This work was supported by the New College of Interdisciplinary Arts and Sciences at Arizona State University, Applied Maths NV, and by the National Science Foundation (ROA Supplement to Award No. MCB0604300). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

注意 ：在任何环境不明的细菌可能是致病的，必须使用适当的生物安全协议，谨慎处理。与活的文化工作，必须在II类生物安全柜使用生物安全2级（BSL-2）的程序进行。有关BSL-2程序的更多信息，请在CDC / NIH手册中标题为“生物安全微生物和生物医学实验室，”33-38页。该文件可在网上<a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a> 。适当的个人防护装备（PPE），包括实验室大衣/袍，护目镜和腈或乳胶手套，必须佩戴。标准的微生物实践和注意事项必须遵循的，和生物危险废物，必须进行适当的丢弃。 在这个演示中使用细菌分离卡切内岩洞，AZ，USA，从4的环境中，包括干钟乳石，流石，湿润钟乳石和钟乳石滴（ 表1）。所有分离物的16S rDNA序列鉴定，并保持在-80℃下在25％的甘油，R 2 B培养基。所有实验均在室温下完成。 注：我们推荐使用相同的样品制备方法获得大规模光谱数据库建设和未知的质谱。样品制备方法以前已证明影响光谱质量和再现性3。使用不同的样品制备方法可能会导致不正确的识别未知的，特别是当更高分类分辨率（ 例如 ，在应变水平）是期望的。 1.沉积在MALDI靶 注意 ：几个协议，以获得蛋白质提取物需要使用的酸和有机溶剂必须按照GUID被利用包含在各自的材料安全数据表（MSDS）elines和信息。合适的PPE必须佩戴并且将异基于化学品的使用的类型和量（ 例如 ，实验室外套/服，手套，安全眼镜，和呼吸保护必须用显著量的有毒的，易燃的溶剂，如乙腈中工作时使用的，和腐蚀性酸，如甲酸和三氟乙酸）。 <ol><li>存1微升不含存活细胞到不锈钢MALDI靶盘，并允许它以干燥（使用适当的，先前所描述的协议11-13得到的）的蛋白质提取物。覆盖干燥的蛋白质提取物与1μl的基质溶液（α氰基-4-羟基肉桂酸溶液），并允许其干燥。 </li><li>对于每一个生物重复，临场技术重复（5〜20技术复制）的适当数量。在这里，发现10技术复制的每个生物重复和3个生物学重复瓦特ERE准备。 注意：我们建议使用蛋白提取的样品制备方法时，使用抛光不锈钢MALDI靶板。使用地面钢靶板可能引起扩散和不同样品的无意混合单个样品井之外。 </li><li>存1微升校准标准到目标板，并让其干燥。覆盖用1微升基质溶液，并允许它干燥。 </li><li>存2微升基质溶液到目标板作为阴性对照。 </li></ol> 2.质谱采集<ol><li>使用MALDI-TOF质谱仪装有氮激光器（λ= 337纳米），并使用布鲁克FlexControl软件操作。 </li><li>由500激光投100拍摄的增量积累收集每个质谱正线性模式。设置在离子源1电压20千伏;离子源2的电压为18.15 kV的;和透镜电压，以9.05千伏。需要注意的是，这些参数是乐器特定FIC，可能需要其他手段的调整，以获得最佳的效果。 </li><li>从每次充电2至20 kDa的设定批量荷范围自动频谱评估。使用质心的峰值检测算法。定在100大的最小分辨率阈值。在2阈值100设定的最小强度阈值：将信号噪声比（N S）。 </li></ol> 3.数据库建设<ol><li>数据库设计<ol><li>创建BioNumerics 7.1一个新的数据库使用“新建数据库向导”。 </li><li>创建谱的实验类型， 例如，的Maldi，在“实验类型”面板中使用的命令。 </li><li>创建使用“数据库设计面板”的水平。使用添加了新的水平，在“数据库”菜单中的“等级&gt;添加新的水平...”命令。在这里，打造“种”的水平，“生物重复”水平“和”技术复制“的水平，respectiv伊利。 </li></ol></li><li>导入和预处理原质谱<ol><li>导出原始质谱为使用FlexAnalysis在“文件”菜单中单击“导出&gt;质谱”命令.txt文件。 </li><li>导入原料质谱（.txt文件）转换为数据库中的技术重复的水平。 </li><li>预处理原料质谱。 <ol><li>导入和重采样（使用二次拟合算法）。 </li><li>执行基线减法（与滚动光盘50点的尺寸）。 </li><li>计算噪音[连续小波变换（CWT），流畅（Kaiser窗以20分的窗口大小和10点测试），并执行第二基线减法（滚动光盘200点大小）。 </li><li>检测峰[CWT具有最小的信噪比（S：N）10]。 </li></ol></li><li>预处理后，保存每个质谱的特征模式，例如含峰峰列表大小，峰强度，S：N 等 ，在数据库中。 </li></ol></li><li>创建复合质谱<ol><li>创建从“分析”菜单中的“总结...”命令预处理光谱复合光谱。选择“生物重复”的目标水平。 </li><li>这里，结合同殖民地的10技术复制质谱产生复合质谱为殖民地，从而导致该菌株在“生物重复”三级复合质谱。 </li><li>在这里，总结一下三复合光谱创建一个复合谱是孤立的“种”的水平。 注意：合成频谱是逐点平均的技术重复的。具有相似性（Pearson相关），以低于95％（缺省设置）的平均复制被排除在复合材料。上的复合光谱峰仅称为如果它们存在于75％（在包括重复的默认设置）。对于生物学重复，这些设置分别为90和60％，分别为。 </li></ol></li></ol> 4.质谱数据分析<ol><li>选择在数据库中的条目，并创建由“比较”面板中单击“创建新的比较”命令比较。 </li><li>这里，可使用质谱在“技术复制”和/或“生物复制”的水平，显示的比较和分析。 </li><li>基于相似性的聚类分析和多维标度（MDS）的<ol><li>创建颜色组。选择三个生物复合质谱，然后单击“创建新组的选择”命令，在“组”菜单中创建一个组对应的分离。指定自动用于这三种质谱一种颜色。 </li><li>另外，定义域指出使用的命令，相应的颜色“数据库条目“面板，使得在此基础上定义的字段中的任何分组使用该组中定义的相同的颜色。 </li><li>进行聚类分析。在“群集”菜单中单击“计算聚类分析”命令。在比较设置页1，选择“Pearson相关”，离开其他参数为默认值。在第2页，选择“UPGMA”。然后单击“完成”。 </li><li>获得“统计”菜单中的MDS阴谋使用“多维尺度...”命令。 </li></ol></li><li>峰匹配<ol><li>在“实验”面板点击谱型“的Maldi”。然后选择“布局&gt;显示图像”。光谱被示为凝胶带。 </li><li>请在“光谱”菜单中的“做峰匹配”命令峰匹配。 </li></ol></li><li>峰值类鉴定<ol><li>执行主成分分析（PCA）。突出“实验MALDI“在实验型”，“面板，并使用”主成分分析...“命令，在”统计“菜单中执行PCA。 </li><li>执行双向聚类。点击“比较”窗口中的“统计&gt;矩阵挖掘”......。的匹配峰值类的峰的强度使用不同的颜色（热图）表示。 </li></ol></li></ol> 5，细菌鉴定与自定义数据库<ol><li>相似系数为基础的方法<ol><li>创建一个比较和按步骤4.3.3描述生成基于质谱在“技术复制”级树状图。保存树状的相似性比较。 </li><li>选择一个未知的质谱，并单击“分析&gt;确定入选作品”。出现在鉴定对话框。 </li><li>选择“比较基础”分类型（或存储classifIER），然后点击“下一步”。在接下来的页面中，选择保存的树状图作为参考比较，然后单击“下一步”。 </li><li>选择“基本相似性”作为一种识别方法，然后单击“下一步”。 </li><li>选择“最大的相似性”的评分方法。键入适当的阈值和最低值的差值为每个参数，然后单击“下一步”。 </li><li>一旦计算完成，将出现识别窗口。在“结果”面板中，数据库的成员最符合未知列。 </li><li>保存该项目鉴定和验证使用“交叉验证分析”命令识别，在“识别项目”面板。 </li></ol></li><li>潜在的生物标志物为基础的方法<ol><li>定义峰值类。在“黑客帝国矿业”窗口中，选择组峰具有共同的特点，并确定这些峰作为特定使用FIC峰值类（潜在生物标志物）“谱&gt;管理高峰类的类型......”中的“比较窗口”。 </li><li>在这里，定义特定的每个隔离所有15株峰值类。 </li><li>选择未知数的质谱和匹配这些光谱的峰到峰定义的类，如前所述。 </li></ol></li></ol>

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Authors Vranckx and Janssens are employees of Applied Maths NV, the manufacturer of data analysis software used in this video. Applied Maths NV provided select software modules highlighted in this video as well as a portion of the publication costs associated with this video.

该演示展示了表征和鉴定用MALDI-TOF MS和一个自定义数据库细菌的详细过程。相较于传统的分子生物学方法，例如，16S rDNA序列分析，MALDI-TOF MS的基于指纹的方法促进更快速鉴定多样的细菌。因为它的耐用性，这种技术被广泛用于从环境和临床环境1,14-16表征细菌，病毒，真菌和酵母。此外，MALDI-TOF MS已经报道，得到，在一些情况下，更高分类分辨率1。例如，B。藻。 A，B，D和...

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been shown to be a rapid and reliable tool for identification of bacteria at the genus, species, and in some cases, strain levels1-4. MALDI-TOF MS ionizes biological molecules (typically proteins) that originate from cell surfaces, intracellular membranes, and ribosomes from bacterial whole cells or protein extracts1,5. The resulting peaks form characteristic patterns or &#8220;fingerprints&#8221; of the bacteria analyzed1. Identification of bacteria is based on these mass-to-charge &#8220;fingerprints&#8221;. 
Two of the most commonly used identification strategies are library-based and bioinformatics-based strategies1. Library-based approaches involve comparing the mass spectra of unknowns to previously collected mass spectra of known bacteria in databases/libraries for identification. Commercially available software, such as BioNumerics, Biotyper, and SARAMIS software packages, as well as open source software tools, such as SpectraBank6, are available to facilitate the comparison and quantification of similarity between mass spectra of unknowns and reference bacteria. Bioinformatics-based approaches usually rely on fully sequenced genomes of bacteria for identification. In contrast to library-based approaches which do not involve identification of the biological nature of particular peaks, bioinformatics-based approaches involve protein identification1. 
The majority of recent MALDI fingerprint-based studies have used library-based approaches to identify bacteria1. Library-based approaches require construction of databases and comparison of the similarity between mass spectra. Studies show that many experimental procedures, such as medium3,7, cultivation time8, sample preparation method3, and matrix used9, affect the mass spectra obtained. Furthermore, some closely-related species and strains generate spectra with only subtle differences. Thus, library-based approaches require rigorously standardized procedures to generate highly reproducible mass spectra between replicates. Minor variations in protocols may compromise the efficacy of identification, especially at the subspecies and strain levels1,3,10. However, neither manufacturer-provided reference databases nor reported custom databases include visually documented procedures for database construction and/or application of a data analysis pipeline. For this reason, the objective of this work was to develop, apply, and demonstrate a comprehensive and detailed procedure for library-based bacterial identification using MALDI-TOF MS. 
In this demonstration, mass spectra of 15 bacteria isolated from a karstic environment (Kartchner Cavern, AZ, USA) were collected and imported into software to construct a model database. Data processing and the analysis pipeline were detailed using the model database. Finally, mass spectra of blind-coded bacteria which were randomly selected from these 15 bacteria were collected again and compared to the reference spectra in the model database for identification. Results show that bacteria can be correctly identified either based on similarity coefficients or potential biomarkers/peak classes.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>&#945;-cyano-4-hydroxy-cinnamic acid</td>
			<td>ACROS Organics</td>
			<td align="right">163440050</td>
			<td>&#8805; 97%, CAS 28168-41-8</td>
		</tr>
		<tr>
			<td>MALDI calibration kit</td>
			<td>Aigma-Aldrich</td>
			<td>MSCAL1-1KT</td>
			<td>This kit also contains acetonitrile, trifluoroacetic acid , sinapinic acid, etc.</td>
		</tr>
		<tr>
			<td>MALDI target plate</td>
			<td>Bruker Daltonics</td>
			<td align="right">280800</td>
			<td>Polished Steel</td>
		</tr>
		<tr>
			<td>Bruker Microflex LRF MALDI-TOF mass spectrometer</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bruker FlexControl software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bruker FlexAnalysis software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bionumerics software</td>
			<td>Applied Maths</td>
			<td></td>
			<td>version 7.1</td>
		</tr>
	</tbody>
</table>

use of maldi-tof mass spectrometry and a custom database to characterize bacteria indigenous to a unique cave environment (kartchner caverns, az, usa)

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, strain levels. Commercially available and open source software tools have been developed to facilitate identification; however, no universal/standardized data analysis pipeline has been described in the literature. Here, we provide a comprehensive and detailed demonstration of bacterial identification procedures using a MALDI-TOF mass spectrometer. Mass spectra were collected from 15 diverse bacteria isolated from Kartchner Caverns, AZ, USA, and identified by 16S rDNA sequencing. Databases were constructed in BioNumerics 7.1. Follow-up analyses of mass spectra were performed, including cluster analyses, peak matching, and statistical analyses. Identification was performed using blind-coded samples randomly selected from these 15 bacteria. Two identification methods are presented: similarity coefficient-based and biomarker-based methods. Results show that both identification methods can identify the bacteria to the species level.

在此演示构建的数据库有四个层次，从最高到最低的水平，其中包括“各级”，“物种”，“生物重复”和“技术复制”，分别为（ 图1A）。 “技术复制”级别包含的技术复制所有的预处理光谱。在“生物重复”和“物种”的水平包含复合（摘要）光谱。 “各级”包含了所有的技术重复谱以及所有的复合光谱。 频谱聚合过程使用的是有代表性的峰， 如图1...

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Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA

This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented.

使用MALDI-TOF质谱和自定义数据库的表征细菌土著一个独特的洞穴环境（卡切内岩洞，AZ，USA）

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, ...

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Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

13.9K 次观看.  Arizona State University. This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented. 

视频: Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA