Soluble ACE2-mediated cell entry of SARS-CoV-2 via interaction with proteins related to the renin-angiotensin system.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause acute respiratory disease and multiorgan failure. Finding human host factors that are essential for SARS-CoV-2 infection could facilitate the formulation of treatment strategies. Using a human kidney cell line-HK-2-that is highly susceptible to SARS-CoV-2, we performed a genome-wide RNAi screen and identified virus dependency factors (VDFs), which play regulatory roles in biological pathways linked to clinical manifestations of SARS-CoV-2 infection. We found a role for a secretory form of SARS-CoV-2 receptor, soluble angiotensin converting enzyme 2 (sACE2), in SARS-CoV-2 infection. Further investigation revealed that SARS-CoV-2 exploits receptor-mediated endocytosis through interaction between its spike with sACE2 or sACE2-vasopressin via AT1 or AVPR1B, respectively. Our identification of VDFs and the regulatory effect of sACE2 on SARS-CoV-2 infection shed insight into pathogenesis and cell entry mechanisms of SARS-CoV-2 as well as potential treatment strategies for COVID-19.

Phosphocholine-induced energy source shift alleviates mitochondrial dysfunction in lung cells caused by geospecific PM2.5 components.

Fine particulate matter (PM2.5) is globally recognized for its adverse implications on human health. Yet, remain limited the individual contribution of particular PM2.5 components to its toxicity, especially considering regional disparities. Moreover, prevention solutions for PM2.5-associated health effects are scarce. In the present study, we comprehensively characterized and compared the primary PM2.5 constituents and their altered metabolites from two locations: Taiyuan and Guangzhou. Analysis of year-long PM2.5 samples revealed 84 major components, encompassing organic carbon, elemental carbon, ions, metals, and organic chemicals. PM2.5 from Taiyuan exhibited higher contamination, associated health risks, dithiothreitol activity, and cytotoxicities than Guangzhou's counterpart. Applying metabolomics, BEAS-2B lung cells exposed to PM2.5 from both cities were screened for significant alterations. A correlation analysis revealed the metabolites altered by PM2.5 and the critical toxic PM2.5 components in both regions. Among the PM2.5-down-regulated metabolites, phosphocholine emerged as a promising intervention for PM2.5 cytotoxicities. Its supplementation effectively attenuated PM2.5-induced energy metabolism disorder and cell death via activating fatty acid oxidation and inhibiting Phospho1 expression. The highlighted toxic chemicals displayed combined toxicities, potentially counteracted by phosphocholine. Our study offered a promising functional metabolite to alleviate PM2.5-induced cellular disorder and provided insights into the geo-based variability in toxic PM2.5 components.

Plasma metabolome and cytokine profile reveal glycylproline modulating antibody fading in convalescent COVID-19 patients.

The COVID-19 pandemic has incurred tremendous costs worldwide and is still threatening public health in the "new normal." The association between neutralizing antibody levels and metabolic alterations in convalescent patients with COVID-19 is still poorly understood. In the present work, we conducted absolutely quantitative profiling to compare the plasma cytokines and metabolome of ordinary convalescent patients with antibodies (CA), convalescents with rapidly faded antibodies (CO), and healthy subjects. As a result, we identified that cytokines such as M-CSF and IL-12p40 and plasma metabolites such as glycylproline (gly-pro) and long-chain acylcarnitines could be associated with antibody fading in COVID-19 convalescent patients. Following feature selection, we built machine-learning-based classification models using 17 features (six cytokines and 11 metabolites). Overall accuracies of more than 90% were attained in at least six machine-learning models. Of note, the dipeptide gly-pro, a product of enzymatic peptide cleavage catalyzed by dipeptidyl peptidase 4 (DPP4), strongly accumulated in CO individuals compared with the CA group. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination experiments in healthy mice demonstrated that supplementation of gly-pro down-regulates SARS-CoV-2-specific receptor-binding domain antibody levels and suppresses immune responses, whereas the DPP4 inhibitor sitagliptin can counteract the inhibitory effects of gly-pro upon SARS-CoV-2 vaccination. Our findings not only reveal the important role of gly-pro in the immune responses to SARS-CoV-2 infection but also indicate a possible mechanism underlying the beneficial outcomes of treatment with DPP4 inhibitors in convalescent COVID-19 patients, shedding light on therapeutic and vaccination strategies against COVID-19.

Technical challenges in defining RNA modifications.

It is well established that DNA base modifications play a key role in gene regulation during development and in response to environmental stress. This type of epigenetic control of development and environmental responses has been intensively studied over the past few decades. Similar to DNA, various RNA species also undergo modifications that play important roles in, for example, RNA splicing, protein translation, and the avoidance of immune surveillance by host. More than 160 different types of RNA modifications have been identified. In addition to base modifications, RNA modification also involves splicing of pre-mRNAs, leading to as many as tens of transcript isoforms from a single pre-RNA, especially in higher organisms. However, the function, prevalence and distribution of RNA modifications are poorly understood. The lack of a suitable method for the reliable identification of RNA modifications constitutes a significant challenge to studying their functions. This review focuses on the technologies that enable de novo identification of RNA base modifications and the alternatively spliced mRNA transcripts.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Fragmentation Pattern-Based Screening Strategy Combining Diagnostic Ion and Neutral Loss Uncovered Novel para-Phenylenediamine Quinone Contaminants in the Environment.

Identifying transformed emerging contaminants in complex environmental compartments is a challenging but meaningful task. Substituted para-phenylenediamine quinones (PPD-quinones) are emerging contaminants originating from rubber antioxidants and have been proven to be toxic to the aquatic species, especially salmonids. The emergence of multiple PPD-quinones in various environmental matrices and evidence of their specific hazards underscore the need to understand their environmental occurrences. Here, we introduce a fragmentation pattern-based nontargeted screening strategy combining full MS/All ion fragmentation/neutral loss-ddMS2 scans to identify potential unknown PPD-quinones in different environmental matrices. Using diagnostic fragments of m/z 170.0600, 139.0502, and characteristic neutral losses of 199.0633, 138.0429 Da, six known and three novel PPD-quinones were recognized in air particulates, surface soil, and tire tissue. Their specific structures were confirmed, and their environmental concentration and composition profiles were clarified with self-synthesized standards. N-(1-methylheptyl)-N'-phenyl-1,4-benzenediamine quinone (8PPD-Q) and N,N'-di(1,3-dimethylbutyl)-p-phenylenediamine quinone (66PD-Q) were identified and quantified for the first time, with their median concentrations found to be 0.02-0.21 µg·g-1 in tire tissue, 0.40-2.76 pg·m-3 in air particles, and 0.23-1.02 ng·g-1 in surface soil. This work provides new evidence for the presence of unknown PPD-quinones in the environment, showcasing a potential strategy for screening emerging transformed contaminants in the environment.

Tissue Accumulation and Biotransformation of 6PPD-Quinone in Adult Zebrafish and Its Effects on the Intestinal Microbial Community.

The pronounced lethality of N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-quinone or 6PPDQ) toward specific salmonids, while sparing other fish species, has received considerable attention. However, the underlying cause of this species-specific toxicity remains unresolved. This study explored 6PPDQ toxicokinetics and intestinal microbiota composition in adult zebrafish during a 14-day exposure to environmentally realistic concentrations, followed by a 7-day recovery phase. Predominant accumulation occurred in the brain, intestine, and eyes, with the lowest levels in the liver. Six metabolites were found to undergo hydroxylation, with two additionally undergoing O-sulfonation. Semiquantitative analyses revealed that the predominant metabolite featured a hydroxy group situated on the phenyl ring adjacent to the quinone. This was further validated by assessing enzyme activity and determining in silico binding interactions. Notably, the binding affinity between 6PPDQ and zebrafish phase I and II enzymes exceeded that with the corresponding coho salmon enzymes by 1.04-1.53 times, suggesting a higher potential for 6PPDQ detoxification in tolerant species. Whole-genome sequencing revealed significant increases in the genera Nocardioides and Rhodococcus after exposure to 6PPDQ. Functional annotation and pathway enrichment analyses predicted that these two genera would be responsible for the biodegradation and metabolism of xenobiotics. These findings offer crucial data for comprehending 6PPDQ-induced species-specific toxicity.

Assessing the Impact of PM2.5-Bound Arsenic on Cardiovascular Risk among Workers in a Non-ferrous Metal Smelting Area: Insights from Chemical Speciation and Bioavailability.

Inhalation of fine particulate matter PM2.5-bound arsenic (PM2.5-As) may cause significant cardiovascular damage, due to its high concentration, long transmission range, and good absorption efficiency in organisms. However, both the contribution and the effect of the arsenic exposure pathway, with PM2.5 as the medium, on cardiovascular system damage in nonferrous smelting sites remain to be studied. In this work, a one-year site sample collection and analysis work showed that the annual concentration of PM2.5-As reached 0.74 µg/m3, which was 120 times the national standard. The predominant species in the PM2.5 samples were As (V) and As (III). A panel study among workers revealed that PM2.5-As exposure dominantly contributed to human absorption of As. After exposure of mice to PM2.5-As for 8 weeks, the accumulation of As in the high exposure group reached equilibrium, and its bioavailability was 24.5%. A series of animal experiments revealed that PM2.5-As exposure induced cardiac injury and dysfunction at the environmental relevant concentration and speciation. By integrating environmental and animal exposure assessments, more accurate health risk assessment models exposed to PM2.5-As were established for metal smelting areas. Therefore, our research provides an important scientific basis for relevant departments to formulate industry supervision, prevention and control policies.

Human Airway Organoids and Multimodal Imaging-Based Toxicity Evaluation of 1-Nitropyrene.

Despite significant advances in understanding the general health impacts of air pollution, the toxic effects of air pollution on cells in the human respiratory tract are still elusive. A robust, biologically relevant in vitro model for recapitulating the physiological response of the human airway is needed to obtain a thorough understanding of the molecular mechanisms of air pollutants. In this study, by using 1-nitropyrene (1-NP) as a proof-of-concept, we demonstrate the effectiveness and reliability of evaluating environmental pollutants in physiologically active human airway organoids. Multimodal imaging tools, including live cell imaging, fluorescence microscopy, and MALDI-mass spectrometry imaging (MSI), were implemented to evaluate the cytotoxicity of 1-NP for airway organoids. In addition, lipidomic alterations upon 1-NP treatment were quantitatively analyzed by nontargeted lipidomics. 1-NP exposure was found to be associated with the overproduction of reactive oxygen species (ROS), and dysregulation of lipid pathways, including the SM-Cer conversion, as well as cardiolipin in our organoids. Compared with that of cell lines, a higher tolerance of 1-NP toxicity was observed in the human airway organoids, which might reflect a more physiologically relevant response in the native airway epithelium. Collectively, we have established a novel system for evaluating and investigating molecular mechanisms of environmental pollutants in the human airways via the combinatory use of human airway organoids, multimodal imaging analysis, and MS-based analyses.

Use of NAD tagSeq II to identify growth phase-dependent alterations in E. coli RNA NAD+ capping.

Recent findings regarding nicotinamide adenine dinucleotide (NAD+)-capped RNAs (NAD-RNAs) indicate that prokaryotes and eukaryotes employ noncanonical RNA capping to regulate gene expression. Two methods for transcriptome-wide analysis of NAD-RNAs, NAD captureSeq and NAD tagSeq, are based on copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry to label NAD-RNAs. However, copper ions can fragment/degrade RNA, interfering with the analyses. Here we report development of NAD tagSeq II, which uses copper-free, strain-promoted azide-alkyne cycloaddition (SPAAC) for labeling NAD-RNAs, followed by identification of tagged RNA by single-molecule direct RNA sequencing. We used this method to compare NAD-RNA and total transcript profiles of Escherichia coli cells in the exponential and stationary phases. We identified hundreds of NAD-RNA species in E. coli and revealed genome-wide alterations of NAD-RNA profiles in the different growth phases. Although no or few NAD-RNAs were detected from some of the most highly expressed genes, the transcripts of some genes were found to be primarily NAD-RNAs. Our study suggests that NAD-RNAs play roles in linking nutrient cues with gene regulation in E. coli.

In situ fabrication of covalent organic frameworks on solid-phase microextraction probes coupled with electrospray ionization mass spectrometry for enrichment and determination of androgens in biosamples.

Solid-phase microextraction (SPME) coupled with electrospray ionization mass spectrometry (ESI-MS) was developed for rapid and sensitive determination of endogenous androgens. The SPME probe is coated with covalent organic frameworks (COFs) synthesized by reacting 1,3,5-tri(4-aminophenyl)benzene (TPB) with 2,5-dioctyloxybenzaldehyde (C8PDA). This COFs-SPME probe offers several advantages, including enhanced extraction efficiency and stability. The analytical method exhibited wide linearity (0.1-100.0 µg L-1), low limits of detection (0.03-0.07 µg L-1), high enrichment factors (37-154), and satisfactory relative standard deviations (RSDs) for both within one probe (4.0-14.8%) and between different probes (3.4-12.7%). These remarkable performance characteristics highlight the reliability and precision of the COFs-SPME-ESI-MS method. The developed method was successfully applied to detect five kinds of endogenous androgens in female serum samples, indicating that the developed analytical method has great potential for application in preliminary clinical diagnosis.

Phospholipid remodeling and its derivatives are associated with COVID-19 severity.

BACKGROUND: Timely medical intervention in severe cases of coronavirus disease 2019 (COVID-19) and better understanding of the disease's pathogenesis are essential for reducing mortality, but early classification of severe cases and its progression is challenging. OBJECTIVE: We investigated the levels of circulating phospholipid metabolites and their relationship with COVID-19 severity, as well as the potential role of phospholipids in disease progression. METHODS: We performed nontargeted lipidomic analysis of plasma samples (n = 150) collected from COVID-19 patients (n = 46) with 3 levels of disease severity, healthy individuals, and subjects with metabolic disease. RESULTS: Phospholipid metabolism was significantly altered in COVID-19 patients. Results of a panel of phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) and of phosphatidylethanolamine and lysophosphatidylethanolamine (LPE) ratios were significantly correlated with COVID-19 severity, in which 16 phospholipid ratios were shown to distinguish between patients with severe disease, mild disease, and healthy controls, 9 of which were at variance with those in subjects with metabolic disease. In particular, relatively lower ratios of circulating (PC16:1/22:6)/LPC 16:1 and (PE18:1/22:6)/LPE 18:1 were the most indicative of severe COVID-19. The elevation of levels of LPC 16:1 and LPE 18:1 contributed to the changes of related lipid ratios. An exploratory functional study of LPC 16:1 and LPE 18:1 demonstrated their ability in causing membrane perturbation, increased intracellular calcium, cytokines, and apoptosis in cellular models. CONCLUSION: Significant Lands cycle remodeling is present in patients with severe COVID-19, suggesting a potential utility of selective phospholipids with functional consequences in evaluating COVID-19's severity and pathogenesis.

PM2.5-induced DNA oxidative stress in A549 cells and regulating mechanisms by GST DNA methylation and Keap1/Nrf2 pathway.

Fine particulate matter (PM2.5) increases the risks of lung cancer. Epigenetics provides a new toxicology mechanism for the adverse health effects of PM2.5. However, the regulating mechanisms of PM2.5 exposure on candidate gene DNA methylation changes in the development of lung cancer remain unclear. Abnormal expression of the glutathione S transferase (GST) gene is associated with cancer. However, the relationship between PM2.5 and DNA methylation-mediated GST gene expression is not well understood. In this study, we performed GST DNA methylation analysis and GST-related gene expression in human A549 cells exposed to PM2.5 (0, 50, 100 µg/mL, from Taiyuan, China) for 24 h (n = 4). We found that PM2.5 may cause DNA oxidative damage to cells and the elevation of GSTP1 promotes cell resistance to reactive oxygen species (ROS). The Kelch-1ike ECH-associated protein l (Keap1)/nuclear factor NF-E2-related factor 2 (Nrf2) pathway activates the GSTP1. The decrease in the DNA methylation level of the GSTP1 gene enhances GSTP1 expression. GST DNA methylation is associated with reduced levels of 5-methylcytosine (5mC), DNA methyltransferase 1 (DNMT1), and histone deacetylases 3 (HDAC3). The GSTM1 was not sensitive to PM2.5 stimulation. Our findings suggest that PM2.5 activates GSTP1 to defend PM2.5-induced ROS and 8-hydroxy-deoxyguanosine (8-OHdG) formation through the Keap1/Nrf2 signaling pathway and GSTP1 DNA methylation.

N6-Methyladenosine Reader YTHDF1 Promotes ARHGEF2 Translation and RhoA Signaling in Colorectal Cancer.

BACKGROUND & AIMS: N6-methyladenosine (m6A) governs the fate of RNAs through m6A readers. Colorectal cancer (CRC) exhibits aberrant m6A modifications and expression of m6A regulators. However, how m6A readers interpret oncogenic m6A methylome to promote malignant transformation remains to be illustrated. METHODS: YTH N6-methyladenosine RNA binding protein 1 (Ythdf1) knockout mouse was generated to determine the effect of Ythdf1 in CRC tumorigenesis in vivo. Multiomic analysis of RNA-sequencing, m6A methylated RNA immunoprecipitation sequencing, YTHDF1 RNA immunoprecipitation sequencing, and proteomics were performed to unravel targets of YTHDF1 in CRC. The therapeutic potential of targeting YTHDF1-m6A-Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) was evaluated using small interfering RNA (siRNA) encapsulated by lipid nanoparticles (LNP). RESULTS: DNA copy number gain of YTHDF1 is a frequent event in CRC and contributes to its overexpression. High expression of YTHDF1 is significantly associated with metastatic gene signature in patient tumors. Ythdf1 knockout in mice dampened tumor growth in an inflammatory CRC model. YTHDF1 promotes cell growth in CRC cell lines and primary organoids and lung and liver metastasis in vivo. Integrative multiomics analysis identified RhoA activator ARHGEF2 as a key downstream target of YTHDF1. YTHDF1 binds to m6A sites of ARHGEF2 messenger RNA, resulting in enhanced translation of ARHGEF2. Ectopic expression of ARHGEF2 restored impaired RhoA signaling, cell growth, and metastatic ability both in vitro and in vivo caused by YTHDF1 loss, verifying that ARHGEF2 is a key target of YTHDF1. Finally, ARHGEF2 siRNA delivered by LNP significantly suppressed tumor growth and metastasis in vivo. CONCLUSIONS: We identify a novel oncogenic epitranscriptome axis of YTHDF1-m6A-ARHGEF2, which regulates CRC tumorigenesis and metastasis. siRNA-delivering LNP drug validated the therapeutic potential of targeting this axis in CRC.

DpCoA tagSeq: Barcoding dpCoA-Capped RNA for Direct Nanopore Sequencing via Maleimide-Thiol Reaction.

Recent discoveries of noncanonical RNA caps, such as nicotinamide adenine dinucleotide (NAD+) and 3'-dephospho-coenzyme A (dpCoA), have expanded our knowledge of RNA caps. Although dpCoA has been known to cap RNAs in various species, the identities of its capped RNAs (dpCoA-RNAs) remained unknown. To fill this gap, we developed a method called dpCoA tagSeq, which utilized a thiol-reactive maleimide group to label dpCoA cap with a tag RNA serving as the 5' barcode. The barcoded RNAs were isolated using a complementary DNA strand of the tag RNA prior to direct sequencing by nanopore technology. Our validation experiments with model RNAs showed that dpCoA-RNA was efficiently tagged and captured using this protocol. To confirm that the tagged RNAs are capped by dpCoA and no other thiol-containing molecules, we used a pyrophosphatase NudC to degrade the dpCoA cap to adenosine monophosphate (AMP) moiety before performing the tagSeq protocol. We identified 44 genes that transcribe dpCoA-RNAs in mouse liver, demonstrating the method's effectiveness in identifying and characterizing the capped RNAs. This strategy provides a viable approach to identifying dpCoA-RNAs that allows for further functional investigations of the cap.

Derivatization of N-Acyl Glycines by 3-Nitrophenylhydrazine for Targeted Metabolomics Analysis and Their Application to the Study of Diabetes Progression in Mice.

N-Acyl glycines (NAGlys) are an important class of metabolites in the detoxification system of the human body. They have been used in the diagnosis of several metabolic diseases. Liquid chromatography-mass spectrometry (LC-MS) is the most frequently used NAGlys detection platform. Here, we describe a simple and sensitive method of NAGlys detection by LC-MS in plasma and urine samples. This approach is based on the use of a derivatization reagent, 3-nitrophenylhydrazine. The reaction is quick in aqueous solution, and no quenching step is needed. To expand the coverage of NAGlys when standards are not available, NAGlys were first identified based on high-resolution LC-MS. Quantification was subsequently carried out on triple quadrupole LC-MS. This approach allowed a much broader measurement of NAGlys (41 NAGlys in total), especially when authentic standards are unavailable. Comprehensive analysis of NAGlys with this new method was applied in plasma and urine samples of db/db diabetic and non-diabetic db/m+ control mice. The majority of detected NAGlys were altered with high differentiation ability in plasma and urine samples from diabetic and non-diabetic mice. These identified NAGlys hold the potential to be diagnostic biomarkers for type II diabetes and diabetic complications.

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data.

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

A Novel Strategy to Directly Quantify Polyethylene Microplastics in PM2.5 Based on Pyrolysis-Gas Chromatography-Tandem Mass Spectrometry.

The broad application of plastic products has resulted in a considerable release of microplastics (MPs) into the ecosystem. While MPs in other environmental matrices (e.g., soil and water) have been studied for a long time, the atmospheric fine particulate matter (PM2.5)-bound MPs are rarely investigated due to the lack of an appropriate analytical approach. The prevalently used visual and spectroscopic means (e.g., optical microscopy, Fourier-transform infrared spectroscopy, and Raman spectroscopy) suffer from obvious drawbacks that cannot precisely detect MPs of tiny sizes and provide quantitative information. In the present study, a novel strategy that does not require sample pretreatment was developed to first effectuate accurate quantification of polyethylene MP (PE-MP) in PM2.5 based on pyrolysis-gas chromatography-tandem mass spectrometry (Pyr-GC-MS/MS). It featured acceptable recoveries (97%-110%), high sensitivity (LOD = 1 pg), and qualified precisions (RSD of 3%-13%). Employing this approach, for the first time, exact atmospheric concentrations of PE-MPs in PM2.5 from megacities in North (Zhengzhou and Taiyuan) and South (Guangzhou) China were obtained, and relatively serious pollution was found in Taiyuan. The 100% sample detection rates also suggested the widespread occurrence and possible human exposure risks of PM2.5-bound PE-MPs. In brief, the new strategy could conduct direct, sensitive, and accurate quantification of PE-MP in PM2.5, favoring further studies of environmental fates, distributions, and toxicities of atmospheric MPs.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.