代谢^®

代谢^®uses a unified workflow to process non-targeted analyses from Bruker's ESI & MALDI Imaging instruments, simplifying the number of steps and rapidly pinpointing and identifying biomarkers.

代谢^®可以跨应用领域使用，包括发现代谢组学，脂肪组学，现象学，食品，环境和制药，并为用户提供支持从基本ID到高级统计数据等工作流的灵活性。

• 代谢^®’s powerful T-ReX algorithm comprises retention time alignment, deisotoping and feature extraction to ensure robust data processing
• Target compounds can be automatically annotated using user defined Analyte Lists
• 未知的ID管道，包括库匹配和在硅中分裂以促进未知ID
• 使用受监督和无监督的统计数据在复杂数据集中可视化相关信息
• Annotation Quality (AQ) scoring providing five indicators of data quality
• 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
• Identification of drug and xenobiotic metabolites using local metabolite prediction
• 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
• Dedicated lipidomics annotation tools, including rule based annotation, 4D Kendrick mass defect plot and CCSPredict
• To simplify the identification of knowns, MetaboScape^®supports theMetaboBASE Personal Library, HMDB Metabolite Library, the Bruker Sumner MetaboBASE Plant Library (including CCS values for >130 compounds), as well as custom libraries
• 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
• Semi-targeted workflows in MetaboScape^®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。

通过集成工具（例如基于精确的前体和片段质量（smartformula3d），搜索本地和公共数据库（CompoundCrawler），诸如分子公式的确定（CompundCrawler），可以通过集成工具确定识别未知化合物的识别，例如在硅中fragmentation to match theoretical to measured MS/MS (MetFrag) and MS/MS bucket matching for the identification of chemically related compounds.

Unknown ID pipeline:
A) SmartFormula3D limits possible precursor molecular formulea to typically one or a few candidates by automatically matching accurate mass and isotopic pattern fragment and precursor ion information.
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) Optional MS/MS Bucket matching enables to assign compounds with similar MS/MS spectra for identification of further possibly unknown but likely structurally similar compounds.

The identification of drug metabolites is not only of great interest to pharmaceutical research but has gained increasing interest in metabolomics, phenomics, exposomics and non-target screening workflows. Here, metabolites of drugs or other xenobiotics like pesticides, toxics or narcotics are expected to occur, which may belong to the family of unidentified, so-called dark metabolome compounds.

代谢^®supports a localBioTransformer¹-based metabolite prediction for assignment of these metabolism products both from liquid samples and directly from tissue using the SpatialOMx workflow. Additionally, changes in time of these metabolites can be tracked and semi-quantified by using integrated time series plots.

^{([1] Djoumbou-Feunang et al.; Journal of Cheminformatics 2019, 11:2).}

基于规则的注释例程^®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^®can calculate and visualize Kendrick Mass Defects, turningcomplex mass spectral informationinto a组成图和优点的积分聚类based on lipid specific同源重复单元（例如CH₂)。The customizable 4D Kendrick mass defect plot allows for intuitive lipid ID validation. Various characteristics of the extracted features can be plotted in 4 dimensions (x-axis, y-Axis, color scale, and bubble size), allowing versatile applications.

• 绘图保留时间与M/zreveals theseparation of different lipid classesusingdifferent colours为了different lipid classes。使用这种彩色编码,你可以很容易发现notations 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 the未知数的注释并提供帮助remove 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.
  

代谢组学和脂肪组学分析的主要要求是快速查明并确定因扰动或疾病而变化的化合物。匹配保留时间，前体质量，同位素模式和MS/MS光谱是获得复合注释置信度的常见标准。Pasef^®关于timsTOF Proprovides hundreds of MS/MS events per second, resulting in a greater depth of fragment coverage in single analysis. Additionally, PASEF^®spectra benefit from ion mobility separation, therefore cleaner MS/MS spectra are obtained using an on-the-fly mobility filter. Each MS value is complemented with a collisional cross section (CCS) value to give a measure of the shape of the analyte, providing further confidence to ID.

In conjunction withSCiLS™ Lab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provides higher sample throughput by omitting time-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.