Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome

Rozanova, Svitlana; Uszkoreit, Julian; Schork, Karin; Serschnitzki, Bettina; Eisenacher, Martin; Tönges, Lars; Barkovits, Katalin; Marcus, Katrin

doi:10.3390/biom13030491

Cited by 10 publications

(11 citation statements)

References 67 publications

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“…Reporting all steps of the workflow, as well as quality assessment, has become more and more important since the mid-2000s [23,24]. During the following decade, diverse metrics and tools for quality assessment have been proposed [25][26][27]. In silico quality assessment of experimental data is, first and foremost, a relative evaluation of the parameters of the dataset (e.g., comparison of the detected number of peptide ions, charge states or peptide intensities per run).…”

Section: Quality Assessment/experiments Reproducibilitymentioning

confidence: 99%

“…For calculating and visualizing the quality measures of the different raw files, we used an updated version of the MaCProQC tool programmed by us [27]. The workflow was re-written in Python 3.8 [36] and is executed via Nextflow [37].…”

Section: Qc Toolmentioning

confidence: 99%

“…The tool calculates different QC metrics, many of which are based on the paper by Bittremieux et al [38]. In contrast to the earlier version of the tool [27], the new workflow uses Comet version v2022.01.0 [39] as a search engine instead of Mascot. For feature finding, the FeatureFinderCentroided from OpenMS version 2.8.0 [40] is used.…”

Section: Qc Toolmentioning

confidence: 99%

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Observations from the Proteomics Bench

König,

Schork,

Eisenacher

2024

Proteomes

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Many challenges in proteomics result from the high-throughput nature of the experiments. This paper first presents pre-analytical problems, which still occur, although the call for standardization in omics has been ongoing for many years. This article also discusses aspects that affect bioinformatic analysis based on three sets of reference data measured with different orbitrap instruments. Despite continuous advances in mass spectrometer technology as well as analysis software, data-set-wise quality control is still necessary, and decoy-based estimation, although challenged by modern instruments, should be utilized. We draw attention to the fact that numerous young researchers perceive proteomics as a mature, readily applicable technology. However, it is important to emphasize that the maximum potential of the technology can only be realized by an educated handling of its limitations.

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Section: Quality Assessment/experiments Reproducibilitymentioning

confidence: 99%

Section: Qc Toolmentioning

confidence: 99%

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Observations from the Proteomics Bench

König,

Schork,

Eisenacher

2024

Proteomes

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“…The applicability of the generated data for the quantitative analysis was evaluated using the previously described in-house-developed quality control tool MaCProQC [28], implemented into KNIME [29].…”

Section: Label-free Nanolc-ms/ms With Data-dependent Acquisition (Dda)mentioning

confidence: 99%

Changes in the Proteome of Platelets from Patients with Critical Progression of COVID-19

Wolny,

Rozanova,

Knabbe

et al. 2023

Cells

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Platelets, the smallest cells in human blood, known for their role in primary hemostasis, are also able to interact with pathogens and play a crucial role in the immune response. In severe coronavirus disease 2019 (COVID-19) cases, platelets become overactivated, resulting in the release of granules, exacerbating inflammation and contributing to the cytokine storm. This study aims to further elucidate the role of platelets in COVID-19 progression and to identify predictive biomarkers for disease outcomes. A comparative proteome analysis of highly purified platelets from critically diseased COVID-19 patients with different outcomes (survivors and non-survivors) and age- and sex-matched controls was performed. Platelets from critically diseased COVID-19 patients exhibited significant changes in the levels of proteins associated with protein folding. In addition, a number of proteins with isomerase activity were found to be more highly abundant in patient samples, apparently exerting an influence on platelet activity via the non-genomic properties of the glucocorticoid receptor (GR) and the nuclear factor κ-light-chain-enhancer of activated B cells (NFκB). Moreover, carbonic anhydrase 1 (CA-1) was found to be a candidate biomarker in platelets, showing a significant increase in COVID-19 patients.

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“…Many factors from sampling, sample preparation, LC conditions, MS acquisition, and data analysis have been identified as root causes of variability. − However, there might be other unidentified factors that could produce variability as well. Despite using a well QCed procedure, false changes still exist due to the reasons mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Pursuing Impactful Quantitative Proteomics Using QC-Channels in Every Spectrum and Trend-Design in Experiment

Lai,

Qi,

Kovach

et al. 2024

J. Am. Soc. Mass Spectrom.

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False changes discovered by quantitative proteomics reduce the trust of biologists in proteomics and limit the applications of proteomics to unlock biological mechanisms, which suppresses the application of proteomics techniques in the pharmaceutical industry more than it does in academic research. To remove false changes that arise during LC-MS/MS data acquisition, we evaluated the contributions of peptide abundance and number of unique peptides on reproducibility. Lower abundance and only one unique peptide have a higher risk of generating a higher coefficient of variation (CV), resulting in less accurate quantification. However, the abundance of peptides in samples is not adjustable and discarding proteins quantified by only one unique peptide is not a choice either. Indeed, a large percentage of proteins are accurately quantified by only one unique peptide. Therefore, to improve the calculations of the CV, we leverage a new function in PEAKS called QC-channels which enables technical replicates of each spectrum to be evaluated prior to calculation of the CV. While the QC-channels function in PEAKS significantly reduced the false quantification, random false changes still exist due to known or unknown reasons. To address this challenge, we present the idea of Trend-design to track trend changes rather than changes from two points to remove false quantifications and reveal consequential changes responding to a treatment or condition. The idea was confirmed by molecules with different affinity and dose in the current study. The combination of QC-channels and Trend-design enables a more impactful quantitative proteomics to allow unlocking biological mechanisms using proteomics.

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Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome

Cited by 10 publications

References 67 publications

Observations from the Proteomics Bench

Observations from the Proteomics Bench

Changes in the Proteome of Platelets from Patients with Critical Progression of COVID-19

Pursuing Impactful Quantitative Proteomics Using QC-Channels in Every Spectrum and Trend-Design in Experiment

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