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MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

ABSTRACT: Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs).In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.

SUBMITTER: Ammar C

PROVIDER: S-EPMC6731086 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

Ammar Constantin C Gruber Markus M Csaba Gergely G Zimmer Ralf R

Molecular & cellular proteomics : MCP 20190624 9

Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only i ...[more]

PMID: 31235637

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Project description:Exposure to natural food contaminants during infancy may influence health consequences later in life. Hence, breast milk may serve as a vehicle to transport these contaminants, including mycotoxins, from mothers to their infants. Analytical methods mostly focused on single exposures in the past, thus neglecting co-occurrences and mixture effects. Here, we present a highly sensitive multi-biomarker approach by a sophisticated combination of steps during sample preparation including a Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) extraction followed by a solid phase extraction (SPE) cleanup and utilizing stable isotopes for compensating challenging matrix effects. The assay was validated in-house, reaching limits of detection (LOD) for all 34 analytes in the range of 0.1 to 300 ng/L with satisfying extraction efficiencies (75-109%) and stable intermediate precisions (1-18%) for most analytes. Compared to a similar multi-mycotoxin assay for breast milk, LOD values were decreased by a factor of 2-60x enabling the assessment of chronic low-dose exposures. The new method was applied to a small set of Nigerian breast milk samples (n = 3) to compare results with already published data. Concentration levels of samples that were found to be contaminated before could be confirmed. In addition, other mycotoxins were determined in all three samples, for example the newly investigated alternariol monomethyl ether (AME) was found for the first time in this biological fluid at concentrations up to 25 ng/L. Moreover, in a pooled Austrian sample obtained from a milk bank, trace amounts of multiple mycotoxins including AME (1.9 ng/L), beauvericin (5.4 ng/L), enniatin B (4.7 ng/L), enniatin B1 (<LOQ), ochratoxin A (<LOQ) and the estrogenic zearalenone (<LOQ) confirmed co-occurrence and exposure even in a country with high food safety standards. In conclusion, the method facilitates the determination of mycotoxins at ultra-trace levels in breast milk, enabling the generation of occurrence data necessary for comprehensive co-exposure assessment.

Dataset Information

MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

Publications

MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

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