别准确率高于针叶树。总体上,通过卷积神经网络,探测了树木年轮特征的深层信息,达到树种的精准识别,提供了一种快速便捷的自动树种初筛鉴定方法。     

单细胞稳定同位素标记技术在固氮微生物中的应用研究

生物固氮是指固氮微生物将大气中氮气还原为生物可利用氨的过程,是环境中新氮的主要来源,调控初级生产力并影响氮储库的收支平衡。由于环境中大部分固氮微生物不可纯培养,不依赖培养且具有高空间分辨率水平的单细胞技术,成为研究固氮微生物的有力手段。~(15)N_2稳定同位素标记技术,以微生物对~(15)N的同化量或速率为依据,是表征微生物固氮活性的最直接手段。本文对~(15)N_2稳定同位素标记结合两种单细胞技术,即纳米二次离子质谱(Nano SIMS)和单细胞拉曼光谱,用于固氮微生物研究的最新进展进行了综述,内容包括揭示环境中高活性固氮微生物、空间分布、与其他生物的共生关系、细胞生理状态等,并进一步对近期发展的基于单细胞拉曼光谱的固氮微生物研究进行了展望。     

拉曼光谱检测微生物的研究方法和进展北大核心

快速准确地识别和鉴定微生物对于环境科、食品质量以及医学诊断等领域研究至关重要。拉曼光谱(Raman spectroscopy)已经被证明是一种能够实现微生物快速诊断的新技术,在提供微生物指纹图谱信息的同时,能够快速、非标记、无创、敏感地在固体和液体环境中实现微生物单细胞水平的检测。本文简单介绍了拉曼光谱的基本概念和原理,重点综述了拉曼光谱微生物检测应用中的样品处理方法及光谱数据处理方法。除此之外,本文概括了拉曼光谱在细菌、病毒和真菌中的应用,其中单独概括了拉曼在细菌快速鉴定和抗生素药敏检测中的应用。最后,本文阐述了拉曼光谱在微生物检测中的挑战和展望。     

胃癌与萎缩性胃炎的血清拉曼光谱数据分析

利用激光诱导拉曼光谱技术,测定了萎缩性胃炎患者、胃癌患者血清的拉曼光谱。采用主成分分析法和判别分析法对拉曼光谱数据进行了分析和处理,得到辨别胃癌和萎缩性胃炎的准确率为92％。     

Using Raman spectroscopy and chemometrics to identify the growth phase of <Emphasis Type="Italic">Lactobacillus casei</Emphasis> Zhang during batch culture at the single-cell level

Background

As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment.

Methods

In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS.

Results

Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%.

Conclusions

To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Single‐cell genomics based on Raman sorting reveals novel carotenoid‐containing bacteria in the Red Sea

Cell sorting coupled with single‐cell genomics is a powerful tool to circumvent cultivation of microorganisms and reveal microbial ‘dark matter’. Single‐cell Raman spectra (SCRSs) are label‐free biochemical ‘fingerprints’ of individual cells, which can link the sorted cells to their phenotypic information and ecological functions. We employed a novel Raman‐activated cell ejection (RACE) approach to sort single bacterial cells from a water sample in the Red Sea based on SCRS. Carotenoids are highly diverse pigments and play an important role in phototrophic bacteria, giving strong and distinctive Raman spectra. Here, we showed that individual carotenoid‐containing cells from a Red Sea sample were isolated based on the characteristic SCRS. RACE‐based single‐cell genomics revealed putative novel functional genes related to carotenoid and isoprenoid biosynthesis, as well as previously unknown phototrophic microorganisms including an unculturable Cyanobacteria spp. The potential of Raman sorting coupled to single‐cell genomics has been demonstrated.     

基于单细菌共焦拉曼光谱的细菌快速检测

【背景】目前利用共焦拉曼光谱技术进行成像和成分鉴别方面的研究较多,但如何快速检测与鉴别多种细菌方面的研究较少。【目的】基于共焦拉曼光谱技术,建立一种在单细菌水平上实现病原微生物快速分类鉴定的方法。【方法】以大肠杆菌为研究对象,利用共焦拉曼光谱技术在单细菌水平上进行了激发波长的优化试验,并研究了大肠杆菌存放时间对单细菌拉曼光谱信息的影响。同时,对白色葡萄球菌、大肠杆菌、金黄色葡萄球菌、沙门氏菌和铜绿假单胞菌进行了共焦拉曼光谱测试,并对5种细菌进行单细菌拉曼光谱的归属分析,设计共焦拉曼光谱技术结合支持向量机(support vector machine,SVM)模型学习算法,进行了5种细菌的快速分类鉴别。【结果】对于单细菌拉曼光谱探测,532、633和785 nm这3种常见的拉曼探测波长中,532 nm具有更好的激发效率和光谱信噪比。结合SVM模型对5种细菌的识别分类,SVM模型的灵敏度和特异性达到了96.00%以上,整体准确率为98.25%。不同存放时间下大肠杆菌拉曼光谱的重复性和稳定性都很好,且SVM模型匹配率均在90.00%以上。【结论】单细菌拉曼光谱结合SVM模型可对5种细菌进行快...     

风险矩阵分级模型在哈尔滨市食品安全风险评估中的建立与应用

目的:建立哈尔滨市风险矩阵分级模型,并应用该模型探索食品污染的风险。方法:采用风险矩阵、文献综述及专家判断法对某年食品安全风险监测中位列化学污染物及微生物污染超标率首位的食品-项目组合,即动物肝脏-克伦特罗组合、水产品-副溶血性弧菌组合进行模型评估。结果:参照风险矩阵,动物肝脏-克伦特罗组合的健康风险分值为6,可以判断哈尔滨市人群由于食用动物肝脏而导致克伦特罗膳食暴露的健康风险等级为"中风险",水产品-副溶血性弧菌组合的健康风险分值为2,可以判断哈尔滨市人群每餐由于食用水产品而导致副溶血性弧菌膳食暴露的健康风险等级为"极低风险"。结论:哈尔滨市该年食品安全总体情况较好,风险等级较低,但部分监测项目存在食品安全风险隐患,应予以关注。     

Raman–deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota

Human intestinal microbiota is important to host health and is associated with various diseases. It is a challenge to identify the functions and metabolic activity of microorganisms at the single-cell level in gut microbial community. In this study, we applied Raman microspectroscopy and deuterium isotope probing (Raman–DIP) to quantitatively measure the metabolic activities of intestinal bacteria from two individuals and analysed lipids and phenylalanine metabolic pathways of functional microorganisms in situ. After anaerobically incubating the human faeces with heavy water (D₂O), D₂O with specific substrates (glucose, tyrosine, tryptophan and oleic acid) and deuterated glucose, the C–D band in single-cell Raman spectra appeared in some bacteria in faeces, due to the Raman shift from the C–H band. Such Raman shift was used to indicate the general metabolic activity and the activities in response to the specific substrates. In the two individuals' intestinal microbiota, the structures of the microbial communities were different and the general metabolic activities were 76 ± 1.0% and 30 ± 2.0%. We found that glucose, but not tyrosine, tryptophan and oleic acid, significantly stimulated metabolic activity of the intestinal bacteria. We also demonstrated that the bacteria within microbiota preferably used glucose to synthesize fatty acids in faeces environment, whilst they used glucose to synthesize phenylalanine in laboratory growth environment (e.g. LB medium). Our work provides a useful approach for investigating the metabolic activity in situ and revealing different pathways of human intestinal microbiota at the single-cell level.     

Influence of Stress on Single-Cell Lag Time and Growth Probability for Listeria monocytogenes in Half Fraser Broth

The impacts of 12 common food industry stresses on the single-cell growth probability and single-cell lag time distribution of Listeria monocytogenes were determined in half Fraser broth, the primary enrichment broth of the International Organization for Standardization detection method. First, it was determined that the ability of a cell to multiply in half Fraser broth is conditioned by its history (the probability for a cell to multiply can be decreased to 0.05), meaning that, depending on the stress in question, the risk of false-negative samples can be very high. Second, it was established that when cells are injured, the single-cell lag times increase in mean and in variability and that this increase represents a true risk of not reaching the detection threshold of the method in the enrichment broth. No relationship was observed between the impact on single-cell lag times and that on growth probabilities. These results emphasize the importance of taking into account the physiological state of the cells when evaluating the performance of methods to detect pathogens in food.Listeria monocytogenes has been involved in severe food-borne outbreaks with high mortality rates. This pathogen is widespread in many environments (16) and can be isolated from a large variety of foods which are the major routes of infection in humans. Ready-to-eat foods that can support the growth of L. monocytogenes may pose a major risk for public health, and the European Union legislation generally requires absence in 25 g at the production stage as a food safety criterion for this type of food (4).In food, L. monocytogenes is often affected by one or more stresses caused by a variety of processing treatments, including heating, freezing, and exposure to acids and to high osmotic pressures (15, 25, 29, 39). Recovering stressed L. monocytogenes from food is of great importance in food safety since sublethally injured bacteria may repair themselves under suitable conditions and regain or even increase their pathogenicity (19, 30).The injury of microbial cells has two major consequences for pathogen behavior in enrichment broths. First, injured cells become sensitive to selective components present in enrichment broths to which they normally show resistance (9, 10, 11, 42). Therefore, some cells of the stressed bacterial population do not initiate growth in enrichment broth, eventually resulting in an inefficient detection of pathogenic bacteria in food samples (50). This phenomenon can explain results obtained in several studies showing the effect of inoculum size on the growth limits of bacterial populations (26, 27, 37). Second, due to repair time, stressed cells show a longer lag phase than do healthy cells (5, 7, 37). This situation results in a true risk of not reaching the bacterial concentration necessary for the detection of the pathogen (in the range of 10² to 10⁴ CFU ml⁻¹) within the enrichment duration.The recent development of gene-based or immunologically based procedures, such as PCR, gene probes, and enzyme-linked immunosorbent assay, has facilitated the development of more-rapid methods which can identify positive samples in considerably shorter time periods. Nevertheless, these relatively rapid tests also require efficient enrichment steps to increase target organism numbers to detectable levels.At the moment of pathogen detection, low numbers of sublethally injured cells, as often encountered in naturally contaminated foods, show a wide distribution of lag-phase durations (45) and may not be able to multiply in broth containing selective components (11, 42). The challenge of the enrichment stage is to obtain appropriate enrichment conditions (2) which will favor pathogen resuscitation and limit the food microflora growth.In our study, we have focused on the primary enrichment phase of the International Organization for Standardization 11290-1 L. monocytogenes detection method (3), i.e., the half Fraser broth (1/2FB). The objectives were to investigate the impact of 12 different stresses on the single-cell growth probability and single-cell lag time of L. monocytogenes in 1/2FB. The intraspecific variability and the impact of food components and background microflora on single-cell growth probability were also studied.     

基于CFS-mRMR特征筛选方法和Adaboost算法的胶质瘤 相关基因筛选及预测模型的建立

目的:找出胶质瘤病变发生机制相关的基因群,并在此基础上建立预测胶质瘤病变发生的预测模型。方法:收集GEO中胶质瘤芯片数据,使用关联特征选择(Correlation-based Feature Subset, CFS)和最小冗余最大相关性(Minimum Redundancy MaximumRelevance, mRMR)特征选择方法筛选出差异基因,分析这些差异基因的功能,然后使用Adaboost算法建立胶质瘤的预测模型,并对模型的预测能力进行评估。结果:通过特征筛选,得到了19个和胶质瘤病变相关的的基因;以该19个基因建组成特征子集,结合AdaBoost算法建立了胶质瘤的预测模型,经验证,模型的预报准确率可以达到95.59%。通过对19个差异基因的GO和KEGG分析,发现这些基因和肿瘤的发生发展有一定作用。结论:CFS-mRMR特征筛选方法可以有效地发现与胶质瘤疾病有关的基因,所筛选的19个差异基因具有生物学意义,且以此构建的胶质瘤预测模型,可以有效地对预测胶质瘤的发生。     

Micro-Raman spectroscopy detects individual neoplastic and normal hematopoietic cells

Current methods for identifying neoplastic cells and discerning them from their normal counterparts are often nonspecific, slow, biologically perturbing, or a combination thereof. Here, we show that single-cell micro-Raman spectroscopy averts these shortcomings and can be used to discriminate between unfixed normal human lymphocytes and transformed Jurkat and Raji lymphocyte cell lines based on their biomolecular Raman signatures. We demonstrate that single-cell Raman spectra provide a highly reproducible biomolecular fingerprint of each cell type. Characteristic peaks, mostly due to different DNA and protein concentrations, allow for discerning normal lymphocytes from transformed lymphocytes with high confidence (p < 0.05). Spectra are also compared and analyzed by principal component analysis to demonstrate that normal and transformed cells form distinct clusters that can be defined using just two principal components. The method is shown to have a sensitivity of 98.3% for cancer detection, with 97.2% of the cells being correctly classified as belonging to the normal or transformed type. These results demonstrate the potential application of confocal micro-Raman spectroscopy as a clinical tool for single cancer cell detection based on intrinsic biomolecular signatures, therefore eliminating the need for exogenous fluorescent labeling.