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MetaboScape

MetaboScape

Prosigna Score / ROR Score:

The ROR Score has been validated to predict the risk of recurrence of disease in ER+ breast cancer after surgery and treatment with 5 years of endocrine therapy. The ROR score depends upon the biology of the intrinsic subtypes, the proliferation score of the tumor, and the tumor size as shown in the equation below:

ROR = aRLumA + bRLumB + cRHer2 + dRBasal + eP + fT

Each of the R variables in the equation above indicate the Pearson correlation coefficient of the PAM50 expression profile for the tumor compared to each of the prototypical centroids for the intrinsic subtypes shown in the heatmap below. P is the proliferation score, which is the average gene expression profile of genes associated with cell-cycle progression and T is the tumor stage.

Figure: Heatmap of PAM50 genes by subtype. Red is higher expression and green is lower expression

MetaboScape

All-in-one software for compound identification from non-targeted workflows


Identify more with MetaboScape

  • Add confidence to your IDs using annotation quality (AQ) scoring with CCS. Visualize biomarkers using built-in statistical tools and map changing pathways.

  • Utilize the 4th dimension using TIMS to reveal CCS for all your compounds

  • Process large sample cohorts rapidly using MetaboScape’s client-server-based software. Run > 200 samples per day using LC-free MRMS aXelerate.

  • Annotate imaging data with compound information, whilst detecting more compound classes using the innovative and unique MALDI-2 source on the timsTOF fleX.

GeoMx DSP COVID-19 Protein and RNA Analysis

  • Rapidly perform high-plex spatial analyses of the host response in FFPE or fresh frozen tissue using the  GeoMx Digital Spatial Profiler (DSP). NanoString’s GeoMx DSP platform enables high-plex protein and RNA experiments in key areas of biology such as molecular response, cellular (immune) response, tissue damage, and drivers of individual susceptibility to severe forms of disease.

  • The GeoMx COVID-19 Immune Response Atlas, a ~1,850-plex RNA assay, enables spatial studies of the SARS-CoV-2 virus and host response. RNA targets include COVID-19 receptors and proteases, pulmonary alveolar type I and II markers, lung biology markers, viral response markers, and SARS-CoV-2 probes. RNA targets are profiled simultaneously using the GeoMx DSP and an Illumina next-generation sequencer (NGS) for readout. Users can run ACD RNAscope™ probes alongside GeoMx RNA probes to identify regions of interest.

  • A five-antibody custom, ready-to-go protein panel, with receptor, protease, and viral markers is available through the GeoMx Technology Access Program or for order through Abcam. This  COVID-19 GeoMx-formatted Antibody  is run with the 20-plex GeoMx Immune Cell Profiling Core (plus controls) with readout on the nCounter Analysis System. Users can add up to six 10-plex modules including the Immune Activation Status, Immune Cell Typing, and/or Cell Death modules to more deeply profile proteins involved in T cell activation and cell death. NanoString scientists can recommend commercially-available markers for lung epithelium, nasal epithelium, immune response markers, and the viral spike protein.

Powerful T-ReXalgorithm

MetaboScape®’s powerful T-ReXalgorithm comprises retention time alignment, deisotoping, and feature extraction to ensure robust data processing

User-defined Analyte Lists

Target compounds can be automatically annotated using user-defined Analyte Lists

Unknown ID

Unknown ID pipeline including library matching and in silico fragmentation to facilitate unknown ID

Visualize Relevant Information

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)

Annotation Quality (AQ) scoring providing five indicators of data quality

Pathway Mapping

Pathway mapping to set identified metabolites in a biological context, thereby turning data into knowledge

Local Metabolite Prediction

Identification of drug and xenobiotic metabolites using local metabolite prediction

Batch Corrections

Batch correction to offset sample effects in large sample cohorts

Investigate Changes

Time series plots to investigate changes in metabolites over time