Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data

qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra

High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.

Combined Metabolic and Chemical (CoMetChem) Labeling Using Stable Isotopes-a Strategy to Reveal Site-Specific Histone Acetylation and Deacetylation Rates by LC-MS

Histone acetylation is an important, reversible post-translational protein modification and a hallmark of epigenetic regulation. However, little is known about the dynamics of this process, due to the lack of analytical methods that can capture site-specific acetylation and deacetylation reactions. We present a new approach that combines metabolic and chemical labeling (CoMetChem) using uniformly 13C-labeled glucose and stable isotope-labeled acetic anhydride. Thereby, chemically equivalent, fully acetylated histone species are generated, enabling accurate relative quantification of site-specific lysine acetylation dynamics in tryptic peptides using high-resolution mass spectrometry. We show that CoMetChem enables site-specific quantification of the incorporation or loss of lysine acetylation over time, allowing the determination of reaction rates for acetylation and deacetylation. Thus, the CoMetChem methodology provides a comprehensive description of site-specific acetylation dynamics.