Suppress or Not to Suppress … CRAFT It: A Targeted Metabolomics Case Study Extracting Essential Biomarker Signals Directly from the Full 1H NMR Spectra of Horse Serum Samples.
Abstract: : There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. : In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. : We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. : The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well.
Publication Date: 2025-06-10 PubMed ID: 40559411PubMed Central: PMC12194815DOI: 10.3390/metabo15060387Google Scholar: Lookup
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- Journal Article
Summary
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Objective Overview
- This study presents a new, simplified method using CRAFT software to extract key inflammation biomarkers from complex 1H NMR spectra of horse serum without complicated spectral suppression or extensive data processing.
Introduction and Background
- Certain biomarkers in blood, such as lipoprotein NMe signals (GlycA and GlycB) and a composite resonance of phospholipids (SPC), are important indicators of inflammation related to serious diseases like cancer and COVID-19.
- Traditional methods (DIRE, JEDI) involve experimental suppression techniques to isolate these specific signals by removing other spectral features, which can be complex and time-consuming.
CRAFT Methodology
- CRAFT (Complete Reduction to Amplitude-Frequency Table) is a time-domain analysis tool that processes free induction decay (FID) data from NMR to directly quantify selected spectral peaks.
- Instead of suppressing parts of the spectrum or applying multiple data correction steps (baseline correction, phase adjustment), CRAFT extracts peak amplitudes directly from a simple water-suppressed 1D NMR spectrum.
- This method generates output in spreadsheet format, containing ready-to-use integrals of the targeted biomarker peaks for comparative or statistical analysis.
- Eliminating preprocessing steps reduces user bias and facilitates automation, enhancing reproducibility and efficiency.
Application to Horse Serum Samples
- The new approach was tested on horse serum samples to detect inflammation by analyzing biomarkers related to Osteochondritis Dissecans (OCD), a joint disease.
- CRAFT analysis successfully distinguished between horses with and without OCD, demonstrating diagnostic potential through statistical separation of cohorts based on biomarker signals.
Advantages and Implications
- The CRAFT method requires only standard water-suppressed 1D NMR spectral data, making it accessible without extra experimental complexity.
- Its simplicity and direct quantitative outputs enable straightforward integration into diagnostic workflows, including retrospective studies on existing datasets.
- This approach can be extended beyond inflammation markers to other relevant metabolites or biomarkers detectable by NMR.
- Applicable to any biofluid or sample type, the method broadens utility in biomedical research and clinical diagnostics.
Cite This Article
APA
Chen J, Yablon A, Metaxas C, Guedin M, Hu J, Conover K, Simpson M, Ralston SL, Krishnamurthy K, Pelczer I.
(2025).
Suppress or Not to Suppress … CRAFT It: A Targeted Metabolomics Case Study Extracting Essential Biomarker Signals Directly from the Full 1H NMR Spectra of Horse Serum Samples.
Metabolites, 15(6), 387.
https://doi.org/10.3390/metabo15060387 Publication
Researcher Affiliations
- LLP 2024, Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- LLP 2024, Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- LLP 2024, Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- LLP 2024, Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- LLP 2024, Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
- Department of Animal Science, Rutgers University, New Brunswick, NJ 08544, USA.
- Independent Researcher, Howell, NJ 08544, USA.
- Chempacker LLC, San Jose, CA 95135, USA.
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
Conflict of Interest Statement
Author Krish Krishnamurthy was employed by the company Chempacker LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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