Libin Xu

The identification of unknown analytes remains a persistent challenge in untargeted mass spectrometry-based analyses. Ion mobility–mass spectrometry (IM-MS) has expanded identification capabilities by enabling the use of collision cross section (CCS) as an orthogonal molecular identifier, but experimentally measured CCS values remain sparse relative to the breadth of known chemical space. Machine learning (ML) approaches have emerged as a complementary strategy for CCS prediction. Many existing models, however, suffer from poor generalizability and accuracy across structurally diverse chemical classes. Here, we present CCSBase2, a large, unified CCS database comprising 66,153 measurements assembled by integrating multiple public datasets, along with a classical ML framework for CCS prediction using Morgan count fingerprints. CCSBase2 achieved a mean relative error of 1.73%, median relative error of 1.23%, and root mean squared error of 4.89 Å2 on a held-out test set. CCSBase2 was trained on a consumer-grade Apple Silicon M4 Pro CPU, demonstrating that accurate CCS prediction can be achieved with minimal computational resources while maintaining fair performance when tested against out-of-distribution compounds. The final model is available at https://ccsbase.net/ccsbase2, and the codebase and datasets are freely available on GitHub (https://github.com/libinxulab/ccsbase2).

High-resolution mass spectrometry (HRMS) is the gold-standard technique for comprehensively profiling chemical exposures in complex human matrices, making it a powerful analytical tool for advancing human exposome research. Yet the scarcity of HRMS reference data, including collision cross-section (CCS) measurements from ion mobility-mass spectrometry (IM-MS) and MS/MS fragmentation spectra, hinders confident structural annotation of chemical exposure agents across laboratories. We therefore developed ToxBase, a multidimensional (m/z, retention time, CCS, MS/MS) reference database for over 2,000 chemicals sourced from the U.S. Environmental Protection Agency's ToxCast chemical library. Built via high-throughput liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), the ToxBase database comprises 3,598 precursor ions spanning 2,075 unique compounds with excellent precision (98.5% of compounds display interday CCS RSDs < 1%) and strong cross-platform agreement. A high-quality MS/MS reference library of the fragmented precursors was assembled using targeted data-dependent acquisition and DDARawProcessor, a novel data extraction algorithm. When applied to LC-IM-MS/MS data obtained from human plasma, urine, and fecal samples (n = 20 per matrix), ToxBase rapidly enabled 42 high-confidence (Level 1) identifications. The ToxBase database is freely available and compatible with the open-source MS data processing platform Skyline for vendor-agnostic suspect screening workflows, providing a valuable resource for standardized, large-scale exposome analysis.

High-resolution mass spectrometry is a powerful tool for untargeted analysis. However, in-source fragmentation (ISF) could lead to the misidentification of compounds in untargeted metabolomics or exposomics studies. To prevent misidentification and to strengthen compound identification through MS/MS spectral library matching, we developed IMFrag, a Jupyter notebook-based tool that utilizes structural information gained from ion mobility-mass spectrometry (IM-MS) as an orthogonal technique to differentiate independent precursor ions from fragments formed via ISF. We first examined l-tryptophan, which is an essential amino acid that undergoes extensive fragmentation during electrospray ionization (ESI). IM-enabled data-independent acquisition (IM-DIA) analysis revealed distinct mobility signatures for identical fragment ions formed at different instrument sites, enabling discrimination between ISFs generated prior to the IM drift tube and fragments produced via postmobility collision-induced dissociation. Similar patterns were observed for a structurally diverse collection of small molecules. Additional structural information could also be inferred from the IM-DIA workflow, such as unique dimers, protonation sites, and distinct ion types that were not apparent from LC–MS alone. These insights were shown to be useful when applied to healthy human plasma samples, which served as a more complex and biologically relevant matrix that contained ambiguities, such as coeluting, isobaric candidate structures. Thus, IMFrag was developed as an accessible framework for interrogating MS¹ and post-IM MS² chemical features in untargeted data sets and can be integrated into untargeted analysis pipelines or used to support the development of ISF-derived MS/MS spectral libraries.

The identification of unknown analytes remains a persistent challenge in untargeted mass spectrometry-based analyses. Ion mobility–mass spectrometry (IM-MS) has expanded identification capabilities by enabling the use of collision cross section (CCS) as an orthogonal molecular identifier, but experimentally measured CCS values remain sparse relative to the breadth of known chemical space. Machine learning (ML) approaches have emerged as a complementary strategy for CCS prediction. Many existing models, however, suffer from poor generalizability and accuracy across structurally diverse chemical classes. Here, we present CCSBase2, a large, unified CCS database comprising 62,043 measurements assembled by integrating multiple public datasets, along with a classical ML framework for CCS prediction using Morgan Fingerprint Counts. CCSBase2 achieved a mean relative error of 1.69%, median relative error of 1.23%, and root mean squared error of 4.65 on a held-out test set. All models were trained on a consumer-grade M4 Pro CPU, demonstrating that accurate CCS prediction can be achieved with minimal computational resources while maintaining strong performance when generalized to out-of-distribution compounds. The final model is available at https://ccsbase.net/ccsbase2, and the codebase and datasets are freely available at GitHub.

Benzalkonium chlorides (BACs) are antimicrobial compounds widely used across a variety of settings, and the COVID-19 pandemic has led to significantly increased usage of BAC-containing products. Previous studies in animals have shown that the kidney is the primary site of BAC accumulation, whereas the liver does not accumulate BACs, likely due to efficient hepatic cytochrome P450-mediated metabolism. Thus, we hypothesized that low BAC-metabolizing capacity contributes to the extent of BAC buildup in the kidney and promotes subsequent kidney injury. Using 2D-cultured proximal tubule epithelial cells (PTECs), we found that renal metabolism is insufficient to detoxify BACs. We then assessed BACs' nephrotoxicity using a novel 3D "kidney-on-a-chip" microphysiological system (MPS). The MPS was exposed to C₁₂- or C₁₄-BAC at 100 nM for 1 week. Transcriptomic analysis of the 3D-cultured PTECs reveals several significantly altered biochemical pathways following BAC exposure, including focal adhesion and cholesterol biosynthesis. Additional studies utilizing orthogonal techniques validated the phenotypic changes associated with some of the significantly altered pathways using 2D-cultured PTECs. Together, these findings demonstrate that the poor BAC-metabolizing capacity of PTECs contributes to the nephrotoxicity of BACs.

Glutathione peroxidase 4 (GPX4) is an antioxidant enzyme important for the reduction of toxic lipid peroxide products. Previous studies revealed the importance of mouse Gpx4 in protecting cardiomyocytes from ferroptosis and, subsequently, the development of cardiovascular disease. In this paper, we investigate the transcriptional consequences of cardiac-specific deletion of Gpx4 in mice and compare this response with that observed in human cardiomyopathy. The findings in this study highlight the importance of GPX4 in maintaining both structural and functional stability of the heart and identify key pathway changes resulting from excessive ferroptosis in cardiac tissue. By overlapping common transcriptional programs perturbed in this animal model and human cardiomyopathy, our findings identify putative mechanisms through which ferroptosis contributes to the development and progression of heart disease. These studies may help guide future cardiovascular therapeutics targeting ferroptosis-dependent pathways.

Exposure to mitochondria-inhibiting quaternary ammonium compounds (QACs) raises the risk for human asthma and chronic obstructive pulmonary disease (COPD). As QACs do not penetrate gut or skin well, we hypothesized that pulmonary exposure could (1) account for observed levels of QAC in human blood and (2) contribute to the observed increased risk for airway disease from QAC exposure. Mice were exposed to Benzalkonium Chloride (BAC) and didecyl dimethylammonium chloride (DDAC) by oropharyngeal aspiration. QAC aspiration increased mice lethality and lung injury 100-fold vs oral dosing. DDAC caused more lethality and lung injury than BAC. Both QACs were more lethal to males than to females. Inspired QAC doses that produced significant lung injury produced blood QAC levels of 1–150 nM, overlapping human concentrations. Thus, in mice, aspirated QACs are 100-fold more injurious and lethal than ingested QACs, aspirated DDAC is twice as injurious and lethal as BAC, and QAC inspiration sufficient to injure the lungs produces overlapping blood concentrations with humans. These mouse data support the hypotheses that QAC inspiration is a much more efficient route of corporeal penetration than dermal or gut penetration, producing much more lung injury than those other routes, and that airborne QAC exposure could underlie increased human risk for pulmonary disease.

We present a case in which vancomycin and dalbavancin exposure preceded emergence of nonsusceptibility to dalbavancin, vancomycin, and daptomycin in a strain of methicillin-resistant Staphylococcus aureus in the setting of a left ventricular assist device (LVAD) infection. We characterized 6 related but genetically unique isolates collected over more than 1 year of recurrent therapy and found that the most resistant isolate acquired multiple walK-related mutations, which has been previously implicated in dalbavancin resistance with vancomycin and daptomycin cross-resistance. Using time-kills at subinhibitory exposures, we found that cefadroxil is synergistic with dalbavancin against the susceptible and resistant strains. This is the first report of dalbavancin nonsusceptibly associated with an LVAD infection, but dalbavancin resistance has been documented previously in association with treatment of endovascular infections. Combination therapy with synergistic and orally bioavailable agents like cefadroxil may be a reasonable strategy to enhance activity and possibly diminish emergence of resistance to dalbavancin.

The degree of protection conferred after receiving an oral cholera vaccine (OCV) varies based on age, prior exposure to Vibrio cholerae , and unknown factors. Recent evidence suggests that the microbiota may mediate some of the unexplained differences in oral vaccine responses. Here, we use metagenomic sequencing of the fecal microbiota at the time of vaccination and relate microbial features to immune responses after OCV using a reference-independent gene-level method. We find that the presence of sphingolipid-producing bacteria is associated with the development of protective immune responses after OCV. We test these associations by stimulating human macrophages with Bacteroides xylanisolvens metabolites and find that sphingolipid-containing extracts increase innate immune responses to OCV antigens. Our findings demonstrate a new analytic method for translating metagenomic sequencing data into strain-specific results associated with a biological outcome, and in validating this tool, we identify that microbe-derived sphingolipids impact immune responses to OCV antigens.