Binjun Yan

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

Rationale: In clinical diagnosis of liver injury, which is an important health concern, serum aminotransferase assays have been the go‐to method used worldwide. However, the measurement of serum enzyme activity has limitations, including inadequate disease specificity and enzyme specificity. Methods: With the high selectivity and specificity provided by nano liquid chromatography–tandem mass spectrometry (LC/MS/MS), this work describes a method for the simultaneous determination of six proteins in liver that can be potentially used as biomarkers for liver injury: glutamic‐pyruvic transaminase 1 (GPT1), glutamic oxaloacetic transaminase 1 (GOT1), methionine adenosyl transferase 1A (MAT1A), glutathione peroxidase 1 (GPX1), cytokeratin 18 (KRT18) and apolipoprotein E (APOE). Results: In validation, the method was shown to have good selectivity and sensitivity (limits of detection at pg/mL level). The analytical method revealed that, compared with normal mice, in carbon tetrachloride‐induced acute liver injury mice, liver MAT1A and GPX1 were significantly lower ( p < 0.01 and p < 0.05, respectively), KRT18 was significantly higher ( p < 0.05) and APOE and GPT1 were marginally significantly lower ( p between 0.05 and 0.1). This is the first work reporting the absolute contents of GPT1, GOT1, MAT1A, GPX1 and KRT18 proteins based on LC/MS. Conclusions: The proposed method provides a basis for establishing more specific diagnostic indicators of liver injury.

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

Chemometrics is an important emerging discipline with unique charm formed by the intersection of mathematics, statistics, chemistry and computer science. The application of chemometrics in the field of pharmacy has injected fresh blood into the scientific research and clinical practice of medicine and has provided a sufficient scientific basis for drug analysis and content determination to solve the problem of cancer treatment with combined therapy in different ranges. This paper introduces the basic principles, advantages and disadvantages of several commonly used pattern recognition and multidimensional correction methods of chemometrics, reviews the application of chemometrics for efficiency enhancement and toxicity reduction in cancer treatment with combined therapy and summarizes its development and prospects in the future.

Paeoniae Radix Alba (PRA), an herbal drug produced from the root of Paeonia lactiflora Pall., is widely used in many herbal medicine prescriptions/preparations. Since the pharmacological effects of PRA come from multiple chemical components, it is important to establish a method for the determination of those components in PRA extracts with simple operation and low cost, which is more suitable to evaluate the quality of PRA extracts and optimize the extraction process. This work introduced the quantitative analysis of multicomponents with a single-marker (QAMS) method for the simultaneous determination of eleven bioactive components in PRA extracts, including gallic acid, oxypaeoniflorin, catechin, albiflorin, paeoniflorin, ethyl gallate, galloylpaeoniflorin, pentagalloylglucose, benzoic acid, benzoylpaeoniflorin, and paeonol. In the QAMS method established based on high performance liquid chromatography with diode array detection, only the reference substance of paeoniflorin was needed, and the other ten components were determined based on their relative correction factors (RCFs) to paeoniflorin. Moreover, the repeatability and robustness of the RCFs were studied with different column temperatures, detection wavelengths, flow rates, column types, and instruments. In method validation, good linearity (r>0.999), stability, repeatability (RSD<1.9), and accuracy (recoveries within 96.1105.5) were shown. Sample analyses showed that the QAMS method was consistent with the conventional external standard method. The established method provided a comprehensive, efficient, and low-cost tool for the routine quality evaluation of PRA extracts.

Herbal medicine (HM) formulae are the combinations of two or more types of constituting herbs. This study has proposed a novel approach to efficiently develop HPLC methods for HM formulae, which take advantage of the mutual retention rules between HM formulae and their constituting herbs. An HM formula composed of two herbs, Radix Salviae Miltiorrhizae and Rhizoma Chuanxiong, was taken as a case study. Based on design of experiments and stepwise multiple linear regression, models relating the analytical parameters to the chromatographic parameters were built (correlation coefficients >0.9870) for chemical compounds in the two herbs. These models representing the retention rules were utilized to predict the elution profile of the formula. The analytical parameters were numerically optimized to ensure adequate separation of the analytes. In validation experiments, satisfactory separations were achieved without any pre-experiments on the formula. The approach can significantly increase the HPLC method development efficiency for HM formulae.

Summary: Varying material particle sizes represent a common contributing factor to the batch‐to‐batch variation of extraction yields. To increase the batch‐to‐batch quality consistency of extracts, a method was proposed to adjust the extraction conditions for different material particle sizes, taking the hydrodistillation extraction process of Radix Curcumae and Fructus Gardeniae as a case study. Statistical models were built for five sesquiterpenes including curcumenone, curcumenol, curdione, curzerenone and furanodienone, to quantitatively describe the effects of particle size and process parameters on their extraction yields. An increase in sodium chloride concentration remarkably increased the yields of curcumenone and curcumenol, and a larger solvent‐to‐solid ratio increased the yields of five sesquiterpenes. Under the adjusted process parameters for two different particle sizes, the yield for each compound was controlled to fall in the 90–110% target range. The proposed method can be applied to various extraction processes of foods and herbal medicines.