Dataset Information

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise.

ABSTRACT:

Background

Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.

Results

LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples.

Conclusion

The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.

SUBMITTER: Mantini D

PROVIDER: S-EPMC1847688 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Publications

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise.

Mantini Dante D Petrucci Francesca F Pieragostino Damiana D Del Boccio Piero P Di Nicola Marta M Di Ilio Carmine C Federici Giorgio G Sacchetta Paolo P Comani Silvia S Urbani Andrea A

BMC bioinformatics 20070326

<h4>Background</h4>Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence ...[more]

PMID: 17386085

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Project description:MALDI-TOF mass spectrometry (MS) on whole cells enables the detection of different cell types and cell activation. Here, we wondered whether this approach would be useful to investigate the host response in sepsis.Peripheral blood mononuclear cells (PBMCs) from patients with severe sepsis and healthy donors were analyzed with MALDI-TOF MS. PBMCs from healthy donors were also stimulated with lipopolysaccharide, peptidoglycan, CpG oligonucleotides, polyinosinic polycytidylic acid, and with heat-inactivated bacteria. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software and matching scores between each spectrum from patients and averaged spectra from the database were calculated.We show that the MALDI-TOF MS signature of PBMCs from septic patients was specific, compared with healthy controls. As the fingerprints observed in patients may be related to PBMC activation, PBMCs from healthy controls were stimulated with cytokines, soluble Pathogen-Associated Molecular Patterns (PAMPs) and heat-killed bacteria, and we created a database of reference spectra. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. No differences were found between patients with documented infection (n?=?6) and those without bacteriological documentation (n?=?6). The spectra of PBMCs from septic patients matched with those of interferon-?- and interleukin-10-stimulated PBMCs, confirming that sepsis is characterized by both inflammatory and immunoregulatory features. Interestingly, the spectra of PBMCs from septic patients without documented infection matched with the reference spectrum of PBMCs stimulated by CpG oligonucleotides, suggesting a bacterial etiology in these patients.Despite the limits of this preliminary study, these results indicate that the monitoring of functional status of PBMCs in peripheral blood by whole cell MALDI-TOF MS could provide unique opportunities to assess disease progression or resolution in clinical settings.

Dataset Information

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise.

Background

Results

Conclusion

Publications

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise.

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