Airborne fine particulate matter induces cognitive and emotional disorders in offspring mice exposed during pregnancy

Lipidomics and mass spectrometry imaging unveil alterations in mice hippocampus lipid composition exposed to hypoxia

Lipids are components of cytomembranes that are involved in various biochemical processes. High-altitude hypoxic environments not only affect the body's energy metabolism, but these environments can also cause abnormal lipid metabolism involved in the hypoxia-induced cognitive impairment. Thus, comprehensive lipidomic profiling of the brain tissue is an essential step toward understanding the mechanism of cognitive impairment induced by hypoxic exposure. In the present study, mice showed reduced new-object recognition and spatial memory when exposed to hypobaric hypoxia for 1 day. Histomorphological staining revealed significant morphological and structural damage to the hippocampal tissue, along with prolonged exposure to hypobaric hypoxia. Dynamic lipidomics of the mouse hippocampus showed a significant shift in both the type and distribution of phospholipids, as verified by spatial lipid mapping. Collectively, a diverse and dynamic lipid composition in mice hippocampus was uncovered, which deepens our understanding of biochemical changes during sustained hypoxic exposure and could provide new insights into the cognitive decline induced by high-altitude hypoxia exposure.

eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Gestational exposure to PM2.5 disrupts fetal development by suppressing placental trophoblast syncytialization via progranulin/mTOR signaling

Recent epidemiological and animal studies have indicated that ambient fine particulate matter (PM2.5) exposure during pregnancy is closely associated with intrauterine growth restriction (IUGR). However, the underlying mechanisms remain to be revealed. In this study, we found that gestational exposure to PM2.5 significantly decreased fetal weight and crown-rump length in mice, accompanied by insufficient placental trophoblast syncytialization and increased expression of progranulin (PGRN) in mice placenta. Administering PGRN neutralizing antibody to pregnant mice alleviated growth restriction and insufficient placental trophoblast syncytialization caused by PM2.5, accompanied with suppressed activation of the mTOR signaling pathway. Furthermore, in vitro experiments using human placental BeWo cells showed that 10 μg·mL-1 PM2.5 activated PGRN/mTOR signaling and suppressed forskolin-induced cell fusion, which was blocked by knockdown of PGRN. Taken together, our results demonstrated that PM2.5 exposure during pregnancy inhibited placental trophoblast syncytialization by activating PGRN/mTOR signaling, leading to abnormal placental development and IUGR. This study reveals a novel mechanism underlying the developmental toxicity of PM2.5 exposure during pregnancy.

Novel Small Molecule Matrix Screening for Simultaneous MALDI Mass Spectrometry Imaging of Multiple Lipids and Phytohormones

Most of the traditional matrices cannot simultaneously image multiple lipids and phytohormones, so screening and discovery of novel matrices stand as essential approaches for broadening the application scope of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). In this work, 12 organic small molecule compounds were comprehensively screened and investigated as potential MALDI matrices for simultaneous imaging analysis of various lipids and phytohormones. In the positive ionization mode, p-nitroaniline, m-nitroaniline, and 2-aminoterephthalic acid displayed good performance for the highly sensitive detection of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), and triacylglycerols (TGs). Furthermore, p-nitroaniline possessed excellent characteristics of strong ultraviolet absorption and homogeneous cocrystallization, making it a desirable matrix for MALDI-MSI analysis of eight plant hormones. Compared with conventional matrices (2,5-dihydroxybenzoic acid (DHB), α-cyano-4-hydroxycinnamic acid (CHCA), and 9-aminoacridine (9-AA), the use of p-nitroaniline resulted in higher ionization efficiency, superior sensitivity, and clearer imaging images in dual polarity mode. Our research offers valuable guidance and new ideas for future endeavors in matrix screening.

Airborne magnetite nanoparticles induced early vascular pathologies by disrupting lipid metabolism under high-fat dietary patterns

Magnetite nanoparticles (MNPs) have been extensively detected in the atmospheric environment and implicated as a prominent threat to atherosclerosis, a chronic vascular inflammatory disease. Due to globalization and economic development, the dramatic shift in diet from traditional to high-fat dietary patterns aggravated atherosclerosis progression induced by environmental factors. However, limited knowledge is available regarding vascular risks and underlying mechanisms of airborne MNPs in high-risk populations with high-fat dietary habits. Herein, we demonstrated that MNPs exerted a proatherogenic effect under high-fat dietary patterns, leading to aortic wall thickening, elastic fiber disorganization, macrophage infiltration, and local inflammation. Based on the correlation analysis between MNPs and PM group, we identified that MNPs might be a key PM component in atherogenic toxicity. MNPs exposure disturbed the dynamic process of lipid metabolism, manifested as aortic lipid accumulation, dyslipidemia, and hepatic lipid metabolism disorder, which was modulated by the JAK-STAT pathway. Overall, these findings provide new insight into understanding the cardiovascular risks and mechanisms of MNPs among high-risk populations.

Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy

Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.

Particulate matter exposure and neurodegenerative diseases: A comprehensive update on toxicity and mechanisms

Exposure to particulate matter (PM) has been associated with a range of health impacts, including neurological abnormalities that affect neurodevelopment, neuroplasticity, and behavior. Recently, there has been growing interest in investigating the possible relationship between PM exposure and the onset and progression of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. However, the precise mechanism by which PM affects neurodegeneration is still unclear, even though several epidemiological and animal model studies have provided mechanistic insights. This article presents a review of the current research on the neurotoxicity of PM and its impact on neurodegenerative diseases. This review summarizes findings from epidemiological and animal model studies collected through searches in Google Scholar, PubMed, Web of Science, and Scopus. This review paper also discusses the reported effects of PM exposure on the central nervous system and highlights research gaps and future directions. The information presented in this review may inform public health policies aimed at reducing PM exposure and may contribute to the development of new treatments for neurodegenerative diseases. Further mechanistic and therapeutic research will be needed to fully understand the relationship between PM exposure and neurodegenerative diseases.

Prenatal PM2.5 exposure impairs spatial learning and memory in male mice offspring: from transcriptional regulation to neuronal morphogenesis

BACKGROUND

As one of the environmental risk factors for human health, atmospheric fine particulate matter (PM_2.5) contributes to cognitive deterioration in addition to respiratory and cardiovascular injuries. Recently, increasing evidence implicates that PM_2.5 inhalation can affect neurological functions in offspring, but the sex-specific outcomes and the underlying biological processes are largely unknown.

OBJECTIVES

To observe the influence of prenatal PM_2.5 exposure on cognitive performance in offspring, to elucidate the neuronal morphological alterations and possible transcriptional regulation based on mRNA-sequencing (mRNA-Seq) data after birth, and to determine the key components of PM_2.5 contributing to the adverse effects.

METHODS

Pregnant C57BL/6J mice were exposed to sterile saline or PM_2.5 suspension. Morris water maze test was used to assess the cognitive function in weanling offspring. Microscopic observation was applied to detect neuronal morphogenesis in vivo and in vitro. The cortex tissues from male offspring were collected on postnatal days (PNDs) 1, 7, and 21 for mRNA-Seq analysis. The organic and inorganic components of PM_2.5 were separated to assess their contributions using primary cultured neurons.

RESULTS

Prenatal PM_2.5 exposure impaired spatial learning and memory in weanling male mice, but not female mice. The sex-specific outcomes were associated with mRNA expression profiles of the cortex during postnatal critical windows, and the annotations in Gene Ontology (GO) of differentially expressed genes (DEGs) revealed that the exposure persistently disrupted the expression of genes involved in neuronal features in male offspring. Consistently, axonal growth impairment and dendritic complexity reduction were observed. Importantly, Homeobox A5 (Hoxa5), a critical transcription factor regulating all of the neuronal morphogenesis-associated hub genes on PNDs 1, 7, and 21, significantly decreased in the cortex of male offspring following PM_2.5 exposure. In addition, both inorganic and organic components were harmful to axonal and dendritic growth, with organic components exhibiting stronger inhibition than inorganic ones.

CONCLUSION

Prenatal PM_2.5 exposure affected spatial learning and memory in male mice by disrupting Hoxa5-mediated neuronal morphogenesis, and the organic components, including polycyclic aromatic hydrocarbons (PAHs), posed more adverse effects than the inorganic components.

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.

Maternal exposure to PM2.5 induces cognitive impairment in offspring via cerebellar neuroinflammation and oxidative stress

Available evidence suggest that exposure to PM2.5 during pregnancy is associated with reduced cognitive function in offspring. This study aimed to investigate the effects of maternal exposure to PM2.5 on offspring cognitive function and to elucidate the underlying mechanisms. In this work, pregnant C57BL/6 female mice were exposed to concentrated ambient PM2.5 or filtered air from day 0.5 (=vaginal plug) to day 15.5 in the Shanghai Meteorological and Environmental Animal Exposure System, and offspring cerebellar tissues were collected on embryonic day 15.5, as well as postnatal days 0, 10 and 42. The mean PM2.5 concentrations exposed to the pregnant mice were 73.06 ± 4.90 μg/m3 and 11.15 ± 2.71 μg/m3 in the concentrated ambient PM2.5 and filtered air chambers, respectively. Maternal concentrated PM2.5 exposure was negatively correlated with offspring spatial memory significantly as assessed by the Morris water maze. Compared with the filtered air group, PM2.5-exposed offspring mice had reduced cerebellar microglia. Both RNA and protein levels of IL-8 and TNF-α were elevated in the concentrated ambient PM2.5 group. PM2.5 exposure increased the level of 8-OHG in miRNA of microglia and Purkinje cells in 6-week-old offspring. The level of prostaglandin F2α (8-iso-PGF2Aα) in the cerebellum was increased at different growing stages of offspring after gestational exposure of PM2.5. These results suggested that maternal air pollution exposure might cause inflammatory damage and oxidative stress to the cerebellum, contributing to reduced cognitive performance in mice offspring.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism

The metabolic responses of organism to external stimuli are characterized by the multicellular- and multiorgan-based synergistic regulation. Network analysis is a powerful tool to investigate this multiscale interaction. The imaging mass spectrometry (iMS)-based spatial omics provides multidimensional and multiscale information, thus offering the possibility of network analysis to investigate metabolic response of organism to environmental stimuli. We present iMS dataset-sourced multiscale network (iMS2Net) strategy to uncover prenatal environmental pollutant (PM2.5)-induced metabolic responses in the scales of cell and organ from metabolite abundances and metabolite-metabolite interaction using mouse fetal model, including metabotypic similarity, metabolic vulnerability, metabolic co-variability and metabolic diversity within and between organs. Furthermore, network-based analysis results confirm close associations between lipid metabolites and inflammatory cytokine release. This networking methodology elicits particular advantages for modeling the dynamic and adaptive processes of organism under environmental stresses or pathophysiology and provides molecular mechanism to guide the occurrence and development of systemic diseases.

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IMS2Net, a multiscale networking methodology to decipher iMS-spatial omics data

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Elaboration of variation and covariation within/between organs to external stimuli

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Understanding metabolic responses of organisms at cell and organ resolutions

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A close association between lipid metabolism and inflammatory cytokine release

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.

Top-Down Rational Engineering of Heteroatom-Doped Graphene Quantum Dots for Laser Desorption/Ionization Mass Spectrometry Detection and Imaging of Small Biomolecules

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is widely applied in mapping macrobiomolecules in tissues, but it is still limited in profiling low-molecular-weight (MW) compounds (typically metabolites) due to ion interference and suppression by organic matrices. Here, we present a versatile "top-down" strategy for rational engineering of carbon material-based matrices, by which heteroatom-doped graphene quantum dots (HGQDs) were manufactured for LDI MS detection and imaging of small biomolecules. The HGQDs derived from parent materials inherited the π-conjugated networks and doping sites for promoting energy transfer and negative ion generation, while their extremely small size guaranteed the matrix uniformity and signal reproducibility in LDI MSI. Compared to other HGQDs, nitrogen-doped graphene quantum dots (NGQDs) exhibited superior capability of assisting LDI of various small molecules, including amino acids, fatty acids, saccharides, small peptides, nucleobases, anticancer drugs, and bisphenol pollutants. Density functional theory simulations also corroborated that the LDI efficiency was markedly raised by the proton-capturing pyridinic nitrogen species and compromised by the electron-deficient boron dopants. NGQDs-assisted LDI MS further enabled label-free investigation on enzyme kinetics using an ordinary short peptide as the substrate. Moreover, due to the high salt tolerance and signal reproducibility, the proposed negative-ion NGQDs-assisted LDI MSI was able to reveal the abundance and distribution of low-MW species in rat brain tissue and achieved the imaging of low-MW lipids in coronally sectioned rat brains subjected to traumatic brain injury. Our work offers a new route for customizing nanomaterial matrices toward LDI MSI of small biomolecules in biomedical and pathological research.

Three-dimensional quantitative mass spectrometry imaging in complex system: From subcellular to whole organism

Mass spectrometry imaging (MSI) has been applied for label-free three-dimensional (3D) imaging from position array across the whole organism, which provides high-dimensional quantitative data of inorganic or organic compounds that may play an important role in the regulation of cellular signaling, including metals, metabolites, lipids, drugs, peptides, and proteins. While MSI is suitable for investigation of the spatial distribution of molecules, it has a limitation with visualization and quantification of multiple molecules. 3D-MSI, however, can be applied toward exploring metabolic pathway as well as the interactions of lipid-protein, protein-protein, and metal-protein in complex systems from subcellular to the whole organism through an untargeted methodology. In this review, we highlight the methods and applications of MS-based 3D imaging to address the complexity of molecular interaction from nano- to micrometer lateral resolution, with particular focus on: (a) common and hybrid 3D-MSI techniques; (b) quantitative MSI methodology, including the methods using a stable isotope labeling internal standard (SILIS) and SILIS-free approaches with tissue extinction coefficient or virtual calibration; (c) reconstruction of the 3D organ; (d) application of 3D-MSI for biomarker screening and environmental toxicological research. 3D-MSI quantitative analysis provides accurate spatial information and quantitative variation of biomolecules, which may be valuable for the exploration of the molecular mechanism of the disease progresses and toxicological assessment of environmental pollutants in the whole organism. Additionally, we also discuss the challenges and perspectives on the future of 3D quantitative MSI.

Mass spectrometry imaging revealed alterations of lipid metabolites in multicellular tumor spheroids in response to hydroxychloroquine

Three-dimensional (3D) multicellular tumor spheroids (MCTS) that mimic the complex tumor microenvironment provide a good platform for in vitro study of drug and endogenous metabolites. Hydroxychloroquine (HCQ) has shown anti-tumor activity in a variety of tumor models. However, the effect of the drug on the alteration of lipid metabolism spatial composition and distribution in the MCTS model is not clear. Herein, we utilized matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) in the analysis of A549 lung cancer multicellular spheroids to investigate the in situ spatial distribution of HCQ and its effect on lipid metabolism. We have successfully observed the spatial variations of HCQ in the inner region of the spheroid at different drug-treated time points. The MSI results also demonstrated that HCQ treatment altered the spatial composition of lipids in the inner and outer regions of treated spheroids. Furthermore, the lipidomic results showed that the identified phosphatidylcholines (PC), lysophosphatidylcholines (LPC), phosphatidylethanolamines (PE), lysophosphatidylethanolamines (LPE), phosphatidylinositols (PI), ceramides (Cer), glucosylceramides (CerG), and diglycerides (DG) were significantly up-regulated, and phosphatidylglycerol (PG) and triglycerides (TG) were remarkable down-regulated. MSI method combined with LC-MS/MS profiling of endogenous metabolites can obtain more detailed information about how spheroids respond to drug and spatial distribution information, thus fostering a better understanding of the relationship between drug-altered lipid metabolism and cancer microenvironment.

Three-Dimensional Imaging of Whole-Body Zebrafish Revealed Lipid Disorders Associated with Niemann-Pick Disease Type C1

Imaging of lipids of whole-body specimens in two-dimensional (2D) analysis provides a global picture of the lipid changes in lipid-disturbed diseases, enabling a better understanding of lipid functions and lipid-modulation processes in different organs. However, 2D imaging of a single cross section can hardly characterize the whole-body lipid alterations. In this work, a three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (3D MALDI-MSI) approach was developed for analysis of whole-body zebrafish, for the first time, and applied to identify altered lipids and map their spatial distributions by using a zebrafish model of Niemann-Pick disease type C1 (NPC1), a neurovisceral lipid storage disorder causing both neurodegenerative disorder and visceral organ damage. The constructed 3D fish model provided comprehensive information on the 3D distribution of lipids of interest and allowed direct correlations between these lipids and organs of the fish. Obtained results revealed that several sphingolipids and phospholipids showed significant alterations and exhibited different localization patterns in various organs such as the brain, spinal cord, intestines, and liver-spleen region in the npc1 gene mutant fish compared to those of the wild type. The whole-body 3D MALDI-MSI approach revealed unique lipid signatures for different NPC1-affected organs, which might offer insights into the link between the impaired lipid storage and subsequent clinical symptoms, such as neurodegeneration and hepatosplenomegaly.

Mapping Metabolic Networks in the Brain by Ambient Mass Spectrometry Imaging and Metabolomics

Metabolic networks and their dysfunction in the brain are closely associated with central nervous function and many psychogenic diseases. Thus, it is of utmost importance to develop a high-throughput imaging method for metabolic network mapping. Here, we developed a metabolic network mapping method to discover the metabolic contexts and alterations with spatially resolved information from the microregion of the brain by ambient-air flow-assisted desorption electrospray ionization mass spectrometry imaging and metabolomics analysis, which can be performed without any chemical derivatization, labels, or complex sample pretreatment. This method can map hundreds of different polar functional metabolites involved in multiple metabolic pathways, including not only neurotransmitters but also purines, organic acids, polyamines, cholines, and carbohydrates, in the rat brain. These high-coverage metabolite profile and microregional distribution information constitute complex networks that regulate advanced functions in the central nervous system. Moreover, this methodology was further used to discover not only the dysregulated metabolites but also the brain microregions involved in the pathology of a scopolamine-treated Alzheimer's model. Furthermore, this methodology was demonstrated to be a powerful visualizing tool that could offer novel insight into the metabolic events and provide spatial information about these events in central nervous system diseases.

The Impact of Particulate Matter (PM2.5) on Human Retinal Development in hESC-Derived Retinal Organoids

Increasing evidence demonstrated that PM2.5 could cross the placenta and fetal blood-brain barrier, causing neurotoxicity of embryonic development. The retina, an embryologic extension of the central nervous system, is extremely sensitive and vulnerable to environmental insults. The adverse effects of PM2.5 exposure on the retina during embryonic neurodevelopment are still largely unknown. Our goal was to investigate the effect of PM2.5 on human retinal development, which was recapitulated by human embryonic stem cell (hESC)-derived retinal organoids (hEROs). In the present study, using the hEROs as the model, the influences and the mechanisms of PM2.5 on the developing retina were analyzed. It demonstrated that the formation rate of the hERO-derived neural retina (NR) was affected by PM2.5 in a concentration dosage-dependent manner. The areas of hEROs and the thickness of hERO-NRs were significantly reduced after PM2.5 exposure at the concentration of 25, 50, and 100 μg/ml, which was due to the decrease of proliferation and the increase of apoptosis. Although we did not spot significant effects on retinal differentiation, PM2.5 exposure did lead to hERO-NR cell disarranging and structural disorder, especially retinal ganglion cell dislocation. Transcriptome analysis showed that PM2.5 treatment was significantly associated with the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT pathways and reduced the level of the fibroblast growth factors (FGFs), particularly FGF8 and FGF10. These results provided evidence that PM2.5 exposure potentially inhibited proliferation and increased apoptosis at the early development stage of the human NR, probably through the MAPK and PI3K/Akt pathway. Our study suggested that exposure to PM2.5 suppressed cell proliferation and promoted cell apoptosis, thereby contributing to abnormal human retinal development.