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Wang, C. X.

Publications and source records attributed to Wang, C. X..

12 recordsLinked to original sources

Serum metabolomics reveals signatures associated with physical resilience trajectories from middle to older age

Lifecourse physical resilience is defined by the ability to maintain abilities across multiple domains of physical performance. While the importance of physical resilience in functional independence and mobility disability is clear, studies investigating metabolomic signatures of physical resilience are lacking. Here, we performed untargeted metabolomics on serum samples from a community-based cohort of 237 individuals followed over 28 years, and applied spectral data mining tools to map identified metabolites to health phenotypes from public repositories. We identified metabolites across multiple chemical classes, including acylcarnitines, glutamine conjugates, and phosphocholines, that were differentially associated with physical resilience status. Notably, medium-chain acylcarnitines negatively associated with physical resilience were more frequently observed in disease phenotypes than in healthy individuals. Kynurenine, a tryptophan metabolite linked to age-related functional decline, increased more steeply with age in individuals with low physical resilience. We also found that metabolites of the antihypertensive drug verapamil were associated with physical resilience in a metabolism-dependent manner, differing between oxidative and glucuronidated forms. Together, these metabolic signatures offer a resource for identifying biochemical pathways and biomarkers relevant to physical resilience for healthy aging.

bioinformatics↗

The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics

Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12C/13C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

bioinformatics↗

Serum metabolic signatures of cognitive resilience in a longitudinal aging cohort

Aging is typically accompanied by a progressive decline in cognitive function, yet some individuals maintain exceptional cognitive performance, even across the transition from middle to older age, defining exceptional cognitive resilience. While existing measures of resilience primarily rely on clinical assessments, its molecular determinants and early predictive markers remain poorly understood. Here, we performed untargeted LC-MS/MS profiling of longitudinal serum samples to identify metabolic signatures associated with cognitive resilience, which was established based on cognitive tests conducted over 28 years in a cohort of 237 participants. We observed associations across multiple chemical classes, including carnitines, glutamine conjugates, phosphocholines, as well as diet-and drug-derived metabolites. Chemical class-specific analyses revealed distinct metabolic profiles, including predominantly negative associations of medium-chain acylcarnitines with cognitive resilience, increased accumulation of glucuronide conjugates in individuals with low cognitive resilience, altered metabolism of the antihypertensive drug, metoprolol, and elevated levels of dietary compounds such as piperine and lutein in individuals with high cognitive resilience. By leveraging public metabolomics data, we further contextualized the metabolic signatures with respect to their organ specificity, microbial origin, and disease associations. Collectively, these metabolic features, including several previously underexplored compounds, represent promising candidates for functional characterization in mechanisms of aging biology and provide mechanistic insights into the molecular basis of cognitive resilience. HighlightsO_LISerum metabolite MS/MS features, including acyl carnitines, phosphocholines, and hippuric acid conjugates, are associated with cognitive resilience in 237 individuals transitioning from middle to older age. C_LIO_LIDiet-derived piperine is positively associated with cognitive resilience. C_LIO_LIDifferences in {beta}-blocker drug metabolism, rather than parent drug levels, are associated with cognitive resilience. C_LIO_LIRepository-scale searches for the resilience-associated metabolites reveal organ specificity, microbial contributions, and their presence across multiple disease contexts. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/715122v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1038203org.highwire.dtl.DTLVardef@14ca5f1org.highwire.dtl.DTLVardef@12275fcorg.highwire.dtl.DTLVardef@16ff70c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Synthesis-driven reverse metabolomics reveals 3-hydroxy N-acyl amides as gut microbial molecules

3-hydroxy N-acyl amides are bioactive lipids with reported anti-obesity and glucose-regulating effects, yet they are rarely detected in untargeted metabolomics studies because they are largely absent from existing spectral reference libraries. To address this gap, we synthesized an MS2 spectral resource comprising 436 structurally diverse 3-hydroxy N-acyl amides, spanning 3- to 18-carbon chains with a wide range of amine headgroups such as ornithine, valine, and dopamine. Using a synthesis-driven reverse metabolomics approach, we found 161,626 spectral matches across 54,744 publicly available files in untargeted metabolomics datasets revealing widespread occurrences in biological samples, including human-derived specimens. Of these molecules detected through MS2 spectral matching, 334 represent newly reported biological entities. We further confirmed their presence in human saliva, stool, and skin using retention time and ion mobility measurements. Frequent detection in microbial datasets and validation in communities of human-derived gut bacteria support microbial production. Several metabolites also showed altered abundance in individuals with diabetes mellitus, showing that this lipid class is modulated in human metabolic disease. Together, these findings establish 3-hydroxy N-acyl amides as a distinct and biologically relevant lipid class, and the accompanying MS2 spectral resource will enable their broader recognition and study in untargeted metabolomics data.

microbiology↗

MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.

Bile acids are essential steroids regulating immunity, nutrient absorption, insulin, appetite, and body temperature. Their structural diversity is vast, but due to spectral similarities, MS/MS spectral matching often fails to resolve isomers. This study introduces a proof-of-concept workflow using a mass spectrometry query language filtering tree that distinguishes isomeric bile acids in untargeted LC-MS/MS data. Its application revealed a deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

bioinformatics↗

scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics

Spatial transcriptomics has emerged as a pivotal technology for profiling gene expression of cells within their spatial context. The rapid growth of publicly available spatial data presents an opportunity to further our understanding of microenvironments that drive cell fate decisions and disease progression. However, existing foundation models, largely pretrained on single-cell RNA sequencing (scRNA-seq) data, fail to resolve the spatial relationships among samples or capture the unique distributions from various sequencing protocols. We introduce scGPT-spatial, a specialized foundation model for spatial transcriptomics continually pretrained on our previously published scGPT scRNA-seq foundation model. We also curate SpatialHuman30M, a comprehensive spatial transcriptomics dataset comprising of 30 million spatial transcriptomic profiles, encompassing both imaging- and sequencing-based protocols. To facilitate integration, scGPT-spatial introduces a novel MoE (Mixture of Experts) decoder that adaptively routes samples for protocol-aware decoding of gene expression profiles. Moreover, scGPT-spatial employs a spatially-aware sampling strategy and a novel neighborhood-based training objective to better capture spatial co-localization patterns among cell states within tissue. Empirical evaluations demonstrate that scGPT-spatial robustly integrates spatial data in mulit-slide and multi-modal settings, and effectively supports cell-type deconvolution and contextualized missing gene expression imputation, outperforming many existing methods. The scGPT-spatial codebase is publicly available at https://github.com/bowang-lab/scGPT-spatial.

bioinformatics↗

The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds

N-acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N-acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N-acyl lipid patterns from 2,700 public datasets, identifying 851 N-acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N-acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

bioinformatics↗

Post-death Vesicles of Senescent Bone Marrow Mesenchymal Stromal Polyploids Promote Macrophage Aging and Breast Cancer

Potential systemic factors contributing to aging-associated breast cancer (BC) remain elusive. Here, we reveal that the polyploid giant cells (PGCs) that contain more than two sets of genomes prevailing in aging and cancerous tissues constitute 5-10% of healthy female bone marrow mesenchymal stromal cells (fBMSCs). The PGCs can repair DNA damage and stimulate neighboring cells for clonal expansion. However, dying PGCs in advanced-senescent fBMSCs can form "spikings" which are then separated into membraned mtDNA-containing vesicles (Senescent PGC-Spiking Bodies; SPSBs). SPSB-phagocytosed macrophages accelerate aging with diminished clearance on BC cells and protumor M2 polarization. SPSB-carried mitochondrial OXPHOS components are enriched in BC of elder patients and associated with poor prognosis. SPSB-incorporated breast epithelial cells develop aggressive characteristics and PGCs resembling the polyploid giant cancer cells (PGCCs) in clonogenic BC cells and cancer tissues. These findings highlight an aging BMSC-induced BC risk mediated by SPSB-induced macrophage dysfunction and epithelial cell precancerous transition. SIGNIFICANCEMechanisms underlying aging-associated cancer risk remain unelucidated. This work demonstrates that polyploid giant cells (PGCs) in bone marrow mesenchymal stromal cells (BMSCs) from healthy female bone marrow donors can boost neighboring cell proliferation for clonal expansion. However, the dying-senescent PGCs in the advanced-senescent fBMSCs can form "spikings" which are separated into mitochondrial DNA (mtDNA)-containing spiking bodies (senescent PGC-spiking bodies; SPSBs). The SPSBs promote macrophage aging and breast epithelial cell protumorigenic transition and form polyploid giant cancer cells. These results demonstrate a new form of ghost message from dying-senescent BMSCs, that may serve as a systemic factor contributing to aging-associated immunosuppression and breast cancer risk. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/583755v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@f07b60org.highwire.dtl.DTLVardef@33ef98org.highwire.dtl.DTLVardef@1b720c8org.highwire.dtl.DTLVardef@1c9514d_HPS_FORMAT_FIGEXP M_FIG Xie et al demonstrate that the polyploid giant cells (PGCs) in the juvenile phase expansion of female bone marrow mesenchymal stromal cells (fBMSCs) can boost neighboring cell proliferation for clonal expansion. However, when fBMSCs enter to the advanced-senescent phase, the dying-senescent PGCs form "spikings" which are then separated into membraned vesicles termed Senescent PGC spiking bodies, SPSBs). The SPSBs carrying fragmented mitochondrial elements and OXPHOS proteins can be phagocytosed by macrophage and breast epithelial cells leading to macrophage aging and breast epithelial protumorigenic transition. The SPSBs are demonstrated to be a new form of post-cell death vesicle from aging BMSCs and may serve as a systemic factor contributing to aging-associated immunosuppression and breast cancer risk. C_FIG

cancer biology↗

Overlap and divergence of neural circuits mediating distinct behavioral responses to sugar

A single sensory cue can elicit diverse behavioral responses. For example, the taste of sugar robustly promotes feeding1, 2 but also influences other behaviors, such as altering locomotor patterns to maximize food-finding3, 4 or conferring a rewarding value onto associated contexts or cues.5-7 Here, we investigate how sweet taste elicits multiple appetitive behaviors in Drosophila. Are different sugar-evoked behaviors coordinately regulated? At what point does the sugar circuit diverge into different pathways that drive distinct behaviors? We first established an optogenetic paradigm to study the effects of sugar taste on locomotion, spatial preference, and associative learning. We then tested how different sugar-evoked behaviors were modulated by internal and external factors, including hunger, diet, or the presence of an aversive taste. Different behaviors were generally modulated in similar ways, but we also observed some differences that reveal selective modulation of specific behavioral pathways. Finally, we investigated where the sugar taste circuit diverges into different behavioral pathways. A recent study identified a sensory-motor circuit comprising five layers of neurons that drives the initiation of feeding in response to sugar.8 By individually manipulating each of these neurons, we show that circuits mediating different innate responses to sugar are partially overlapping and begin to diverge at the level of second- and third-order neurons, whereas circuits for innate versus learned behaviors may diverge at the first synapse. Connectomic analyses reveal distinct subcircuits that mediate different behaviors. Together, these studies provide insight into how neural circuits are organized to elicit diverse behavioral responses to a single stimulus.

neuroscience↗

A dedicated hypothalamic oxytocin circuit controls aversive social learning

To survive and thrive in a complex social group, it is essential to not only know who to approach but more importantly who to avoid. After a single defeat, mice learn to stay away from the winning aggressor for weeks. Here, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin receptor expressing cells in the anterior subdivision of ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance learning. After defeat, aVMHvlOXTR cells drastically increase their responses to aggressor cues. This response change is functionally important as optogenetic activation of aVMHvlOXTR cells elicits time-locked social avoidance towards a benign social target whereas inactivating the cells suppresses defeat-induced social avoidance. Furthermore, OXTR in the aVMHvl is itself essential for the behavior change. Knocking out OXTR in the aVMHvl or antagonizing the receptor during defeat, but not during post-defeat social interaction, impairs defeat-induced social avoidance. aVMHvlOXTR receives its private source of oxytocin from SOROXT cells, which are highly activated by the noxious somatosensory inputs associated with defeat. Oxytocin released from SOROXT depolarizes aVMHvlOXTR cells and facilitates their synaptic potentiation, and hence, increases aVMHvlOXTR cell responses to aggressor cues. Ablating SOROXT cells impairs defeat-induced social avoidance learning whereas activating the cells promotes social avoidance after a subthreshold defeat experience. Altogether, our study reveals an essential role of SOROXT-aVMHvlOXTR circuit in defeat-induced social learning and highlights the importance of brain oxytocin system in social plasticity.

neuroscience↗

One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data

1The surge of single-cell RNA sequencing technologies gives rise to the abundance of large single-cell RNA-seq datasets at the scale of hundreds of thousands of single cells. Integrative analysis of large-scale scRNA-seq datasets has the potential of revealing de novo cell types as well as aggregating biological information. However, most existing methods fail to integrate multiple large-scale scRNA-seq datasets in a computational and memory efficient way. We hereby propose OCAT, One Cell At a Time, a graph-based method that sparsely encodes single-cell gene expressions to integrate data from multiple sources without most variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT efficaciously facilitates a variety of downstream analyses, such as differential gene analysis, trajectory inference, pseudotime inference and cell inference. OCAT is a unifying tool to simplify and expedite the analysis of large-scale scRNA-seq data from heterogeneous sources.

genomics↗

Transduction of the Geomagnetic Field as Evidenced from Alpha-band Activity in the Human Brain

Magnetoreception, the perception of the geomagnetic field, is a sensory modality well-established across all major groups of vertebrates and some invertebrates, but its presence in humans has been tested rarely, yielding inconclusive results. We report here a strong, specific human brain response to ecologically-relevant rotations of Earth-strength magnetic fields. Following geomagnetic stimulation, a drop in amplitude of EEG alpha oscillations (8-13 Hz) occurred in a repeatable manner. Termed alpha event-related desynchronization (alpha-ERD), such a response is associated with sensory and cognitive processing of external stimuli. Biophysical tests showed that the neural response was sensitive to the dynamic components and axial alignment of the field but also to the static components and polarity of the field. This pattern of results implicates ferromagnetism as the biophysical basis for the sensory transduction and provides a basis to start the behavioral exploration of human magnetoreception.

biophysics↗