MetaboScape^®

MetaboScape^®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.

MetaboScape^®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。

• MetaboScape^®’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
• Unknown ID pipeline including library matching and在硅中fragmentation to facilitate unknown 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
• 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
• Dedicated lipidomics annotation tools, including rule based annotation, 4D Kendrick mass defect plot and 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（全球天然产品社会分子网络）
• 客户端服务器体系结构以启用快速数据处理和多个用户共享方法和访问共享数据集
• 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.

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.

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)在硅中使用实现的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桶匹配使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.

MetaboScape^®支持本地BioTransformer¹-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^®考虑到脂质物质标准倡议（LSI）指南，可以鉴定脂质物种。该脂质类（LC）注释工具避免了过度注释的风险，并简化了脂质特征的自动识别。

MetaboScape^®can calculate and visualize Kendrick Mass Defects, turning复杂的质谱信息into acompositional mapwithinformative clustering of pointsbased on lipid specifichomologous 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.

• Plotting保留时间与M/z揭示分离不同的脂质类usingdifferent coloursfor不同的脂质类。使用此颜色编码，您可以轻松地发现保留时间明显偏差的注释或相对于同一脂质类的其余部分。
• PlottingM/z vs CCS，您可以通过可视化链长和双键差异差异的脂质的CCS的趋势来进一步询问脂质数据。这些趋势可用于确认脂质类ID，并且可以助攻在里面未知数的注释并提供帮助删除误报。
  
• Visualize the Kendrick Mass Defect with CH₂指定为重复单元允许快速研究选定类的脂质物种，以达到饱和度和链长度一致性。此外，所示的脂质的示例被注释为三酰基甘油（TGS），揭示了使用反向相色谱法的预期洗脱顺序，以及CCS价值的增加趋势，从而充分利用了所有4个互补维度。
  

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^®data acquisition on thetimstof 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™ 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 workflow通过省略现象学研究中的耗时色谱法来提供更高的样品吞吐量。可以通过LC-MS分析易于检测到的化合物，从而允许靶向和非靶向代谢组学方法。T-Rex 2D在Metaboscape®中提取的数据为FIA MRMS数据的自动注释提供了信心。新型的Scimax MRMS系统可以表现出其极端性能，质量分辨率> 100万，质量精度<0.2 ppm。超高的分辨能力使您能够使用同位素精细结构来实现元素组成的明确测定。这增加了非靶向代谢组学中复合ID的另一层置信度。