MetaboScape^®

MetaboScape^®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。

MetaboScape^®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.

• MetaboScape^®’s powerful T-ReX algorithm comprises retention time alignment, deisotoping and feature extraction to ensure robust data processing
• 目标化合物可以使用用户定义的分析列表自动注释
• Unknown ID pipeline including library matching andin silicofragmentation to facilitate unknown ID
• Visualize relevant information in complex data sets using supervised and non-supervised statistics, including PCA, t-test, ANOVA, PLS and bucket correlation analyses
• Annotation Quality (AQ) scoring providing five indicators of data quality
• Pathway mapping to set identified metabolites in a biological context, thereby turning data into knowledge
• Identification of drug and xenobiotic metabolites using local metabolite prediction
• Batch correction to offset sample effects in large sample cohorts
• Time series plots to investigate changes in metabolites over time
• 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
• 为了简化已知的识别，代表镜^®supports theMetaboBASE Personal Library, HMDB Metabolite Library, the Bruker Sumner MetaboBASE Plant Library (including CCS values for >130 compounds), as well as custom libraries
• 定制数据导出到适合导入的文件格式GNPS(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^®go hand in hand with targeted workflows for absolute quantification usingtasq^®

The aim of non-targeted profiling is to identify features that are characteristic of a particular physiological state or sample. As there is no single workflow to enable access to the dynamic temporal and spatial fingerprints for all compounds, there is a need to evaluate data from complementary platforms. MetaboScape^®addresses these needs by allowing for the evaluation of complementary data from both ESI and MALDI Imaging, as well as confidently assigning relevant markers in their biological context.

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。

未知ID管道：
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) Query of candidate formula in local and public databases returns possible structure candidates.
C)In silico使用实现的MetFrag功能的碎片将理论片段结构与测量的MS/MS峰和得分最有可能结构相匹配。
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。

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.

MetaboScape^®支持本地BioTransformer¹- 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 et al.; Journal of Cheminformatics 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.

MetaboScape^®可以计算和可视化肯德里克质量缺陷，转弯复杂的质谱信息进入compositional mapwithinformative clustering of points基于脂质特异性homologous repeating units (e.g. 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.

• Plottingretention time vs m/z揭示分离不同的脂质类usingdifferent coloursfor不同的脂质类. 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.
• Plottingm/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 canassist在里面annotation of unknownsand help to删除误报.
  
• Visualize the Kendrick Mass Defect with CH₂specified 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^®data acquisition on thetimstof 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。

In conjunction withSCiLS™ Labsoftware, T-ReX² empowers the SpatialOMx-based non-targeted profiling for processing and annotation of features, including drug metabolites, lipids and glycans. For the first time, map analytes spatially using this unique combination of T-ReX³ and combine it with CCS-aware annotation of compounds to enable a higher confidence annotation of compounds acquired using MALDI Imaging on timsTOF fleX systems.

The chromatography freeMRMS aXelerate workflowprovides 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.