Biology subjects

Baker, E.

Publications and source records attributed to Baker, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Using XCiMS to Quantify the Benefits of Ion Mobility Spectrometry for Untargeted Metabolomics

Integrating ion mobility spectrometry (IMS) with liquid chromatography-mass spectrometry (LC-MS) has the potential to improve untargeted metabolomics workflows, but assessing the benefits of IMS has been challenging due to a paucity of software solutions to process raw LC-IMS-MS data in a manner that enables direct comparison with LC-MS data. We developed the XCiMS software to collapse 4D data into pseudo-3D data. Peak detection is then performed in the pseudo-3D space, followed by targeted extraction of 4D signals from 3D-guided regions of interest. Here, we applied XCiMS to estimate the potential of IMS for untargeted metabolomics when analyzing human plasma. First, we assessed the number of metabolites that are uniquely resolved by LC-IMS-MS but not LC-MS. Although as many as 50% of 3D peaks produce multiple 4D peaks when using high-resolution IMS, few represent newly resolved structural isomers. We found that the increase in features due to IMS was mostly caused by charge isomers and ion clusters. Second, we investigated the impact of IMS on metabolite identification. When using a 16-minute chromatographic separation, we found that IMS can decrease the frequency of chimeric MS/MS by about half. We conclude that while IMS adds considerable complexity to untargeted metabolomics data without resolving a large number of additional structural isomers relative to a standard LC-MS experiment, it does facilitate metabolite identification by decreasing chimeric spectra and providing CCS values for library searching.

bioinformatics↗

bioRxiv2026

Multiple roads to swarming: divergent molecular machineries drive the repeated evolution of locusts

Locust swarming, one of nature's most spectacular examples of a repeated emergent polyphenism, has long been suspected to rely on conserved "swarming genes" or shared genomic features. By applying a model-clade approach comparing six species that vary in their degrees of plasticity and collective behavior, we show that the evolution of swarming locusts is not driven by shared genomic features or a universal set of swarming genes. In contrast, we find that this phenomenon evolved through flexible regulatory architectures, in which the degree of behavioral plasticity directly correlates with the total scale of density-responsive gene expression. While different locust species recruit largely non-overlapping gene sets to achieve the same syndrome, these divergent molecular machineries converge on similar higher-level biological functions. Thus, multiple molecular pathways achieve locust swarming, challenging the preconceived notion about the genetic prerequisites to transition from solitary to collective states. Further, we establish that a complex syndrome such as locust swarming emerges through modular regulatory systems that can be amplified, modified, or attenuated across the tree of life.

evolutionary biology↗

bioRxiv2026

An unrecognized host response to microbial exposure resets circadian timing

As ubiquitous features of every natural environment, microbes have profoundly shaped eukaryotic biology throughout evolution. Circadian clocks evolved in all domains of life as central regulators that align physiology with environmental cycles, yet whether they respond directly to microbial signals remains unknown. Here, we demonstrate that evolutionarily diverse microbes potently reset mammalian cellular clocks and can drive phase shifts in plants and algae, indicating cross-kingdom effects of microbes on circadian rhythms. In mammals, exposure to soluble bacterial components distinct from canonical innate immune ligands induces acute PER2 upregulation independently of Bmal1 or nascent transcription. A targeted inhibitor screen and biochemical assays implicate p38 MAPK as a modulator of this response. Taken together, this positions bacterial exposure as a previously unrecognized circadian clock input, revealing a new axis of host-microbe interaction that modulates biological timing at the cellular level.

cell biology↗

bioRxiv2026

OmniBind: Proteome-Wide Promiscuity Predictions for Early-Stage Drug Screening

Off-target binding remains a leading cause of drug attrition, yet no method exists for rapidly quantifying small-molecule promiscuity across the human proteome. Here, we define promiscuity as the mean predicted binding affinity over 15,405 human proteins and derive a specificity score combining target affinity with this proteome-wide distribution. To make this assessment tractable at scale, we introduce OmniBind, a message-passing neural network that predicts promiscuity directly from a SMILES string at about a thousand compounds per second, several orders of magnitude faster than proteome-wide profiling. OmniBind promiscuity scores correlate with experimental binding data near assay reproducibility limits. Ranking candidates by specificity rather than affinity alone improves enrichment of approved drugs across all thresholds tested, an advantage robust to the choice of affinity predictor. OmniBind fills an unoccupied niche in the early-stage screening landscape as a fast, proteome-scale complement to traditional safety panels, with accuracy that will scale as the underlying affinity predictors continue to improve.

molecular biology↗

bioRxiv2026

Bile acid dependent attenuation of toxin mediated disease is independent of colonization resistance against C. difficile

Clostridioides difficile infection (CDI) is a severe antibiotic associated disease and a major cause of morbidity and mortality worldwide. CDI is thought to arise from the loss of protective gut microbes that mediate functions such as secondary bile acid metabolism and nutrient competition, yet the relative contributions of these mechanisms remain unclear. To determine how these processes influence C. difficile growth, virulence, and disease, we performed in vitro and in vivo experiments using two Clostridia strains previously associated with colonization resistance against C. difficile. Neither organism prevented colonization or growth through nutrient competition alone. In contrast, secondary bile acid metabolism significantly reduced toxin-mediated disease in vivo in a strain dependent manner. These findings demonstrate that secondary bile acid modulation is an important component of CDI prevention independent of nutrient competition and suggest that attenuating virulence, in addition to limiting colonization, may represent a key strategy for next-generation CDI therapeutics.

microbiology↗

bioRxiv2025

Regulatory features determine the evolutionary fate of laterally acquired genes in plants

Lateral gene transfer (LGT) is widespread in eukaryotes, including in animals and plants where it can fuel adaptive evolution and innovation. However, the factors that influence the integration and long-term retention of transferred genes remain poorly understood. The pangenome of the grass Alloteropsis has a high turnover of laterally acquired genes, and here we combine expression, methylation and genomic data to identify factors promoting their long-term persistence. Most transferred genes appear to be degenerating, showing lower expression levels and/or greater sequence truncation compared to their vertically inherited homologs. These degenerating genes also show significantly higher levels of DNA methylation, potentially indicating transcriptional silencing. The likelihood of a transferred gene being retained will be influenced by how easily it can be expressed in the recipient genome. In Alloteropsis, putatively functional laterally acquired genes had expression levels significantly more similar to their donor xenolog than to their vertically inherited homolog. This pattern suggests that transferred genes may carry cis-regulatory elements encoded on the fragment of DNA that moves between species, facilitating their expression in the new genomic context. Evolutionary novelty may also increase the likelihood that selection retains a transferred gene. However, only a significant difference in expression level, not sequence divergence, between donor and recipient orthologs is associated with successful lateral gene transfer. Overall, our results show that most transferred genes degrade over time. However, those capable of regulating their own expression are more likely to persist and contribute to long-term evolutionary innovation. Significance StatementLateral gene transfer (LGT) can introduce novel traits into plant genomes, yet most transferred genes are only transient residents and are degenerating, with reduced expression, truncation, and elevated DNA methylation. However, a minority persist and are more likely to resemble their donor counterparts in expression, suggesting co-transfer of cis-regulatory elements. These findings indicate that regulatory compatibility is key to their long-term survival.

evolutionary biology↗

bioRxiv2025

Pollen sterols are highly diverse but phylogenetically conserved

Sterols regulate cell membrane fluidity and are precursors for hormone and secondary metabolite production in plants, but plant sterols also have a critical role as nutrients in herbivores. Here we describe the distribution of 78 different sterols from pollen of 295 UK wildflower taxa and use this data to develop an evolutionary rationale for the diversity of sterols in pollen compared to vegetative tissues. The sterolome was a function of plant lineage and conserved in groups as high as subfamily. Insect herbivores are auxotrophic for sterols, and notably bees can not modify them therefore rely on dietary sources, primarily pollen, to meet their metabolic needs. Most pollen in the present study contained high proportions of {Delta}5 sterols including {beta}-sitosterol, 24-methylenecholesterol and isofucosterol, which are important sterols for bees. The sterols recorded in honey bees occurred in the pollen of only 68% of plant taxa, however, none matched these proportions exactly suggesting they must forage pollen from multiple plant taxa to satisfy their sterol requirements. We conclude that there is evidence for pollen sterol composition being the result of diverse driving forces including plant lineage and pollinator nutritional requirements.

biochemistry↗

bioRxiv2025

Novel PFAS in Alligator Blood Discovered with Non-Targeted Ion Mobility-Mass Spectrometry

Per- and polyfluoroalkyl substances (PFAS) are a large and growing class of chemicals gaining global attention due to their persistence, mobility, and toxicity. Given the diverse chemical properties of PFAS and their varying distributions in water and tissue, monitoring of different matrices is critical to determine their presence and accumulation. Here, we used a platform combining liquid chromatography, ion mobility spectrometry, and high-resolution mass spectrometry for non-targeted analysis to detect and identify PFAS in alligator plasma from North Carolina (5 years, 2018-2022) and Florida (2021 only). Structures for 12 PFAS were elucidated, including 2 novel structures, and an additional 34 known PFAS were detected. Three of these compounds were previously unreported in environmental media. More PFAS were detected in North Carolina alligators and no novel PFAS were detected in Florida gators. Quantitative analysis of 21 of the known PFAS revealed that plasma concentrations did not change over the 5-year study. HighlightsO_LI46 PFAS were detected in blood plasma from North Carolina and Florida alligators using LC-IMS-HRMS C_LIO_LIStructures for 12 previously unreported PFAS were elucidated, including 2 novel structures C_LIO_LICape Fear River alligators had the most types and highest concentrations of PFAS C_LI

pharmacology and toxicology↗

bioRxiv2025

Cutaneous lupus features specialized stromal niches and altered retroelement expression

Cutaneous Lupus is an inflammatory skin disease causing highly morbid inflamed skin and hair loss. In order to investigate the pathophysiology of cutaneous lupus, we performed single-cell RNA and spatial sequencing of lesional and non-lesional cutaneous lupus skin compared to healthy controls. Pathway enrichment analyses of lesional keratinocytes revealed elevated responses to type I interferon, type II interferon, tumor necrosis factor, and apoptotic signaling. Detailed clustering demonstrated unique fibroblasts specific to lupus skin with likely roles in inflammatory cell recruitment and fibrosis. We also evaluated the association of retroelement expression with type I interferons in the skin. We observed increased retroelement expression which correlated with interferon-stimulated genes across multiple cell types. Moreover, we saw elevated expression of genes involved in RIG-I and cGAS-STING pathways, which transduce elevated nucleic acid signals. Treatment of active cutaneous lupus with Anifrolumab reduced RIG-I and cGAS-STING pathways in addition to the most abundant retroelement family, L2b. Our studies better define type I interferon-mediated immunopathology in cutaneous lupus and identify an association between retroelement expression and interferon signatures in cutaneous lupus.

immunology↗

bioRxiv2025

Updated Guidance for Communicating PFAS Identification Confidence with Ion Mobility Spectrometry

Over the last decade, global contamination from per- and polyfluoroalkyl substances (PFAS) has become apparent due to their detection in countless matrices worldwide, from consumer products to human blood to drinking water. As researchers implement non-targeted analyses (NTA) to more fully understand the PFAS present in the environment and human bodies, clear guidance is needed for consistent and objective reporting of the identified molecules. Confidence levels for small molecules analyzed and identified with high-resolution mass spectrometry (HRMS) have existed since 2014; however, unification of currently-used levels and improved guidance for their application is needed due to inconsistencies in reporting and continuing innovations in analytical methods. Here, we (i) investigate current practices for confidence level reporting of PFAS identified with liquid chromatography (LC), gas chromatography (GC), and/or ion mobility spectrometry (IMS) coupled with HRMS and (ii) propose a simple, unified confidence level guidance that incorporates both PFAS-specific attributes and IMS collision cross section (CCS) values. SynopsisUnified and simplified requirements to guide confidence level assignment in non-targeted PFAS identification efforts with ion mobility spectrometry. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/634925v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1b9808dorg.highwire.dtl.DTLVardef@1a6e663org.highwire.dtl.DTLVardef@5f8813org.highwire.dtl.DTLVardef@1dea0d1_HPS_FORMAT_FIGEXP M_FIG For Table of Contents only C_FIG

scientific communication and education↗

bioRxiv2024

Genes involved in protein folding and chromatin organization buffer genetic variation

Mutations are not always phenotypically active or show different effects in different individuals. While the mechanisms underlying this variable relationship between mutations and phenotypes are largely elusive, some specific genes may influence the phenotypic effects of cryptic variation. We employ the toolbox of Saccharomyces cerevisiae to perform a genome-wide screen aimed at identifying these so-called genetic buffer genes. Measuring the fitness of 1.8 million mutated strains identified a small set of evolutionary conserved buffer genes involved in protein folding and chromatin organization, including GIM3, SSA2, HOG1 and FKH2. Deletion of these genes increases the fitness effect of de novo mutations as well as standing genetic variation, with some mutations becoming adaptive. Moreover, losing a buffer gene results in a decline of standing genetic variation. Together, these results reveal a set of conserved genes that influence the phenotypic outcome of mutations and play a role in complex genetics and incomplete penetrance.

evolutionary biology↗

bioRxiv2024

Exosomes Released from Senescent Cells and Circulatory Exosomes Isolated from Human Plasma Reveal Aging-associated Proteomic and Lipid Signatures

Senescence emerged as significant mechanism of aging and age-related diseases, offering an attractive target for clinical interventions. Senescent cells release a senescence-associated secretory phenotype (SASP), including exosomes that may act as signal transducers between distal tissues, propagating secondary or bystander senescence and signaling throughout the body. However, the composition of exosome SASP remains underexplored, presenting an opportunity for novel unbiased discovery. We present a detailed proteomic and lipidomic analysis of exosome SASP using mass spectrometry from human plasma from young and older individuals and from tissue culture of senescent primary human lung fibroblasts. We identified [~]1,300 exosome proteins released by senescent cells induced by three different senescence inducers. In parallel, a human plasma cohort from young (20-26 years) and old (65-74 years) individuals revealed over 1,350 exosome proteins and 171 plasma exosome proteins were regulated when comparing old vs young individuals. Of the age-regulated plasma exosome proteins, we observed 52 exosome SASP factors that were also regulated in exosomes from the senescent fibroblasts, including serine protease inhibitors (SERPINs), Prothrombin, Coagulation factor V, Plasminogen, and Reelin. 247 lipids were identified in exosome samples. Following senescence induction, identified phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins increased significantly indicating cellular membrane changes. Interestingly, significantly changed proteins were related to extracellular matrix remodeling and inflammation, both potentially detrimental pathways that can damage surrounding tissues and even induce secondary senescence. Our findings reveal mechanistic insights and potential senescence biomarkers, enabling a better approach to surveilling the senescence burden in the aging population and offering therapeutic targets for interventions.

cell biology↗

bioRxiv2024

Proteomic and Lipidomic Plasma Evaluations Reveal Biomarkers for Domoic Acid Toxicosis in California Sea Lions

Domoic acid is a neurotoxin secreted by the marine diatom genus, Pseudo-nitzschia, during toxic algal bloom events. California sea lions (Zalophus californianus) are exposed to domoic acid through ingestion of fish that feed on toxic diatoms, resulting in a domoic acid toxicosis (DAT), which can vary from mild to fatal. Sea lions with mild disease can be treated if toxicosis is detected early after exposure, therefore, rapid diagnosis of DAT is essential but also challenging. In this work, we performed multi-omics analyses, specifically proteomic and lipidomic, on blood samples from 31 California sea lions. Fourteen sea lions were diagnosed with DAT based on clinical signs and postmortem histological examination of brain tissue, and 17 had no evidence of DAT. Proteomic analyses revealed three apolipoproteins with statistically significant lower abundance in the DAT individuals compared to the non-DAT individuals. These proteins are known to transport lipids in the blood. Lipidomic analyses highlighted 29 lipid levels that were statistically different in the DAT versus non-DAT comparison, 28 of which were downregulated while only one was upregulated. Furthermore, of the 28 downregulated lipids, 15 were triglycerides, illustrating their connection with the perturbed apolipoproteins and showing their potential for use in rapid DAT diagnoses. SYNOPSISMulti-omics evaluations reveal blood apolipoproteins and triglycerides are altered in domoic acid toxicosis in California sea lions. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/592757v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@b61289org.highwire.dtl.DTLVardef@17ae9f7org.highwire.dtl.DTLVardef@96904eorg.highwire.dtl.DTLVardef@dc299a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Phage-induced efflux down-regulation boosts antibiotic efficacy

The interactions between a virus and its host vary in space and time and are affected by the presence of molecules that alter the physiology of either the host or the virus. Determining the molecular mechanisms underpinning these interactions is paramount for predicting the fate of bacterial and phage populations and for designing rational phage-antibiotic therapies. We study the interactions between stationary phase Burkholderia thailandensis and the phage {Phi}Bp-AMP1. Although heterogeneous genetic resistance to phage rapidly emerges in B. thailandensis, the presence of phage enhances the efficacy of three major antibiotic classes, the quinolones, the beta-lactams and the tetracyclines, but antagonizes tetrahydrofolate synthesis inhibitors. We discovered that enhanced antibiotic efficacy is underpinned by reduced antibiotic efflux in the presence of phage. This new phage-antibiotic therapy allows for eradication of stationary phase bacteria, whilst requiring reduced antibiotic concentrations, which is crucial for treating infections in sites where it is difficult to achieve high antibiotic concentrations.

microbiology↗

bioRxiv2023

Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

bioinformatics↗

bioRxiv2023

Dysregulated expression of Hoxa1 isoforms in hematopoietic stem and progenitor cells causes myelodysplastic syndromes.

The homeobox gene, Hoxa1, has two different isoforms generated by alternative splicing: a full-length homeodomain-containing Hoxa1 (Hoxa1-FL), and a truncated Hoxa1 (Hoxa1-T), that lacks the homeodomain. The effects of the distinct Hoxa1 isoforms in hematopoiesis have not been investigated. Oncoretroviral studies revealed that Hoxa1-T acts in a dominant negative manner, regulating transcriptionally active Hoxa1. Oncoretroviral overexpression of wildtype Hoxa1 (WT-Hoxa1), which generates both Hoxa1 isoforms, in murine hematopoietic stem and progenitor cells (HSPCs) perturbed hematopoiesis, resulting in transplantable myelodysplastic syndromes (MDS) in mice. Overexpression of a mutated Hoxa1 cDNA (MUT-Hoxa1) that generates Hoxa1-FL, but not Hoxa1-T, led to a more severe MDS that transformed to secondary acute myeloid leukemia (sAML). DNA damage repair pathways were downregulated in Hoxa1-overexpressing hematopoietic progenitor cells, accompanied by increased {gamma}H2AX foci. In silico analyses revealed that CD34+ cells from approximately 50% of patients with MDS had elevated HOXA1-FL expression. Conditional knock-in WT-Hoxa1 and MUT-Hoxa1 mice were generated and had features of pre-MDS, developing altered hematopoiesis within 4 months of Hoxa1 isoform overexpression in HSPCs. HSPCs were significantly reduced in all knock-in mice, accompanied by significantly increased apoptosis in WT-Hoxa1 HSPCs. Healthy wildtype recipients transplanted with bone marrow cells from Hoxa1 knock-in mice developed trilineage MDS, with Hoxa1 isoform and gene dosage dependent phenotypes. Collectively our data identify a role for HOXA1 in the pathogenesis of MDS. Our Hoxa1 mouse models capture different stages of progression of disease from pre-MDS to MDS to sAML and provide novel, clinically relevant tools to study MDS. Key pointsHOXA1 is upregulated in approximately 50% of MDS patient CD34+ BM cells, highlighting a potential role for HOXA1 in the pathogenesis of MDS. Dysregulated expression of Hoxa1 isoforms in murine hematopoietic stem and progenitor cells predisposes mice to pre-MDS and MDS.

cancer biology↗

bioRxiv2022

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in non-targeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas phase collision cross section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable and Reusable) data principles. Another unique aspect of CCSP 2.0 its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73 and 1.87% for the 170 [M-H]-, 155 [M+H]+ and 138 [M+Na]+ adducts tested. For class-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a {Delta}CCS threshold of 2.8% and a mass error of 10 ppm.

biochemistry↗

bioRxiv2021

Uncovering Xenobiotics in the Dark Metabolome using Ion Mobility Spectrometry, Mass Defect Analysis and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, excretion, and co-existence with other endogenous molecules however greatly complicate nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites and xenobiotics is often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate the use of ion mobility spectrometry coupled with MS (IMS-MS) and mass defect filtering in a xenobiotic structural annotation workflow to reduce large metabolomic feature lists and uncover potential xenobiotic classes and species detected in the metabolomic studies. To evaluate the workflow, xenobiotics having known high toxicities including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were examined. Initially, to address the lack of available IMS collision cross section (CCS) values for per- and polyfluoroalkyl substances (PFAS), 88 PFAS standards were evaluated with IMS-MS to both develop a targeted PFAS CCS library and for use in machine learning predictions. The CCS values for biomolecules and xenobiotics were then plotted versus m/z, clearly distinguishing the biomolecules and halogenated xenobiotics. The xenobiotic structural annotation workflow was then used to annotate potential PFAS features in NIST human serum. The workflow reduced the 2,423 detected LC-IMS-MS features to 80 possible PFAS with 17 confidently identified through targeted analyses and 48 additional features correlating with possible CompTox entries.

biochemistry↗