Resolving of Overlapping Asymmetrical Chromatographic Peaks by Using Wavelet-Transform and Gram-Charlier Peak Model

The paper describes a method for resolving overlapping asymmetric peaks that make up a chromatogram. The presented method uses the Gram-Charlier model in the form of the first three terms of the Gram-Charlier series as a basis. Using the wavelet transform, the parameters of this model are determined, which is used to describe a single or overlapping chromatographic peak. Hermitian wavelets of the first four orders are used in the computation of the wavelet transform. To speed up the computation of multiple wavelet transforms, the possibility of coding a signal using the Chebyshev-Hermite functions is considered in order to further restore the set of wavelet transforms simultaneously. According to the presented method, the parameters of the peaks are determined by analytical expressions without using the numerical approximation of the chromatogram by the peak model, which avoids the disadvantages of the numerical approach. The resulting method is used to resolve overlapping asymmetric peaks. The advantage of the method over others is shown by calculating the area of each of the resolved peaks.

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in software pipelines for processing LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Unfortunately, random noise, non-baseline separated compounds and unspecific background signals complicate this task.ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box.In independent training and validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (F1 score of 0.99). A comparison of different sets of reference features showed that at least 100 reference features (including isotopologs) should be provided to achieve high-quality results for detecting new chromatographic peaks.PeakBot is implemented in Python (3.8) and uses the TensorFlow (2.4.1) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.AvailabilityThe framework is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/[email protected] informationSupplementary data are available at Bioinformatics online.

Evaluation of different PNGase F enzymes in immunoglobulin G and total plasma N-glycans analysis

Glycoproteins, proteins that are co- and posttranslationally modified by sugars (glycans), have significant roles in pathophysiology of many different diseases. One of the main steps in sample preparation for free N-glycan analysis is deglycosylation or glycan removal. The aim of this study was to compare different peptide N-glycosidase F (PNGase F) enzymes (Rapid PNGase F and two recombinant versions) for deglycosylation of total human plasma glycoproteins and different amounts of human immunoglobulin G (IgG). Deglycosylation with different PNGase F enzymes resulted in different IgG and plasma N-glycosylation hydrophilic interaction liquid chromatography ultra-performance liquid chromatography profiles. Additionally, one recombinant version of PNGase F is more efficient in deglycosylation of complex N-glycans compared with Rapid PNGase F and recombinant version of PNGase F from a different manufacturer. In terms of chromatographic peak intensities and coefficient of variation %Area values, all tested versions of PNGase F enzymes were very reproducible and on the similar level when used in optimal conditions. However, care should be taken in terms of which enzyme is used with which protocol, particularly when scaling up.

Study on chemical constituents of different species of Dendrobium

The objective of this study is to determine the erianin of 10 species of Dendrobium by high performance liquid chromatography (HPLC). Processing techniques were washed, dried and ground. The research method for determining the content of erianin was adopted from Chinese Pharmacopoeia 2015 Edition. Acetonitrile-0.05% phosphoric acid (37:63) was used as mobile phase. The samples were separated on Sharpsil-TC18 column(4.6*150mm;5μm) at a flow rate of 1.2mL/min and detected at 230nm, and the column temperature was kept at 30℃. The injection volume was 20μL. The number of theoretical plates was not less than 6000 according to the chromatographic peak of erianin. The result of the study showed erianin was detected only in Dendrobium chrysotoxum among the 10 species of Dendrobium. and the content was 0.098%. Conclusion: The content of erianin in Dendrobium chrysotoxum met the specification of no less than 0.03% in the 2015 edition of Chinese Pharmacopoeia.

LC-MS analysis of p-aminosalicylic acid under electrospray ionization conditions manifests a profound solvent effect

Under identical mass spectrometric conditions, chromatographic peak intensities of p-aminosalicylic acid recorded by LC-MS, using methanol as the mobile phase are drastically different from those acquired using is it acetonitrile as the eluent.