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Estimation of low-level components lost through chromatographic separations with finite detection limits.

ABSTRACT: The search for biomarkers allowing the assessment of disease by early diagnosis is facilitated by liquid chromatography. However, it is not clear how many components are lost due to being present in concentrations below the detection limit and/or being obscured by chromatographic peak overlap. First, we extend the study of missing components undertaken by Enke and Nagels, who employed the log-normal probability density function (pdf) for the distribution of signal intensities (and concentrations) of three mixtures. The Weibull and exponential pdfs, which have a higher probability of small-concentration components than the log-normal pdf, are also investigated. Results show that assessments of the loss of low-intensity signals by curve fitting are ambiguous. Next, we simulate synthetic chromatograms to compare the loss of peaks from superposition (overlap) with neighboring peaks to the loss arising from lying below the limit of detection (LOD) imposed by a finite signal-to-noise ratio (SNR). The simulations are made using amplitude pdfs based on the Enke-Nagels data as functions of relative column efficiency, i.e., saturation, and SNR. Results show that at the highest efficiencies, the lowest-amplitude peaks are lost below the LOD. However, at small and medium efficiencies, peak overlap is the dominant loss mechanism, suggesting that low-level components will not be found easily in liquid chromatography with single channel detectors regardless of SNR. A simple treatment shows that a multichannel detector, e.g., a mass spectrometer, is necessary to expose more low-level components.

SUBMITTER: Devitt NM

PROVIDER: S-EPMC7748966 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Fluorescence-based chelators are commonly used to probe labile low-molecular-mass (LMM) metal pools in the cytosol of eukaryotic cells, but such chelators destroy the complexes of interest during detection. The objective of this study was to use chromatography to directly detect such complexes. Towards this end, 47 batches of cytosol were isolated from fermenting S. cerevisiae yeast cells and passed through a 10 kDa cut-off membrane. The metal contents of the cytosol and resulting flow-through solution (FTS) were determined. FTSs were applied to a size-exclusion LC column located in an anaerobic refrigerated glove box. The LC system was coupled to an online inductively-coupled-plasma mass spectrometer (ICP-MS) for detection of individual metals. Iron-detected chromatograms of cytosolic FTSs from WT cells exhibited 2-4 major species with apparent masses between 500-1300 Da. Increasing the iron concentration in the growth medium 40-fold increased the overall intensity of these peaks. Approximately 3 LMM cytosolic copper complexes with apparent masses between 300-1300 Da were also detected; their LC intensities were weak, but these increased with increasing concentrations of copper in the growth medium. Observed higher-mass copper-detected peaks were tentatively assigned to copper-bound metallothioneins Cup1 and Crs5. FTSs from strains in which Cup1 or the Cox17 copper chaperone were deleted altered the distribution of LMM copper complexes. LMM zinc- and manganese-detected species were also present in cytosol, albeit at low concentrations. Supplementing the growth medium with zinc increased the intensity of the zinc peak assigned to Crs5 but the intensities of LMM zinc complexes were unaffected. Phosphorus-detected chromatograms were dominated by peaks at apparent masses 400-800 Da, with minor peaks at 1000-1500 Da in some batches. Sulfur chromatograms contained a low-intensity peak that comigrated with a glutathione standard; quantification suggested a GSH concentration in the cytosol of ca. 13 mM. A second LMM sulfur peak that migrated at an apparent mass of 100 Da was also evident.

Project description:BackgroundHigh-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.ResultsWe introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a p-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.ConclusionThe proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a p-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.

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Estimation of low-level components lost through chromatographic separations with finite detection limits.

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