代谢^®

代谢^®使用统一的工作流程来处理布鲁克的ESI和Maldi成像仪器中的非目标分析，简化步骤的数量并迅速确定并识别生物标志物。

代谢^®can be used across application areas, including discovery metabolomics, lipidomics, phenomics, foodomics, environmental and pharma and provides users with the flexibility to support workflows ranging from basic ID to advanced statistics.

• 代谢^®功能强大的T-Rex算法包括保留时间对齐，除外和特征提取，以确保可靠的数据处理
• 目标化合物可以使用用户定义的分析列表自动注释
• 未知的ID管道，包括库匹配和in silico分裂以促进未知ID
• Visualize relevant information in complex data sets using supervised and non-supervised statistics, including PCA, t-test, ANOVA, PLS and bucket correlation analyses
• 注释质量（AQ）评分提供了五个数据质量指标
• 途径映射以在生物学环境中设置确定的代谢物，从而将数据转换为知识
• 使用局部代谢物预测鉴定药物和异生物代谢产物
• 批次校正以抵消大型样品队列中的样品效应
• 时间序列图随着时间的推移研究代谢物的变化
• 专用的脂肪组学注释工具，包括基于规则的注释，4D Kendrick质量缺陷图和CCSPREDICT
• To simplify the identification of knowns, MetaboScape^®支持Metabobase个人图书馆，HMDB代谢物图书馆，Bruker Sumner Metabobase植物库（包括> 130种化合物的CCS值）以及自定义库
• Customized data export to a file format suitable for import inGNP(Global Natural Products Social Molecular Networking)
• Client-server architecture to enable rapid data processing and multiple users to share methods and access shared datasets
• 半目标工作流程中的代态镜头^®与有针对性的工作流程并驾齐驱，以绝对量化使用TASQ^®

非目标分析的目的是识别特定生理状态或样本特征的特征。由于没有单个工作流程可以访问所有化合物的动态时间和空间指纹，因此需要评估互补平台的数据。代谢^®通过允许评估ESI和MALDI成像的互补数据，并在其生物学背景下自信分配相关标记来解决这些需求。

Identification of unknown compounds is supported through integrated tools, such as molecular formula determination based on accurate mass of precursors and fragments (SmartFormula3D), search of local and public databases (CompoundCrawler),in silico片段化以匹配理论与测量的MS/MS（METFRAG）和MS/MS桶匹配，以鉴定化学相关化合物。

Unknown ID pipeline:
A）SmartFormula3D通过自动匹配准确的质量和同位素模式片段和前体离子信息，将可能的前体分子式可能限制为通常一个或几个候选者。
b）本地和公共数据库中候选公式的查询返回可能的候选结构。
C）In silicofragmentation using implemented MetFrag functionality matches theoretical fragment structures to measured MS/MS peaks and scores most likely structure.
d）可选的MS/MS存储桶匹配使能够分配具有相似MS/MS光谱的化合物，以鉴定进一步的未知但可能在结构上相似的化合物。

药物代谢产物的鉴定不仅引起了药物研究的极大兴趣，而且对代谢组学，现象学，外博学和非目标筛查工作流的兴趣越来越大。在这里，预计将发生药物或其他异生元的代谢物，例如农药，有毒物质或麻醉品，这可能属于未鉴定的，所谓的黑暗代谢组化合物。

代谢^®supports a local生物转化器¹- 基于液体样品分配这些代谢产物的代谢产物预测，并直接使用空间瘤工作流来直接从组织中。此外，可以使用集成的时间序列图跟踪和半定位时间的时间变化。

^{（[1] Djoumbou-Feunang等人；化学杂志学杂志2019，11：2）。}

Rule based annotation routines in MetaboScape^®enable the identification of lipid species taking into consideration the Lipidomics Standards Initiative (LSI) guidelines. This Lipid Class (LC) annotation tool avoids this risk of over annotation and simplifies the automatic identification of lipid features.

代谢^®可以计算和可视化肯德里克质量缺陷，转弯complex mass spectral information进入组成图和优点的积分聚类基于脂质特异性同源重复单元（例如CH₂）。可自定义的4D Kendrick质量缺陷图允许直观的脂质ID验证。提取特征的各种特征可以在4个维度（X轴，Y轴，颜色尺度和气泡大小）中绘制，从而允许多功能应用。

• 绘图retention time vs m/zreveals theseparation of different lipid classes使用不同的颜色为了different lipid classes。Using this colour coding, you can easily spot annotations with obvious deviation in retention time or CCS relative to the rest of the same lipid class.
• 绘图m/z vs CCS, you can further interrogate lipid data by visualizing trends in CCS observed for lipids with differences in chain length and double bond numbers. These trends can be used to confirm lipid class IDs and canassistin theannotation of unknownsand help toremove false positives。
  
• 用CH可视化Kendrick质量缺陷₂specified as repeating unit allows to quickly investigate lipid species of a selected class for saturation and chain length consistency. In addition, the shown example for lipids annotated as Triacylglycerols (TGs) reveals the expected elution order using reversed phase chromatography, as well as the increasing trends in CCS value making full use of all 4 complementary dimensions.
  

A major requirement of metabolomics and lipidomics analyses is to quickly pinpoint and identify those compounds that change as a result of perturbation or disease. Matching retention time, precursor mass, isotopic pattern and MS/MS spectra are common criteria for accessing confidence in compound annotations. PASEF^®关于timsTOF Pro每秒提供数百个MS/MS事件，在单个分析中导致片段覆盖bob综合客户端app的深度更大。另外，Pasef^®光谱从离子迁移率分离中受益，因此使用直接的迁移率获得了更清洁的MS/MS光谱。每个MS值都与碰撞横截面（CCS）值相辅相成，以衡量分析物的形状，从而进一步提供对ID的信心。

和这个结合SCILS™实验室软件T-Rex²赋予了基于空间的非目标分析，用于处理和注释特征，包括药物代谢物，脂质和聚糖。MAP首次使用T-Rex³的独特组合在空间上进行了MAP分析，并将其与CCS感知的化合物注释相结合，以使使用MALDI成像在TimStof Flex系统上获得的化合物获得更高的置信度注释。

无色谱MRMS轴向工作流程提供更高的样品处理量的省略-consuming chromatography in phenomics research. Compounds are accessible that are not readily detectable by LC-MS analysis, allowing targeted and non-targeted metabolomics approaches. The data extraction by T-ReX 2D in MetaboScape® provides confidence in automatic annotation of the FIA MRMS data. The novel scimaX MRMS system can show its extreme performance with mass resolutions of >1 million and mass accuracies of <0.2 ppm. The ultra-high resolving power enables you to utilze isotopic fine structure for the unambiguous determination of elemental composition. This adds another layer of confidence for compound ID in non-targeted metabolomics.