Dynamic Stomach Model-Capillary Electrophoresis-ICPMS for Evaluation of Release and Transformation Behaviors of Arsenic Species from Microplastics during Digestion.

Microplastics (MPs) can act as carriers of environmental arsenic species into the stomach with food and release arsenic species during digestion, which threatens human health. Herein, an integrated dynamic stomach model (DSM)-capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICPMS) is developed for online monitoring of the release and transformation behaviors of arsenic species loaded on MPs (As-MPs) in the simulated human stomach. The 3D-printed DSM with a soft stomach chamber enables the behaviors of gastric peristalsis, gastric and salivary fluid addition, pH adjustment, and gastric emptying (GE) to be controlled by a self-written program after oral ingestion of food with As-MPs. The gastric extract during digestion is introduced into the spiral channel to remove the large particulate impurity and online filtered to obtain the clarified arsenic-containing solution for subsequent speciation analysis of arsenic by CE-ICPMS. The digestion conditions and pretreatment processes of DSM are tracked and validated, and the release rates of As-MPs digested by DSM are compared with those digested by the static stomach model and DSM without GE. The release rate of inorganic arsenic on MPs is higher than that of organic arsenic throughout the gastric digestion process, and 8% of As(V) is reduced to As(III). The detection limits for As(III), DMA, MMA, and As(V) are 0.5-0.9 µg L-1 using DSM-CE-ICPMS, along with precisions of ≤8%. This present method provides an integrated and convenient tool for evaluating the release and transformation of As-MPs during human gastric digestion and provides a reference for exploring the interactions between MPs and metals/metalloids in the human body.

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis.

Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

A Portable Microplasma Optical Emission Spectrometric Device with Online Digestion Function for Field and Sensitive Determination of Lead in Biological Samples.

Accurate detection and screening of Pb in biological samples is helpful to assess the risk associated with lead pollution to human health. However, conventional atomic spectroscopic instruments are bulky and cumbersome, requiring additional sample pretreatment equipment, and difficult to perform field analysis with. Herein, a portable point discharge (PD) microplasma-optical emission spectrometric (OES) device with online digestion function is designed for field and sensitive determination of lead in biological samples. With rice as a model, online digestion of a batch of six 50 mg samples can be achieved in the HNO3 and H2O2 system within 25 min by a temperature control and timing module. Compared to the conventional microwave digestion, the digestion efficiency of this device reaches 97%. Pb in digestion solution is converted into volatile species by hydride generation (HG) and directly introduced into PD-OES for excitation and detection by a self-designed rotatable and telescopic cutoff gas sampling column. Six samples can be successively detected in 2 min, and argon consumption of the whole process is only <800 mL. Under the optimized conditions, the detection limit of Pb is 0.018 mg kg-1 (0.9 µg L-1) and precision is 3.6%. The accuracy and practicability of the present device are verified by measuring several certified reference materials and real biological samples. By virtue of small size (23.5 × 17 × 8.5 cm3), lightweight (2.5 kg), and low energy consumption (24.3 W), the present device provides a convenient tool for field analysis of toxic elements in biological samples.

Phosphorescence approach based on silica protected carbon dots for autofluorescence interference-free and highly selective detection of fluoride.

BACKGROUND: Fluoride (F-), an anion with the smallest ionic radius and highest charge density, plays an important role in biomedical and environmental processes, making the development of accurate F- detection methods of great importance. Fluorometric methods with simplicity and sensitivity have gained considerable attention in F- detection. However, their accuracy faces challenges due to issues like autofluorescence interference during real-time light excitation and limited selectivity. Therefore, it is important to establish a simple, real-time light excitation-free, and highly selective method for the accurate determination of F- in complicated samples. RESULTS: Herein, a novel phosphorescent approach is developed for the selective and accurate detection of F- in complex samples. Phosphorescence emission CDs@SiO2 is fabricated by confining CDs in a silica protective layer. This design retains the favorable water solubility of silica while benefitting from its inertness, making it resistant to most substances. Furthermore, phosphorescent analysis without real-time light excitation eliminates autofluorescence interference, significantly improving the signal-to-noise ratio (SNR) and simplifying sample pretreatment. The specific interaction between F- and the Si-O bond can lead to the degradation of the silica protective layer, exposing the CDs to the solution, resulting in phosphorescence quenching, achieving the highly accurate and sensitive detection of F- with a linear range of 0.001-4 mM and a limit of detection (LOD) of 1 µM. SIGNIFICANCE: This novel F- phosphorescence method based on the metal-free phosphorescent nanomaterial CDs@SiO2 integrates the benefits of no autofluorescence interference, high selectivity, and full aqueous compatibility, and its combination with a smartphone provides a simple, portable, and cost-effective detection platform for accurate and highly sensitive determination of F- in complex samples.

Urine Self-Sampling Kit Combined with an Automated Preparation-Sampler Device for Convenient and Reliable Analysis of Arsenic Metabolites by HPLC-ICPMS.

Speciation analysis of arsenic in urine is essential for the studies of arsenic metabolism and biological effects, but the unstable arsenic species represented by MMAIII and DMAIII pose a huge challenge to analytical accuracy. Herein, a novel urine self-sampling (USS) kit combined with an automated preparation-sampler (APS) device is rationally designed and used for convenient analysis of arsenic metabolites by high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS). The subject can collect urine into a sampling vial at home and use a homemade syringe to pump argon to displace oxygen in the vial, thereby inhibiting the oxidation of MMAIII and DMAIII. After USS and transportation, the sampling vial is loaded directly onto the APS device, where the urine sample can be automatically mixed with diluent, filtered, and loaded into HPLC-ICPMS for arsenic speciation analysis under anaerobic conditions. For a single sample, the sampling time and the analysis time are <8 and <18 min, respectively. The recoveries of MMAIII and DMAIII in urine over 24 h at 4 °C are 86 and 67%, surpassing the conventional sampling method by 28 and 67%, respectively. When the APS is coupled to HPLC-ICPMS, the detection limits of AsC, iAsIII, MMAIII, DMAV, MMAV, DMAIII, and iAsV are 0.03-0.10 µg L-1 with precisions of <10%. The present method provides a convenient and reliable tool for the storage and analysis of unstable arsenic species in urine and lays the foundation for studying the metabolic and biological effects of methylated trivalent arsenicals.

Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example.

Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Elemental dyshomeostasis can be used to assess toxicity of environmental pollutants. Non-targeted metallomics, combining synchrotron radiation X-ray fluorescence (SRXRF) and machine learning, has successfully differentiated cancer patients from healthy individuals. The whole idea of this work is to screen the phytotoxicity of nano polyethylene terephthalate (nPET) and micro polyethylene terephthalate (mPET) through non-targeted metallomics with SRXRF and deep learning algorithms. Firstly, Seed germination, seedling growth, photosynthetic changes, and antioxidant activity were used to evaluate the toxicity of mPET and nPET. It was showed that nPET, at 10 mg/L, was more toxic to rice seedlings, inhibiting growth and impairing chlorophyll content, MDA content, and SOD activity compared to mPET. Then, rice seedling leaves exposed to nPET or mPET was examined with SRXRF, and the SRXRF data was differentiated with deep learning algorithms. It was showed that the one-dimensional convolutional neural network (1D-CNN) model achieved 98.99% accuracy without data preprocessing in screening mPET and nPET exposure. In all, non-targeted metallomics with SRXRF and 1D-CNN can effectively screen the exposure and phytotoxicity of nPET/mPET and potentially other emerging pollutants. Further research is needed to assess the phytotoxicity of different types of MPs/NPs using non-targeted metallomics.

Two-Dimensional Multi-parameter Cytometry Platform for Single-Cell Analysis.

A 2D flow cytometry platform, known as CytoLM Plus, was developed for multi-parameter single-cell analysis. Single particles or cells after hydrodynamic alignment in a microfluidic unit undergo first-dimension fluorescence and side scattering dual-channel optical detection. They were thereafter immediately directed to ICP-MS by connecting the microfluidic unit with a high-efficiency nebulizer to facilitate the second-dimension ICP-MS detection. Flow cytometry measurements of fluorescent microspheres evaluated the performance of CytoLM Plus for optical detection. 6434 fluorescence bursts were observed with a valid signal proportion as high as 99.7%. After signal unification and gating analysis, 6067 sets of single-particle signals were obtained with 6.6 and 6.2% deviations for fluorescence burst area and height, respectively. This is fairly comparable with that achieved by a commercial flow cytometer. Afterward, CytoLM Plus was evaluated by 2D flow cytometry measurement of Ag+-incubated and AO-stained MCF-7 cells. A program for 2D single-cell signal unification was developed based on the algorithm of screening in lag time window. In the present case, a lag time window of -4.2 ± 0.09 s was determined by cross-correlation analysis and two-parameter optimization, which efficiently unified the concurrent single-cell signals from fluorescence, side scattering, and ICP-MS. A total of 495 sets of concurrent 2D signals were screened out, and the statistical analysis of these single-cell signals ensured 2D multi-parameter single-cell analysis and data elucidation.

Simultaneous Imaging of Multiple miRNAs in Mitochondria Controlled by Fluorescently Encoded Upconversion Optical Switches for Drug Resistance Studies.

Mitochondrial miRNAs (mitomiRs) are essential regulators of biological processes by influencing mitochondrial gene expression and function. To comprehensively understand related pathological processes and treatments, simultaneous imaging of multiple mitomiRs is crucial. In this study, we present a technique that enables simultaneous monitoring of multiple mitomiRs in living cells using a near-infrared (NIR) photoactivated controlled detection probe (PD-mFleU) with a fluorescence-encoded error correction module and a nonsupervised machine learning data-processing algorithm. This method allows controlled sensing imaging of mitomiRs with a DNA reporter probe that can be activated by NIR light after targeted mitochondrial localization. Multilayer upconversion nanoparticles (UCNPs) are used for encoding probes and error correction. Additionally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is used to process and analyze the image. Using this technique, we achieved rapid in situ imaging of the abnormal expression of three mitomiRs (miR-149, miR-590, and miR-671) related to mt-ND1 in drug-resistant cells. Furthermore, upregulating the three mitomiRs simultaneously efficiently reverted drug-resistant cells to sensitive cells. Our study provides an analytical strategy for multiplex imaging of mitomiRs in living cells with potential clinical applications.

Phosphorescence, fluorescence, and colorimetric triple-mode sensor for the detection of acid phosphatase and corresponding inhibitor.

Acid phosphatase (ACP) as a clinical diagnostic biomarker for several pathophysiological diseases has aroused widespread interest. Compared to commonly developed single-mode ACP detection technology, the multi-mode detection method with self-validation can provide more reliable results. Herein, we proposed a triple-mode phosphorescence, fluorescence, and colorimetric method for ACP detection in combination with CDs@SiO2. HAuCl4 with oxidase-like activity can catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to the blue oxide TMB (TMBox), offering absorption signals and quenching the phosphorescence and fluorescence of CDs@SiO2 based on the internal filtration effect (IFE). ACP can hydrolyze ascorbic acid 2-phosphate (AAP) to yield ascorbic acid (AA), thereby reducing TMBox to TMB, triggering solution fading and restoring phosphorescence and fluorescence signals. When the ACP inhibitor malathion is present, the reduction of TMBox is hindered, which successively led to the suppression of CDs@SiO2 phosphorescence and fluorescence signal recovery. According to these principles, triple-mode ACP (LOD = 0.0026 mU mL-1) and malathion detections (LOD = 0.039 µg mL-1) with favorable accuracy and sensitivity are realized. With simplicity, robustness, and versatility, the triple-mode sensor can be extended to the detection of the AAP hydrolase family and the screening of corresponding inhibitors.

One-Pot Pretreatment Coupled to Microplasma Optical Emission Spectrometry for Field and Sensitive Determination of Inorganic Mercury and Methylmercury in Fish.

Field and sensitive analysis of mercury species in seafood is helpful to assess the risk of human exposure to mercury, but the cumbersome pretreatment process is time-consuming and laborious. Herein, a simple one-pot pretreatment system is designed for extraction, separation, and enrichment of inorganic mercury (Hg(II)) and methylmercury (MeHg) in fish, and coupled to dielectric barrier discharge (DBD) microplasma optical emission spectrometry (OES). Both Hg(II) and MeHg species in fish can be effectively extracted by tetramethylammonium hydroxide under ultrasound, then separated from the fish matrix by vapor generation and photochemical vapor generation, and finally enriched on the activated carbon electrode tips. Mercury trapped on the activated carbon electrode tips can be rapidly released to produce OES under the DBD microplasma excitation for quantitative analysis. The pretreatment and analysis of a batch of 12 samples are completed within 50 min, and the extraction efficiency of total mercury is up to 90% for 100 mg of freeze-dried fish or 86% for 1 g of fresh fish. Under the optimized conditions, the detection limits are 2 µg kg-1 for Hg(II) and 1.2 µg kg-1 for MeHg in freeze-dried fish, and precisions are 3.2% for Hg(II) and 3.9% for MeHg. The present method is applied to the analysis of the certified reference material and real marine fishes, giving rise to spiked recoveries of 95-103%. The present system hardly leads to MeHg and Hg(II) transforming into each other during extraction, providing a simple, convenient, and low-cost analytical tool to evaluate the risk of mercury species in fish.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

A machine learning-based colorimetric sensor array for high-precision pathogen identification in household refrigerators.

A precise and convenient sensor was constructed to identify pathogens in household refrigerators (4 °C, RH = 55%) by integrating a volatile organic compound fingerprint-responsive gel-based colorimetric sensor array and a neural network. The platform is expected to be extended to the intelligent food packaging field and has promise for point-of-need monitoring of pathogens.

Open flow cytometer with the combination of 3D hydrodynamic single cell focusing and confocal laser-induced fluorescence detection.

Flow cytometry is among the most powerful tools for single-cell analysis, while the high cost and mechanical complexity of the commercial instrumentation limit the applications in personalized single-cell analysis. For this issue, we hereby construct an open and low-cost flow cytometer. It is highly compact to integrate the functions of (1) single cell aligning by a lab-made modularized 3D hydrodynamic focusing device, and (2) fluorescence detection of the single cells by a confocal laser-induced fluorescence (LIF) detector. The ceiling cost of the entire hardware for the LIF detection unit and 3D focusing device is $ 3200 and $ 400 respectively. A sheath flow velocity of 150 µL/min produces a focused sample stream of 17.6 µm × 14.6 µm at sample flow of 2 µL/min, based on the LIF response frequency and the laser beam spot diameter. The assay performance of the flow cytometer was evaluated by characterizing fluorescent microparticles and acridine orange (AO) stained HepG2 cells, producing throughputs of 40.5/s and 6.2/s respectively. Favorable assay precision and accuracy were demonstrated by the agreement of frequency histogram with imaging analysis, and good Gaussian-like distributions of fluorescent microparticles and AO-stained HepG2 cells. Practically, the flow cytometer was successfully applied for the evaluation of ROS generation in single HepG2 cells.

Dual Functional Full-Color Carbon Dot-Based Organelle Biosensor Array for Visualization of Lipid Droplet Subgroups with Varying Lipid Composition in Living Cells.

In situ visualization of lipid composition diversity in lipid droplets (LDs) is essential for decoding lipid metabolism and function. However, effective probes for simultaneously localizing and reflecting the lipid composition of LDs are currently lacking. Here, we synthesized full-color bifunctional carbon dots (CDs) that can target LDs as well as respond to the nuance in internal lipid compositions with highly sensitive fluorescence signals, due to lipophilicity and surface state luminescence. Combined with microscopic imaging, uniform manifold approximation and projection, and sensor array concept, the capacity of cells to produce and maintain LD subgroups with varying lipid composition was clarified. Moreover, in oxidative stress cells, LDs with characteristic lipid compositions were deployed around mitochondria, and the proportion of LD subgroups changed, which gradually disappeared when treated with oxidative stress therapeutics. The CDs demonstrate great potential for in situ investigation of the LD subgroups and metabolic regulations.

A "dual-key-and-lock" ratiometric fluorescent probe with biocompatibility and selectivity for imaging vicinal dithiol proteins.

TMR-TPE, a ratiometric fluorescent probe, was reported for the imaging of vicinal dithiol proteins (VDPs) in living cells. Profiting from the "dual-key-and-lock" design, TMR-TPE solves the toxicity problem of VDP probes (98% cell viability at 50 µM) and avoids the interference of small thiols (up to 10 mM GSH). The change of VDPs during drug-induced liver injury was monitored for the first time using TMR-TPE.

Spatial Metallomics Reveals Preferable Accumulation of Methylated Selenium in a Single Seed of the Hyperaccumulator Cardamine violifolia†.

Cardamine violifolia is a Se hyperaccumulator found in Enshi, China. In this study, spatial metallomics was applied to visualize the distribution and speciation of Se in a single seed of C. violifolia. It was found that Se reached 1729.89 ± 28.14 mg/kg and the main Se species were SeCys and SeMet in bulk seeds. Further in situ study on a single seed found that the methylated Se species located mostly in the episperm. This is the first visualized evidence of the in situ distribution of methylated Se species in the seeds of C. violifolia. In all, spatial metallomics finds a preferable accumulation of methylated Se species in the seed coat, which deepens the understanding of the tolerance of Se by C. violifolia. The protocol applied in this study may also be used for the understanding of the tolerance of heavy metals/metalloids in other hyperaccumulators.

Facile Lego-Spinner Pretreatment Device for Analysis of Arsenic Species in Dried Blood Spots by Ion Chromatography-Inductively Coupled Plasma-Mass Spectrometry.

Dried blood spot (DBS) detection has the advantages of small blood collection, convenience, and reliability, which provides a possibility for large-scale evaluation of arsenic exposure in human population. Herein, a facile Lego-spinner pretreatment device is rationally designed for speciation analysis of arsenic in DBSs by ion chromatography-inductively coupled plasma-mass spectrometry (IC-ICP-MS). In the mixing mode of the Lego-spinner, the magnetic stir bar in the centrifuge tube rotates under a magnetic field to assist the dispersive extraction of arsenic species in the DBS with reagents. In the centrifugation mode of the Lego-spinner, the arsenic extract is separated from the blood matrix for the subsequent IC-ICP-MS analysis. For the DBS prepared from 80 µL of whole blood, the whole pretreatment operation can be completed within 25 min. The detection limits of arsenobetaine, arsenite, dimethylarsenate, monomethylarsonate, and arsenate in the DBS are 0.09-0.15 µg L-1, and precisions are <11%. The concentrations of these five arsenic species are highly correlated between whole blood and the DBS (r2 > 0.97), and Bland-Altman analysis indicates that the concentration difference of arsenic species between whole blood and the DBS is within ±20%. The DBS sampling approach can effectively preserve arsenic species for at least 30 days at 4 °C, and the contents of arsenic species in the DBS prepared from capillary blood are in a reasonable agreement with those of venous whole blood (gold standard). This Lego-spinner provides a handy and efficient tool for fast extraction of arsenic species in DBSs, facilitating the in-depth study of arsenic migration and transformation in the human body.

Gut as the target tissue of mercury and the extraintestinal effects.

Mercury (Hg) is harmful to the environment and human health. The gut plays important roles as the biological, chemical, mechanical, and immune barriers in animals and human beings. It has been known that Hg can be absorbed and methylated/demethylated in the gut, on the other hand, the impacts of Hg to the gut (especially the gut microbiota) is less studied. This review paper summarizes the impacts of inorganic Hg (IHg) and methyl Hg (MeHg) on gut barriers and the extraintestinal effects (damage to other organs such as the liver and brain). Both IHg and MeHg were found to cause intestinal microbial disorders, abnormal metabolites production, tight junction damage, and immune responses in the gut. The damage to the gut also contributed to the extraintestinal effects like the hepatotoxicity by IHg and the neurotoxicity by MeHg. In all, it is proposed that the gut should be considered as an important target tissue of Hg exposure, and the regulation of gut microbiota may have the potential for the prevention and control of the toxicity of Hg.

Ultramultiplex NaLnF4 Nanosatellites Combined with ICP-MS for Exosomal Multi-miRNA Analysis and Cancer Classification.

Quantification of exosomal multi-miRNA can reveal the initiation, progression, and metastasis of tumors, which is conducive to the noninvasive early diagnosis of cancer. However, low-sensitivity and single-plex detection characteristics of traditional methods seriously hinder the accuracy and specificity of exosomal miRNAs in cancer diagnosis. Herein, we design an ultramultiplexing strategy that enables simultaneous and sensitive detection of multiple exosomal miRNAs by nanosatellites (magnetic beads (MBs) @ NaLnF4) and catalytic hairpin assembly (CHA) amplification in combination with inductively coupled plasma-mass spectrometry (ICP-MS) to diagnose cancer accurately. The competitive binding of target exosomal miRNAs with the recognition sequences on nanosatellites triggers the drop of NaLnF4 from MBs, followed by a CHA reaction that releases more NaLnF4 labels for ICP-MS detection. This method is used to detect ten types of miRNAs simultaneously with a detection limit of 0.01 fM, which is one order of magnitude lower than the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. Linear discriminant analysis as a machine learning algorithm is subsequently applied to analyze the signals of exosomal multi-miRNA, and the discrimination accuracy of ten cell exosomes reaches 98.6%. In a clinical cohort of 42 patients, including five cancer types and healthy controls, exosomal multi-miRNA analysis achieves accurate cancer diagnosis and classification with 100% accuracy. Our results show that the combination of nanosatellites, CHA, and ICP-MS provides a universal biosensing platform for simultaneous and ultrasensitive detection of multiple targets.

Nano-WSe2 Is Absorbable and Transformable by Rice Plants.

As typical transition metal dichalcogenides (TMDC), tungsten selenide (WSe2) nanosheets (nano-WSe2) are widely used in various fields due to their layered structures and highly tunable electronic and magnetic properties, which results in the unwanted release of tungsten (W) and selenium (Se) into the environment. However, the environmental effects of nano-WSe2 in plants are still unclear. Herein, we evaluated the impacts and fate of nano-WSe2 and micro-WSe2 in rice plants (Oryza sativa L.). It was found that both nano-WSe2 and micro-WSe2 did not affect the germination of rice seeds up to 5000 mg/L but nano-WSe2 affected the growth of rice seedlings with shortened root lengths. The uptake and transportation of WSe2 was found to be size-dependent. Moreover, W in WSe2 was oxidized to tungstate while Se was transformed to selenocysteine, selenomethionine, SeIV and SeVI in the roots of rice when exposed to nano-WSe2, suggesting the transformation of nano-WSe2 in rice plants. The exposure to nano-WSe2 brought lipid peroxidative damage to rice seedlings. However, Se in nano-WSe2 did not contribute to the synthesis of glutathione peroxidase (GSH-Px) since the latter did not change when exposed to nano-WSe2. This is the first report on the impacts and fate of nano-WSe2 in rice plants, which has raised environmental safety concerns about the wide application of TMDCs, such as WSe2 nanosheets.