南极海洋芳香烃低温降解微生物的拉曼光谱分析

激光镊子拉曼光谱技术可以实现在自然状态下对单个细胞或细胞器较长时间的观察研究.应用激光镊子拉曼光谱技术实时观察南极微生物低温降解芳香烃过程中单个南极细菌的细胞生长和胞内生物大分子的动态变化过程,收集、分析其拉曼光谱,结果发现:单细胞的拉曼光谱反映了其细胞内部的生命物质组成,南极动球菌 NJ41 和希瓦氏菌 NJ49 生...     

用激光拉曼光谱区分胃癌变细胞与正常细胞

利用激光拉曼光谱对胃癌细胞及正常胃细胞进行了对比检测,对胃癌细胞特征拉曼峰作了初步探讨,发现胃癌患者标本的特征拉曼峰与非胃癌患者标本的光谱有明显的不同。实验发现,浓度为每mL 1.25×105个胃癌细胞的样品在经过若干天培养后均能够被检测出胃癌细胞的特征拉曼峰,分别位于特定波长583,633,656 nm处。这是几个很少见诸报道的新拉曼光谱峰。不论是特征拉 曼峰,还是在胃癌细胞及正常胃细胞的几个共有的拉曼峰674, 707, 773, 799 nm处,拉曼光谱强度都是随血清中癌细胞浓度的增加而增加。激光拉曼光谱分析有可能发展成为一种快速有效的癌细胞检测方 法。     

人肝癌组织细胞的激光光镊拉曼光谱研究

激光光镊与拉曼光谱相结合形成的激光光镊拉曼光谱系统(LTRS)已用于分析生物组织标本,可对单个活细胞进行操控和光谱收集。从拉曼光谱特征峰位置、强度和线宽可得到有关细胞的组成、结构及细胞内物质相互作用的信息。文章应用LTRS系统,分析了来自人的恶性肝癌组织的不同病变部位标本,包括肝癌组织细胞、肝癌癌旁细胞和远离肝癌组织的肝脏正常的组织细胞,观察到了随肝癌的病变部位变化所出现的一些有趣的拉曼光谱峰的变化。正常的肝组织细胞在1 070和1 266 cm-1处的峰很明显,而肝癌和肝癌癌旁组织细胞的这两个峰则不明显,肝正常组织细胞的1 445 cm-1峰明显高于肝癌和肝癌癌旁组织细胞。已知1 070 cm-1峰代表脂类和核酸,1 266和1 445 cm-1峰代表脂类和蛋白。引起这些峰变化的物质很可能参与了肝癌的发生。上述初步研究结果表明：单细胞激光光镊拉曼光谱可以区分肝癌的不同病变部位,将是检测和分析肝癌组织标本的一种很好的方法。     

单细胞拉曼光谱结合多元统计方法分析不同酿酒酵母菌株的成分差异

应用单细胞拉曼光谱技术结合多元统计方法分析比较了六株不同来源的酿酒酵母菌株的同步化细胞,从而获知菌株间细胞成分的差异.利用连续密度梯度离心法分离酿酒酵母同步化细胞,分别对六个酵母菌株的同步化单细胞进行拉曼信号收集,并结合主成分分析(PCA)、辨别函数分析(DFA)两种多元统计分析光谱差异.结果表明,细胞的拉曼光谱表征其...     

光镊捕获金福菇孢子的拉曼光谱分析

使用光镊拉曼光谱系统俘获悬浮在生理盐水中的单个金福菇孢子,激发并收集其拉曼信号,结果显示单个金福菇孢子的拉曼光谱基本能呈现其内含物的组成和结构信息,脂类物质是其主要成分。同种栽培方式的3个不同菌株单个金福菇孢子的平均拉曼光谱信号基本一致,分别对其进行多元统计分析(PCA),无法区分其拉曼光谱信号;同一菌株不同栽培方式的单个孢子平均拉曼光谱的差异光谱表明其主要成分相同,都以脂类物质为主;而源自于脂类1743、1655、1442、1125、1080、1070、876、728cm-1等峰的信号强度基本一致,说明其脂类的含量基本相同。从单细胞拉曼光谱角度初步分析金福菇孢子,为光镊拉曼光谱技术研究食用菌孢子萌发机理提供有意义的参考。     

拉曼镊子分析红酵母合成类胡萝卜素

利用拉曼镊子对红酵母合成类胡萝卜素进行分析,考查氮源和碳源对类胡萝卜素产量的影响.取发酵终点细胞,一部分用于紫外光谱法测定,另一部分用拉曼镊子检测.原始光谱经过背景扣除、基线校正等方法预处理,定性分析不同培养基培养细胞的平均光谱,类胡萝卜素的拉曼信号强度有明显不同;紫外检测结果和拉曼峰高数据有良好的相关性,拟合参数的相关系数分别达到0.907 8和0.9121;定量分析1508cm1峰高表明适宜红酵母细胞生长和类胡萝卜素合成的氮源和碳源分别是酵母粉+胰蛋百胨、葡萄糖.以上结果说明,拉曼镊子能提供红酵母胞内类胡萝卜索的含量信息,是实时检测红酵母细胞类胡萝卜素合成和优化发酵培养基的有效工具.     

单细胞拉曼光谱研究游离脂肪酸对NIT-1胰岛β细胞生理的影响

运用激光镊子拉曼光谱技术探讨高浓度游离脂肪酸对NIT-1胰岛β细胞生理的影响。实验组细胞以含0.25mmol/L棕榈酸培养12,24,48h,平行对照组中不含任何脂肪酸。通过扫描形式收集各组细胞的拉曼光谱后,使用Hoechs33342/PI荧光染色保留活细胞的光谱,并在OriginPro 8.0系统中比较各组平均光谱的差异。结果显示对照组间的拉曼光谱无明显差异;高脂组光谱在780、1124、1092cm-1及1172cm-1发生拉曼位移,且780、1172、855cm-1及873cm-1峰值强度降低。这些变化的光谱峰在分子归属上指认为细胞内DNA和蛋白质。由此推断高浓度游离脂肪诱导NIT-1细胞损伤在光谱表现为细胞内DNA及蛋白质的结构、含量改变,且该变化与细胞的凋亡有着密切联系。在实验中激光光镊拉曼系统对观察细胞早期损伤有着积极作用。     

内吞金纳米粒子的鼻咽癌细胞SERS光谱

采用内吞方法将金纳米粒子引入细胞内,测试分析单个活性CNE-1鼻咽癌细胞的常规拉曼光谱和SERS光谱,并对其进行初步谱峰归属。CNE-1细胞的常规拉曼光谱有6个主要的拉曼峰:718,1001,1123,1336,1446和1660cm-1;沉积于细胞内的金纳米粒子强烈地增强了细胞内生化物质拉曼信号,在内吞金纳米粒子的CNE-1细胞的拉曼光谱中出现了20多个SERS拉曼信号,主要拉曼峰的强度明显高于常规拉曼信号。DNA骨架振动(1026,1097,1336和1585cm-1)证明金纳米粒子通过内吞作用而进入细胞核内。结果表明,基于胶体金SERS技术可能为活性鼻咽癌细胞内生化物质的探测提供一种高灵敏的方法。     

光镊拉曼光谱法分析红法夫酵母内虾青素含量

建立一种用激光镊子拉曼光谱法快速定量红法夫酵母细胞内虾青素含量的方法。测定不同浓度虾青素标准品溶液的拉曼光谱,取其1 520cm-1峰峰高绘制虾青素标准曲线;取不同氮源、碳源的红法夫酵母细胞,一部分用激光镊子拉曼光谱法测定,一部分用紫外-可见分光光度法测定;最后分析两者间的相关性。结果表明虾青素标准曲线的相关系数到达0.998 3。在单位质量红法夫酵母虾青素含量与单位体积红法夫酵母发酵液虾青素产量方面,对比激光镊子拉曼光谱法与紫外-可见分光光度法,两种方法所得数据具有良好的相关性,其相关系数分别达到0.917 7和0.905 4,表明激光镊子拉曼光谱法能够达到紫外-可见分光光度法的测定效果,是定量分析红法夫酵母细胞内虾青素的更有效方法。     

激光镊子拉曼光谱法优化红法夫酵母合成虾青素条件

用激光镊子拉曼光谱法优化红法夫酵母生产虾青素的条件。首先,将红法夫酵母细胞拉曼光谱与虾青素标准品溶液拉曼光谱进行对比,找出定量虾青素的拉曼特征峰;然后对照红法夫酵母生长曲线与不同时间点红法夫酵母细胞内虾青素含量曲线,进行发酵时间的优化;最后将红法夫酵母分别用不同氮源、碳源培养基培养,对比其细胞的虾青素拉曼特征峰强度,最终优化培养基。由实验可得,适宜定量虾青素的拉曼特征峰是1520cm-1峰;最佳发酵时间为72h;最佳优化培养基氮源含硫酸铵4g/L+硝酸钾4g/L,碳源含蔗糖60g/L。从上述结果可知,用激光镊子拉曼光谱法对红法夫酵母合成虾青素的条件进行优化,充分发挥了其操作简便、耗时短、样品用量少、对样品无损伤等优势,使所得结果更准确可信。因此,激光镊子拉曼光谱法是红法夫酵母细胞合成虾青素条件优化的理想选择。     

单个血小板的拉曼光谱分析

利用波长为785 nm的激光束构建光镊拉曼光谱系统,俘获悬浮在生理盐水中的人和三种动物(家猪、大鼠、家兔)的单个血小板,激发并收集其拉曼光谱。单个血小板的平均拉曼光谱信号清楚地反映了血小板的生化组成特点,人血小板的拉曼光谱与三种动物的明显不同,1 524 cm-1是人类血小板所特有的拉曼信号峰,而1 157 cm-1在人类中显著高于三种动物的,人类血小板的平均I_{1 157}/ I_{1 003}为0.795,而家猪、大鼠、家兔的分别为0.532,0.502,0.485。多元统计分析结果显示,不同物种血小板的拉曼信号落入不同的空间,可以判别区分。上述结果表明,单个血小板的拉曼光谱基本反映了血小板的主要组成成分,结合多元统计分析可以用于辨别不同的物种的血小板,光镊拉曼光谱系统是实时研究细胞生理、生化变化的快速简便而有效的工具。     

Ⅱ型糖尿病人与大鼠红细胞拉曼光谱学比较

使用激光共聚焦拉曼光谱仪测量正常大鼠红细胞、正常人红细胞、糖尿病STZ造模大鼠红细胞、糖尿病四氧嘧啶造模大鼠红细胞和人Ⅱ型糖尿病红细胞的拉曼光谱,应用主成分分析(principal component analysis,PCA)结合支持向量机(support vector machines,SVM)分类器对数据进行判别分析,然后采用类间距离判断两种造模方法与人Ⅱ型糖尿病的接近程度。结果发现糖尿病红细胞与正常红细胞的拉曼光谱存在明显差异,糖尿病在酰胺 ⅥCO变形振动谱带处峰高显著,并在酰胺ⅤN—H变形振动谱带处谱线出现偏移,属于磷脂的脂酰基C—C骨架1 130 cm-1谱线增强,1 088 cm-1谱线强度减弱,说明糖尿病红细胞膜的通透性增强。PCA结合SVM可以很好地区分以上5类红细胞的拉曼光谱,分类器测试结果表明分类准确度达100%。通过分别计算两种造模方法与人Ⅱ型糖尿病的类间距离,发现STZ造模法更接近人Ⅱ型糖尿病。由此得出结论：拉曼光谱法可以用于糖尿病诊断,大鼠糖尿病STZ造模法更接近人类Ⅱ型糖尿病。     

Confocal Raman microspectroscopy discriminates live human metastatic melanoma and skin fibroblast cells

Confocal Raman microspectroscopy (CRM) continues to develop as a promising technique with possible clinical applications for the diagnosis and treatment of skin cancers. CRM studies of single cells can provide information on the biochemical content of cancer cells in situ, potentially providing new biochemical signatures or markers of cancer cells. Here, we report a CRM study of single, living human metastatic melanoma cells (SK‐Mel‐2) and normal skin fibroblast cells (BJ) cultured and examined under identical experimental conditions. A total of almost 1200 Raman spectra were measured from more than 120 BJ and SK‐Mel‐2 cells using an inverted microscope with 647 nm laser excitation. Raman spectra were measured from within three distinct intracellular regions of the cells – cytoplasm, nucleoplasm, and nucleolus. When Raman spectra from each cell type were compared using principal components analysis (PCA) and linear discriminant analysis with leave‐one‐dish‐out cross‐validation (LDA‐CV), the two cell types were discriminated with 93% (cytoplasm), 98% (nucleolus), and 96% (nucleoplasm) accuracy. The main biochemical differences identified between the two cell types were higher RNA levels in the nucleoli of BJ cells and high amounts of lipid and collagen in the cytoplasm of SK‐Mel‐2 cells. For both cell types, higher levels of RNA were detected in the nucleoli versus the nucleoplasm. PCA with LDA‐CV was 98% (cytoplasm), 93% (nucleoplasm), and 73% (nucleolus) accurate in identifying the intracellular region based on the Raman spectra from both cell types. No significant trend was observed when the data were analyzed with respect to cell passage number. Thus, CRM with PCA and LDA‐CV successfully discriminated two skin cancer‐relevant cell lines while detecting different amounts of nucleic acids, lipids, and proteins in distinct intracellular regions, further underscoring its potential as a clinical diagnostic tool. Copyright © 2013 John Wiley & Sons, Ltd.     

Raman spectroscopic analysis differentiates between breast cancer cell lines

Breast cancer incident rates are increasing in women worldwide with the highest incidence rates reported in developing countries. Major breast cancer screening approaches like mammography, ultrasound, clinical breast examination (CBE) and magnetic resonance imaging (MRI) are currently used but have their own limitations. Optical spectroscopy has attained great attention from biomedical researchers in recent years due to its non‐invasive and non‐destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. Raman spectroscopy, a vibrational spectroscopic approach, has been used to explore the chemical fingerprints of different biological tissues including normal and malignant types. This approach was used to characterize and differentiate two breast cancer and one normal breast cell lines (MDA‐MB‐436, MCF‐7 and MCF‐10A) using dispersive Raman spectroscopy. Raman spectra of the cell lines have revealed that basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and protein Raman peaks were found to differ in intensity, and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. We have shown that a combination of Raman spectroscopy and chemometrics are capable of differentiation between breast cancer cell lines. These variations may be useful in identifying new spectral markers to differentiate different subtypes of breast cancer although this needs confirmation in a larger panel of cell lines as well as clinical material. Copyright © 2015 John Wiley & Sons, Ltd.     

Raman spectral signatures of mouse mammary tissue and associated lymph nodes: normal,tumor and mastitis

Raman spectroscopy involves the interaction of light with the molecular vibrations and therefore can provide information about molecular structure, tissue composition and changes in its environment. We explored whether Raman spectroscopy can reliably distinguish mammary tumors from normal mammary tissues and other pathological states in mice. We analyzed a large number of Raman spectra from the tumor and normal mammary glands of mice injected with 4T1 tumor cells, which were collected using a high‐resolution (less than 4 cm⁻¹) Raman spectrometer at a fixed (785 nm) laser excitation wavelength and with 60 mW of laser power. The spectra of normal and tumor mammary glands showed consistent differences in the intensity of certain Raman bands and loss of some bands in the tumor spectra. Multivariate statistical methods—principal component analysis (PCA) and discriminant functional analysis (DFA)—were used to separate the data into different groups of mammary tumors, mastitis, lymph nodes contralateral and tumor‐cell‐injected sides, and normal contralateral and tumor‐cell‐injected sides. We demonstrate that this spectroscopic technique has the feasibility of discriminating tumor and mastitis from normal tissues and other pathological states in a short period of time and may detect tumor transformation earlier than the standard histological examination stage. Copyright © 2006 John Wiley & Sons, Ltd.     

Simultaneous Raman micro–spectroscopy of optically trapped and stacked cells

The combination of Raman spectroscopy and optical trapping holds great promise for single‐cell studies and is an emergent theme in microfluidic environments. Here, the evolution of the Raman signal intensity with an axial increment of the mass of the substance of interest inside a specific Raman excitation volume is investigated. Whilst Raman spectroscopy may be applied to tissue samples, solutions and single cells, there are no easily available methods to rapidly acquire signals from small cell populations. We show a simple but powerful method to record the Raman intensity signal simultaneously from a small number of trapped cells or colloidal particles using the technique of optical stacking. The Raman spectra of stacks of red blood cells and yeast cells show that this method can be applied to biological systems. We demonstrate how we may reveal biochemical fingerprints that would otherwise require long integration times for each single cell or averaging over many sequentially acquired cell spectra. There is potential to apply this method to directly attain Raman spectra from sorted sub‐populations of normal, abnormal and tumour cell lines. Copyright © 2007 John Wiley & Sons, Ltd.     

膀胱肿瘤离体组织的拉曼光谱学研究

使用激光共聚焦显微拉曼光谱仪测取膀胱肿瘤和正常膀胱组织的拉曼特征谱,应用主成分分析/支持向量机(principal component analysis,PCA/support vector machines,SVM)分类器对数据进行判别分析,最后使用弃一交叉验证法(leave-one-out cross validation,LOOCV)测试判别结果的准确度。结果发现膀胱肿瘤组织与正常膀胱组织的拉曼光谱存在明显差异,肿瘤组织在782和1 583cm-1等核酸特征谱带处峰高显著增强,而正常组织在1 061,1 295,2 849,2 881cm-1等蛋白质和脂质特征谱带处峰高显著增强。PCA/SVM可良好区分膀胱肿瘤组织和正常膀胱组织的拉曼光谱,LOOCV测试分类器显示肿瘤诊断的敏感度86.7%、特异度87.5%、阳性预测值92.9%、阴性预测值77.8%。由此得出结论:拉曼光谱可以良好诊断膀胱肿瘤的体外组织,展现了优越的临床应用前景。     

大鼠的发炎白细胞的喇曼光谱研究

为了研究大鼠处于正常状态和发炎状态下白细胞内部的物质是否发生变化,制备了35%总体表面积Ⅲ度烫伤导致全身炎症的模型,采用一个激发波长为780 nm的半导体激光束来囚禁和激发正常与发炎两种状态下单个白细胞的喇曼光谱.结果显示,正常状态和发炎状态下的白细胞喇曼光谱有显著的区别,主要在726、785、935、1093、1371和1657 cm~(-1)处发炎白细胞的峰值强度比正常白细胞明显高出很多;通过光谱指认表明,当机体发炎时,白细胞中的蛋白质氨基酸含量基本没有变化,但是蛋白质结构发生明显改变;核酸的碱基含量增加,DNA双螺旋结构发生改变.利用PCA主成份对单个白细胞的喇曼光谱进行分析,发现通过PCA可以完全区分出正常状态和发炎状态的白细胞.