Why Panome Bio? Our Technology & Advantages

Why Metabolomics?

Metabolomics quantifies the abundance of diverse small molecules present within biological systems. The small molecules captured in a metabolomics analysis range from central carbon metabolites that provide a footprint of the metabolic landscape of a sample to exposure compounds (cosmetics, toxins, dietary metabolites, etc.) that contextualize the environmental factors that may drive metabolic alterations. As metabolites are downstream of genetic, transcriptional, and translational layers of regulation, metabolomics provides a metabolic readout that is closest to phenotype. This yields a powerful opportunity to find not only predictive biomarkers, but to also leverage the enormous collection of biochemical knowledge assembled over the last 100+ years to rapidly translate metabolite alterations into actionable biological hypotheses.

Pyramid displaying 'omics technology in order of phenotypic relevalance. From greatest to least, metabolomics, proteomics, transcriptomics, genomics.
Chart showing typical significant metabolites in an assay. Pie graph highlighting metabolites versus noise in a metabolomics assay.

Panome Bio’s Advantages


While tremendously powerful, metabolomics data is complex. Within a single sample, tens-of-thousands of signals are detected from the raw data. However, recent work has shown that the vast majority of these signals correspond to noise rather than unique biological compounds. Without careful analysis, metabolomics data can lead to erroneous conclusions and wasted resources.

At Panome Bio, we utilize state-of-the-art liquid chromatography-mass spectrometry assays and data processing to give you the highest quality and most interpretable metabolomics data. Rather than just matching against predefined metabolite libraries, our untargeted analysis scans the raw data for all metabolite signals present and matches those signals against comprehensive small molecule databases to provide an unbiased and global view of metabolism. To ensure that we provide the highest-quality results, the processed data is curated and summarized into an interpretable report that gives you both a high-level view of your data and highlights specific metabolite alterations found in your study.

From Biomarker Discovery to Validation

The metabolomics platform of Panome Bio is specifically designed to facilitate high-throughput biomarker discovery. As metabolomics provides a high-dimensional readout of metabolic phenotype, numerous metabolites have been identified as biomarkers for diseases ranging from cancer to COVID-19. Further, as metabolomics is still a young discipline compared to other ‘omics fields, the universe of metabolite biomarkers has just barely been explored.

At Panome Bio, our biomarker discovery pipeline consists of broad metabolite profiling accomplished through multiple complementary liquid chromatography-mass spectrometry assays. We then use sophisticated computation to extract and identify metabolite signals in your raw data. We then normalize and apply rigorous statistical and machine learning-based approaches to elucidate the metabolites most closely associated with your sample groups. Beyond discovery, we also have technology to translate these biomarkers into a targeted metabolomics assays amenable for use on instruments available in clinical laboratories.

Our Services

Flow diagram showing how Panome Bio takes their customers from study design to metabolomics to statistical analysis to a validated, targeted assay.
Personalized Reference Library for Large Datasets

Personalized Reference Library for Large Datasets

An important aspect of having a successful biomarker study is to have sufficient statistical power. As metabolism is highly variable, to identify metabolite biomarkers in biospecimens, large sample numbers are usually required. However, conventional approaches for unbiased, global metabolomics are not amenable to large sample sizes and inaccurate results may be returned when using such approaches. At Panome Bio, we have developed a workflow for large-scale metabolomics analysis that enables arbitrarily large sample sizes to be analyzed without loss of accuracy or coverage.

Our approach is based on the generation of a Personalized Reference Library that characterizes the totality of metabolite signals present in your raw data. This library is tailored to your samples to ensure that the most relevant metabolites to your study are characterized. With the Personalized Reference Library, we can then process the study samples efficiently and accurately. Further, we have developed a machine learning approach that uses quality control samples interspaced throughout data acquisition to remove technical variation from the metabolite profiles and normalize the data for batch effects when samples are run months or even years apart. When performing metabolomics with Panome Bio, you can trust that the data that we provide is of the highest quality, scope, and accuracy currently available.

Protein structure with metabolites represented within the protein.

A Multi-Omic Approach

Proteomics is used to study the structures, functions, and patterns of protein expression in biological systems and is an important tool in understanding the link between proteins and disease. We have the ability to complement your metabolomics data with proteomics to better understand pathways and metabolism through our Panome Proteomics suite. Panome Bio has two species-agnostic discovery proteomics platforms which include a bio-fluid based offering built with the Seer Proteograph™️ XT as well as a separate, tissue-based untargeted mass spectrometry-based approach well suited for all sample types. We also have targeted human and mouse protein panels for absolute quantitation.

Specialized Data Analysis

At Panome Bio, we not only provide raw data, but provide specialized data analysis to maximize the utility and interpretability of your metabolomics data. A subset of our specialized data analysis workflows are listed below:

• Drug-target mapping/dose-response metabolomics
• Longitudinal/time-course analysis
• Pathway analysis
• Multi-omics integration – Proteomics with the SomaScan 7K Assay
• Multi-group, multi-condition analyses
• Metabolic kinetics/flux
• Isotope-tracer analysis

Download an example of our Standard Data Analysis report and let us know how we can help you integrate metabolomics into your next study.

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Example heat map from a personalized data analysis report from Panome Bio.
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