Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics.

Sample normalization is a crucial step in metabolomics for fair quantitative comparisons. It aims to minimize sample-to-sample variations due to differences in the total metabolite amount. When samples lack a specific metabolic quantity to accurately represent their total metabolite amounts, post-acquisition sample normalization becomes essential.

Early-Life gut inflammation drives sex-dependent shifts in the microbiome-endocrine-brain axis.

Despite recent advances in understanding the connection between the gut microbiota and the adult brain, there remains a wide knowledge gap in how gut inflammation impacts brain development. We hypothesized that intestinal inflammation in early life would negatively affect neurodevelopment through dysregulation of microbiota communication to the brain. We therefore developed a novel pediatric chemical model of inflammatory bowel disease (IBD), an incurable condition affecting millions of people worldwide.

Constructing HairDB to facilitate exposome research using human hair.

This study introduces HairDB, an online database serving as a comprehensive repository of hair-related chemicals for exposome research. HairDB was created via an integrative approach. It first extracted 4,184 unique hair-related chemicals through text mining of over 34 million PubMed abstracts and 5.

An important role for triglyceride in regulating spermatogenesis.

is a powerful model to study how lipids affect spermatogenesis. Yet, the contribution of neutral lipids, a major lipid group which resides in organelles called lipid droplets (LD), to sperm development is largely unknown. Emerging evidence suggests LD are present in the testis and that loss of neutral lipid- and LD-associated genes causes subfertility; however, key regulators of testis neutral lipids and LD remain unclear.

Capture of endogenous lipids in peptidiscs and effect on protein stability and activity.

Compared to protein-protein and protein-nucleic acid interactions, our knowledge of protein-lipid interactions remains limited. This is primarily due to the inherent insolubility of membrane proteins (MPs) in aqueous solution. The traditional use of detergents to overcome the solubility barrier destabilizes MPs and strips away certain lipids that are increasingly recognized as crucial for protein function.

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis.

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (Cl/Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning.

Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism.

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion.

Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics.

The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent.

Knockdown of PTEN promotes colon cancer progression and induces M2 macrophage polarization in the colon cancer cell environment.

Objective: This article aims to study the effect of phosphate and tension homolog deleted on chromosome ten (PTEN) knockdown on colon cancer progression and macrophage polarization in the cancer environment.

Materials And Methods And Results: The expression of PTEN in colon cancer tissues and colon cancer cells was significantly lower than in precancerous tissues or CCD-18Co cells, and the decrease was most evident in SW620 cells. The expressions of phosphate (p)-p38, c-Jun N-terminal kinase (JNK), activator protein 1 (AP-1), B-cell lymphoma-2 (Bcl-2) protein in colon cancer tissues and cells were significantly higher than in precancerous tissues or CCD-18Co cells (P-values < 0.

Addressing big data challenges in mass spectrometry-based metabolomics.

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size.

Adaptive Box-Cox Transformation: A Highly Flexible Feature-Specific Data Transformation to Improve Metabolomic Data Normality for Better Statistical Analysis.

Metabolomic data normality is vital for many statistical analyses to identify significantly different metabolic features. However, despite the thousands of metabolomic publications every year, the study of metabolomic data distribution is rare. Using large-scale metabolomic data sets, we performed a comprehensive study of metabolomic data distributions.

MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities.

Motivation: Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data.

Comprehensive assessment of the diminished statistical power caused by nonlinear electrospray ionization responses in mass spectrometry-based metabolomics.

It seems to be well received that nonlinear electrospray ionization (ESI) distorts the signal distribution in mass spectrometry (MS) analysis, thus leading to diminished statistical power for t-test. However, the exact consequence and possible solutions to this quantitative issue have not been systematically explored. In this work, using a serial diluted urine metabolomics dataset, we demonstrated that over 80% of the metabolic features present nonlinear ESI response patterns, causing either left-skewed or right-skewed MS signal distributions.

Rufinamide (RUF) suppresses inflammation and maintains the integrity of the blood-brain barrier during kainic acid-induced brain damage.

Rufinamide (RUF) is a structurally unique anti-epileptic drug, but its protective mechanism against brain injury remains unclear. In the present study, we validated how the RUF protected mice with kainic acid (KA)-induced neuronal damage. To achieve that, a mouse epilepsy model was established by KA intraperitoneal injection.

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating a problematic number of false positives. In this work, we take advantage of deep learning technology to develop a convolutional neural network (CNN)-based program that can automatically recognize metabolic features with poor EIC shapes, which are of low feature fidelity and more likely to be false.

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data.

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome.

Hair is a unique biological matrix that adsorbs short-term exposures (e. g., environmental contaminants and personal care products) on its surface and also embeds endogenous metabolites and long-term exposures in its matrix.

Recognizing Contamination Fragment Ions in Liquid Chromatography-Tandem Mass Spectrometry Data.

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not.

Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics.

Despite the well-known nonlinear response of electrospray ionization (ESI) in mass spectrometry (MS)-based analysis, its complicated response patterns and negative impact on quantitative comparison are still understudied. We showcase in this work that the patterns of nonlinear ESI response are feature-dependent and can cause significant compression or inflation to signal ratios. In particular, our metabolomics study of serial diluted human urine samples showed that over 72% and 16% metabolic features suffered ratio compression and inflation, respectively, whereas only 12% of the signal ratios represent real metabolic concentration ratios.

Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns.

Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g.

Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics.

The nonlinear signal response of electrospray ionization (ESI) presents a critical limitation for mass spectrometry (MS)-based quantitative analysis. In the field of metabolomics research, this issue has largely remained unaddressed; MS signal intensities are usually directly used to calculate fold changes for quantitative comparison. In this work, we demonstrate that, due to the nonlinear ESI response, signal intensity ratios of a metabolic feature calculated between two samples may not reflect their real metabolic concentration ratios (i.

Self-Assembly of Metallacages into Multidimensional Suprastructures with Tunable Emissions.

Cubic metallacages were arranged into multidimensional (one-, two-, and three-dimensional) suprastructures via multistep assembly. Four new shape-controllable, hybrid metallacages with modified substituents and tunable electronic properties were prepared using dicarboxylate ligands with various substituents (sodium sulfonate, nitro, methoxyl, and amine), tetra-(4-pyridylphenyl) ethylene, and cis-(PEt)Pt(OTf). The as-prepared metallacages were used as building blocks for further assembly.