Liquid chromatography coupled with photodiode array and a multivariate curve resolution - Alternating least square algorithm for identification and quantification of co-eluting impurities in pharmaceutical analysis.

This paper systematically investigated and reported for the first time the identification and quantification of co-eluting impurities as low as 0.05 area% by PDA with i-PDeA II deconvolution software in the LabSolutions Chromatographic Data System (CDS) using an integrated multivariate curve resolution-alternating least squares (MCR-ALS) algorithm with a bidirectional exponentially modified Gaussian (BEMG) model function. The algorithm was able to consistently identify 0.05% impurities when co-eluting with the main component (Rs ≥ 0.8) as well as when co-eluting with another impurity (Rs ≥ 0.5). In the case of two co-eluting impurities from 0.05% to 1% (Rs ≥ 0.5), the quantification error ranged from +10.6% to -16.7%. In the case of an impurity co-eluting with the main component (Rs ≥ 0.8), the quantification error was 4.4-8.9% for 1% impurity and 109-184% for 0.05% impurity. The precision was excellent for the range of 0.05-1.0% impurities with the RSD being 1.4-3.0% for 1% impurity and 4.0-8.7% for 0.05% impurity. The identification rate and quantitation accuracy were not affected by the spectral similarity of the molecules, as comparable results were obtained by analyzing two molecules with low similarity (4,4-difluorobenzophenone and valerophenone) and two molecules with high similarity (diazepam and oxazepam) based on simulated data. This peak resolution by MCR-ALS approach provides fast and robust identification and quantification of co-eluting impurities even when method development efforts do not provide complete separation of the target peaks, and could therefore find a wide range of applications in pharmaceutical and other types of analyses.

Serum immunoglobulin G Fc region N-glycosylation profiling by matrix-assisted laser desorption/ionization mass spectrometry can distinguish breast cancer patients from cancer-free controls.

Herein, we report that breast cancer (BC) patients can be distinguished from cancer-free (NC) controls by serum immunoglobulin G (IgG) crystallizable fragment (Fc) region N-glycosylation profiling using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Recently, there has been much progress in the field of tumor immunology. However, to date, the role and biomarker potential of IgG Fc region N-glycosylation, which affects the function of antibodies, have not been examined in BC. In the present study, we profiled serum IgG Fc region N-glycans in BC patients (N = 90) and NC controls (N = 54) using MALDI-MS. An IgG Fc region N-glycan-based multiple logistic regression model was produced which could distinguish BC patients from NC controls (area under the receiver operative characteristic curve = 0.874). Furthermore, stage 0 patients could also be distinguished using this model. These results suggest that an unknown humoral factor or soluble mediator affects IgGs from the earliest stage of breast cancer, and also suggests that IgG Fc region N-glycosylation may play a role in tumor biology. Although further investigation is required, our findings are the evidence that IgG N-glycan profiling has the potential to be used as a breast cancer biomarker and may provide the insights into tumor immunology.

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry.

BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Mass++: A Visualization and Analysis Tool for Mass Spectrometry.

We have developed Mass++, a plug-in style visualization and analysis tool for mass spectrometry. Its plug-in style enables users to customize it and to develop original functions. Mass++ has several kinds of plug-ins, including rich viewers and analysis methods for proteomics and metabolomics. Plug-ins for supporting vendors' raw data are currently available; hence, Mass++ can read several data formats. Mass++ is both a desktop tool and a software development platform. Original functions can be developed without editing the Mass++ source code. Here, we present this tool's capability to rapidly analyze MS data and develop functions by providing examples of label-free quantitation and implementing plug-ins or scripts. Mass++ is freely available at http://www.first-ms3d.jp/english/ .

A study of boron adsorption onto activated sludge.

Boron adsorption onto activated sludge was investigated using bench-scale reactors under simulated wastewater treatment conditions. Two experiments, continuous flow and batch, were performed. Boron concentrations were determined by means of inductively coupled plasma mass spectrometry. The results of the continuous-flow experiment indicated that a small amount of boron accumulated on the activated sludge and its concentration in the sludge depended on the nature of the biota in the sludge. Freundlich and Langmuir isotherm plots generated using the data from the batch experiment indicated that boron was adsorbed onto rather than absorbed into the sludge. The Freundlich constants, k and 1/n, were determined to be 26 mg/kg and 0.87. These values indicate that activated sludge has a limited capacity for boron adsorption and thus utilization of the excess sludge for farmland may not be toxic to plant at least boron concern.