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Summary Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery. Availability and implementation The MetFlow is freely available on http://metflow.zhulab.cn/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

AbstractReducing monosaccharides and their phosphates are critical metabolites in the central carbon metabolism pathway of living organisms. Variations in their content can indicate abnormalities in metabolic pathways and the onset of certain diseases, necessitating their analysis and detection. Reducing monosaccharides and their phosphates exhibit significant variations in content within biological samples and are present in many isomers, which makes the accurate quantification of reducing monosaccharides and their phosphates in biological samples a challenging task. Various analytical methods such as spectroscopy, fluorescence detection, colorimetry, nuclear magnetic resonance spectroscopy, sensor-based techniques, chromatography, and mass spectrometry are employed to detect monosaccharides and phosphates. In comparison, chromatography and mass spectrometry are highly favored for their ability to simultaneously analyze multiple components and their high sensitivity and selectivity. This review thoroughly evaluates the current chromatographic and mass spectrometric methods used for detecting reducing monosaccharides and their phosphates from 2013 to 2023, highlighting their efficacy and the advancements in these analytical technologies. [ABSTRACT FROM AUTHOR]

Quantum dots are highly fluorescent and photostable, making them excellent tools for imaging. When using these quantum dots in cells and animals, however, intracellular biothiols (such as glutathione and cysteine) can degrade the quantum dot monolayer, compromising function. Here, we describe a label-free method to quantify the intracellular stability of monolayers on quantum dot surfaces that couples laser desorption/ionization mass spectrometry with inductively coupled plasma mass spectrometry. Using this new approach we have demonstrated that quantum dot monolayer stability is correlated with both quantum dot particle size and monolayer structure, with appropriate choice of both particle size and ligand structure required for intracellular stability. [ABSTRACT FROM AUTHOR]

Engineering of nanoparticle surface functionality provides controlled interactions with biomolecules such as cell membrane lipids, proteins and nucleic acids. Concurrently, this surface chemistry control also opens up new avenues for improving mass spectral analyses. In this Minireview, we highlight some of the emerging work that integrates surface-engineered nanoparticles with mass spectrometry to improve the analysis of a wide variety of chemical and biological systems. [ABSTRACT FROM AUTHOR]

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) are abnormal expression in various malignant tumors. Our previous research demonstrated that focally amplified long non-coding RNA (lncRNA) on chromosome 1 (FALEC) is an oncogenic lncRNA in prostate cancer (PCa). However, the role of FALEC in castration-resistant prostate cancer (CRPC) is poorly understood. In this study, we showed FALEC was upregulated in post-castration tissues and CRPC cells, and increased FALEC expression was associated with poor survival in post-castration PCa patients. RNA FISH demonstrated FALEC was translocated into nucleus in CRPC cells. RNA pulldown and followed Mass Spectrometry (MS) assay demonstrated FALEC directly interacted with PARP1 and loss of function assay showed FALEC depletion sensitized CRPC cells to castration treatment and restored NAD+. Specific PARP1 inhibitor AG14361 and NAD+ endogenous competitor NADP+ sensitized FALEC-deleted CRPC cells to castration treatment. FALEC increasing PARP1 meditated self PARylation through recruiting ART5 and down regulation of ART5 decreased CRPC cell viability and restored NAD+ through inhibiting PARP1meditated self PARylation in vitro. Furthermore, ART5 was indispensable for FALEC directly interaction and regulation of PARP1, loss of ART5 impaired FALEC and PARP1 associated self PARylation. In vivo, FALEC depleted combined with PARP1 inhibitor decreased CRPC cell derived tumor growth and metastasis in a model of castration treatment NOD/SCID mice. Together, these results established that FALEC may be a novel diagnostic marker for PCa progression and provides a potential new therapeutic strategy to target the FALEC/ART5/PARP1 complex in CRPC patients. [ABSTRACT FROM AUTHOR]

Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MSE, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics. [ABSTRACT FROM AUTHOR]

Objective. To investigate the site of action of sinapine thiocyanate (ST), following acupoint herbal patching (AHP). Methods. Twenty Wistar rats were randomized into five groups (groups A, B, C,D, and E), and all groups received the same AHP in vivo. Skin samples were excised at 2 h, 4 h, 6 h, 10 h, and 26 h after AHP administration from group A to group E separately and the concentrations of ST in the skin were determined using a liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) method. A pharmacokinetic profile of ST following AHP was performed at the same time in a group of five Wistar rats to detect plasma levels at the same time intervals. Results. The mean ± SD ST concentrations (ng/ml) at 2 h (group A), 4 h (group B), 6 h (group C), 10 h (group D), and 26 h (group E) after AHP administration were 250.01 ± 61.99, 61.01 ± 30.41, 40.12 ± 26.94, 78.66 ± 59.43, and 19.55 ± 18.95, respectively. No ST was detected in rats' plasma samples at the same time points. Conclusions. The site of action of ST following AHP is in the skin. [ABSTRACT FROM AUTHOR]

Introduction: Untargeted metabolomics based on liquid chromatography-mass spectrometry is inevitably affected by batch effects that are caused by non-biological systematic bias. Previously, we developed a novel method called WaveICA to remove batch effects for untargeted metabolomics data. To detect batch effect information, the method relies on a batch label. However, it cannot be used in the scenario in which there is only one batch of data or the batch label is unknown. Objectives: We aim to improve the WaveICA method to remove batch effects for untargeted metabolomics data without using batch information. Methods: We improved the WaveICA method by developing WaveICA 2.0 to remove batch effects for metabolomics data, and provided an R package WaveICA_2.0 to implement this method. Results: The performance of the WaveICA 2.0 method was evaluated on real metabolomics data. For metabolomics data with three batches, the performance of the WaveICA 2.0 method was similar to that of the WaveICA method in terms of gathering quality control samples (QCSs) and subject samples together in principle component analysis score plots, increasing the similarity of QCSs, increasing differential peaks, and improving classification accuracy. For metabolomics data with only one batch, the WaveICA 2.0 method had a strong ability to remove intensity drift and reveal more biological information and outperformed the QC-RLSC and QC-SVRC methods in our study using our metabolomics data. Conclusion: Our results demonstrated that the WaveICA 2.0 method can be used in practice to remove batch effects for untargeted metabolomics data without batch information. [ABSTRACT FROM AUTHOR]

Lipids are an important class of biomolecules, and play many essential functions in biology. Ion mobility-mass spectrometry (IM-MS) has emerged as a promising technology for lipidomics by providing a holistic and multi-dimensional characterization of lipid structures. However, the lipid identification using the multi-dimensional match (i.e., MS1, retention time, collision cross section, and MS/MS spectra) gives multiple lipid candidates, and often over-reports the structural information. Here, we developed a lipid identification strategy that integrated library-based match and rule-based refinement for accurate lipid structural elucidation in IM-MS based lipidomics. The new strategy took the advantage of multi-dimensional information for high-coverage identification, while it also utilized the fragmentation rules to determine the accurate structural information. We demonstrated that the combined strategy accurately determined the lipid structures as lipid species level, fatty acyl level, or fatty acyl position level for different lipid classes in the lipid standard mixture and various biological samples. The combined strategy efficiently reduced the redundancy and improved the accuracy for different lipid classes, and identified a total of 440–960 lipid species in various biological samples. Finally, we performed quantitative lipidomics analysis of NIST SRM 1950 human plasma using IM-MS technology. The measured concentrations of most quantified lipids (>80%) were highly consistent with values reported from other independent laboratories. In summary, the developed lipid identification strategy allowed for the accurate identification of lipid structures, and facilitated accurate lipid quantification in IM-MS based untargeted lipidomics. Image 1 • Developed a lipid identification strategy for ion mobility-mass spectrometry (IM-MS) based untargeted lipidomics. • The integration of library-based match and rule-based refinement improved accuracy of lipid identification. • IM-MS technology enabled accurate lipid structural identification and quantification for lipidomics analysis. [ABSTRACT FROM AUTHOR]

Colon cancer remains one of the most common digestive system malignancies in the World. This study investigated the possible interaction between RAD51 and minichromosome maintenance proteins (MCMs) in HCT116 cells, which can serve as a model system for forming colon cancer foci. The interaction between RAD51 and MCMs was detected by mass spectrometry. Silenced MCM vectors were transfected into HTC116 cells. The expressions of RAD51 and MCMs were detected using Western blotting. Foci forming and chromatin fraction of RAD51 in HCT116 cells were also analyzed. The results showed that RAD51 directly interacted with MCM2, MCM3, MCM5, and MCM6 in colon cancer HTC116 cells. Suppression of MCM2 or MCM6 by shRNA decreased the chromatin localization of RAD51 in HTC116 cells. Moreover, silenced MCM2 or MCM6 decreased the foci forming of RAD51 in HTC116 cells. Our study suggests that the interaction between MCMs and RAD51 is essential for the chromatin localization and foci forming of RAD51 in HCT116 cell DNA damage recovery, and it may be a theoretical basis for analysis of RAD51 in tumor samples of colon cancer patients. [ABSTRACT FROM AUTHOR]

Introduction: The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism. Objectives: We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis. Methods: We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids. Results: Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased. Conclusion: We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis. [ABSTRACT FROM AUTHOR]

Summary Ion mobility—mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches. Availability and implementation The software is freely available at: http://imms.zhulab.cn/LipidIMMS/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract: Background: Methionine dependence is a feature unique to cancer cells, exhibited as inability to grow in a methionine-depleted environment supplemented with homocysteine, the immediate metabolic precursor of methionine. However, the molecular mechanisms by which methionine restriction inhibits cancer cells growth have not been elucidated. The effect of methionine restriction on the protein expression in gastric cancer cells was studied. Methods: SGC7901 cells were treated with M−H+ medium for 5 days, which was followed by analysis of total cellular protein from cells by a combination of 2-DE and MS. Then the differential expressional levels of partially identified proteins were determined by Western blot analysis. Results: The well-resolved, reproducible 2-DE patterns of SGC7901 cells cultured in M+H− or M−H+ medium were established. The 10 differential proteins between pairs of gastric cancer cells SGC7901 cultured either in M+H− medium or M−H+ medium, were identified by MALDI-TOF/TOF MS, and the differential expression levels of 2 identified proteins were confirmed. Conclusion: These data will be valuable for further study of the molecular mechanisms by which methionine restriction induces cell cycle arrest and apoptosis in human gastric cancer. [Copyright &y& Elsevier]