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Web Date: January 2, 2012

Method Cleans Up Cluttered Spectra

Biological NMR: A new way to remove unwanted signals could make analyzing complex biological samples easier
Department: Science & Technology
News Channels: Analytical SCENE, Biological SCENE
Keywords: signal suppression, background subtraction, NMR, metabolomics, metabolite profiling, glucose, mixture analysis
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Structured Subtraction
The typical approach to removing unwanted NMR signals, such as glucose peaks, from spectra (top) involves subtracting the spectrum of pure glucose (red) from the spectrum of a complex sample (blue). Unfortunately, it only removes a fraction of the unwanted signals, leaving behind difficult-to-interpret data (green). The “add to subtract” approach (bottom) yields a 98% reduction in glucose signals.
Credit: Anal. Chem
A schematic demonstrating “add to subtract” approach to eliminating glucose NMR signals.
 
Structured Subtraction
The typical approach to removing unwanted NMR signals, such as glucose peaks, from spectra (top) involves subtracting the spectrum of pure glucose (red) from the spectrum of a complex sample (blue). Unfortunately, it only removes a fraction of the unwanted signals, leaving behind difficult-to-interpret data (green). The “add to subtract” approach (bottom) yields a 98% reduction in glucose signals.
Credit: Anal. Chem

When researchers use nuclear magnetic resonance to analyze the contents of biological samples, signals from a few compounds often swamp out the rest. A new method suppresses the unwanted signals by first boosting them (Anal. Chem., DOI: 10.1021/ac202548n). Its developers say that the method could help researchers analyze low-abundance compounds in patient samples and discover small-molecule biomarkers for disease.

NMR is a powerful tool, says Daniel Raftery of Purdue University, but spectral overlap seriously limits its use with complex samples. Glucose is often a prime offender: Its NMR signal can dominate spectra of biological samples such as blood. The problem is magnified in samples from patients with diabetes, for example, whose urine can contain significantly higher concentrations of glucose than of other biomolecules.

Current methods for cleaning up NMR data, such as 2-D NMR or glucose removal from samples, lengthen the analysis time or risk changing the sample and introducing errors, Raftery says. To reduce glucose signals without compromising resolution, throughput, or accuracy, he and his team developed a technique they call “add to subtract.” The researchers take an NMR spectrum of a biological sample, and then spike the sample with glucose to roughly double its concentration. They then acquire a second spectrum.

They use a computer program to scale up the peaks from the initial spectrum and subtract them from the spectrum of the spiked sample, resulting in a clean, glucose-free spectrum, containing only information for the remaining molecules in the sample. The researchers demonstrated the technique using serum and urine samples from healthy donors. In both samples, they could spot several amino acids that were difficult or impossible to analyze in the unprocessed spectra.

The team is currently working to extend the technique to other types of biological samples, Raftery says. They think that their “add to subtract” idea should also work with other types of spectroscopy.

 
Chemical & Engineering News
ISSN 0009-2347
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