RNA splicing is a key regulatory process of gene expression that can increase the transcriptome complexity. Simultaneous monitoring of multiple splicing variants in living cells is critical for gaining new insight into cell development. Herein, we demonstrate the development of proximity-activated, programmable multicomponent nucleic acid enzymes (MNAzymes) for the simultaneous visualization of multiple RNA splicing variants (i.e., BRCA1 WT and BRCA1 Δ11q) in living cells. The presence of BRCA1 WT and BRCA1 Δ11q can specifically bring their corresponding partzymes into the proximity of each other to form two active MNAzyme motifs. Subsequently, the active sites of reporter probes 1 and 2 are cyclically cleaved by two activated MNAzyme motifs, respectively, to release abundant Cy3 and Cy5 fluorescent molecules, generating enhanced fluorescence signals for the simultaneous detection of BRCA1 WT and BRCA1 Δ11q and . Notably, this assay can be simply and isothermally conducted in a one-step format without the necessity for unstable protein enzymes, precise temperature control, and complex operation procedures. This method can sensitively detect 2.46 fM BRCA1 WT and 2.77 fM BRCA1 Δ11q and accurately distinguish breast cancer patients from healthy individuals by measuring target BRCA1 splicing variants from the tissue samples. Moreover, this method can real-time image BRCA1 splicing variants in living cells and can be extended to detect other cellular target RNAs (e.g., miRNAs, piRNAs, lncRNAs, and circRNAs) by simply changing the sequences of substrate arms, holding promising applications in clinical diagnosis and precise therapy.

A high-sensitivity fiber-optic photoacoustic carbon monoxide (CO) sensor based on quantum cascade laser (QCL) and nonresonant multipass cell is proposed. By leveraging the mid-infrared fundamental band absorption, multipass absorption enhancement, and cantilever resonance frequency detection, a multimechanism synergy has been achieved to enable highly sensitive detection of CO. In the mid-infrared band, CO exhibits a strong absorption coefficient, thereby eliminating the need for a high-power optical amplifier. Furthermore, by integrating a miniaturized multipass cell, the photoacoustic signal is remarkably enhanced, enabling the miniaturization and ultrahigh sensitivity of the sensor. A pair of spherical reflectors are symmetrically installed at both ends of the photoacoustic cell to form a Herriott multipass cell. The light beam reflects 20 times within the multipass cell, creating 10 elliptically distributed light spots. The gas chamber volume of the sensor is only 1.28 mL, with an optical path length of 510 mm. The generated photoacoustic signals are measured by a fiber-optic Fabry-Perot (FP) cantilever microphone, which can detect weak signals with high sensitivity at the resonance frequency of the cantilever. The measured signal amplitude is 8.7 times that of a single reflection. When the averaging time is 100 s, the minimum detection limit of the system for CO is 0.8 ppb, corresponding to a normalized noise equivalent absorption (NNEA) coefficient of 4.94 × 10 Wcm/Hz.

Nanoparticle size dispersion within polymer nanocomposites is crucial for ensuring material properties and performance. Monitoring the evolution of particle size distribution during processing proves to be critical for elucidating fundamental mechanisms and optimizing manufacturing parameters. The size dispersion evaluation relies on microscopy imaging of the nanoparticles inside the polymer matrix. However, current imaging techniques face significant challenges due to resolution limitations. In this study, we introduce a method that, despite having a microscopy resolution larger than the minimal particle size, effectively assesses the evolution of nanoparticle size dispersion during the fabrication process of polymer nanocomposites. We show that this method has an amplifying effect on the observation of nanoparticles with larger size, namely, the probabilities of the "residues" of the size statistics are larger than the corresponding original probabilities. We demonstrate the utility of this method to assess the agglomeration of nanoparticles during the fabrication processes of polymer nanocomposites. We prepare zinc oxide (ZnO) nanoparticles, incorporate them into polyethylene terephthalate (PET) chips, subsequently process them into ZnO/PET composite fibers, and apply the method to inspect the whole process of the fabrication. Our findings indicate that the developed method provides a reliable evaluation of nanoparticle size dispersion across different material forms. We observed that the fabrication process from ZnO/PET chips to ZnO/PET fibers increases the degree of aggregation, whereas the step from ZnO nanoparticles to ZnO/PET chips maintains a relatively fine size dispersion. Our developed method shows a novel "residue imaging" strategy and can be listed as a useful way to inspect the filler particle dispersion in polymer nanocomposites.

Research on nanoplastic (NP) toxicity and their "carrier effects" on human health remains nascent, especially for real-time, in situ monitoring of metabolic reactions in live cells. Herein, we developed smart surface-enhanced Raman spectroscopy (SERS) slides using a cyclic centrifugation-enhanced electrostatic loading (CCEL) method to facilitatively track live-cell metabolic signals. The designed core-shell polystyrene NPs (mPS) with embedded Raman probes successfully identified intracellular accumulation via a distinct Raman-silent peak. The smart SERS slide effectively monitored the metabolic changes induced by mPS at the molecular level, distinguishing different stages of membrane interaction, the endocytosis process, endosomal aggregation, and cell apoptosis. Besides, this platform was employed to perform a real-time, in situ comparison of cell cycle alterations induced by bare NPs and their "carrier effects", revealing that NPs extended both the S and G2 phases in BEAS-2B cells, while the "carrier effects" further prolonged G2 and disrupted S-phase progression. Additionally, we integrated machine learning algorithms to accurately predict the cell cycle impacts associated with mPS and their "carrier effects". This study provides a label-free, in situ, real-time method for monitoring NP-induced metabolic changes in live cells, laying the groundwork for further investigation into cytotoxic behaviors and strategies to mitigate NP toxicity.

Metabolomics, which involves the comprehensive analysis of small molecules within biological systems, plays a crucial role in elucidating the biochemical underpinnings of physiological processes and disease conditions. However, current coverage of the metabolome remains limited. In this study, we present a heuristic strategy for untargeted metabolomics analysis (HeuSMA) based on multiple chromatographic gradients to enhance the metabolome coverage in untargeted metabolomics. This strategy involves performing LC-MS analysis under multiple gradient conditions on a given sample (e.g., a pooled sample or a quality control sample) to obtain a comprehensive metabolomics data set, followed by constructing a heuristic peak list using a retention index system. Guided by this list, heuristic peak picking in quantitative metabolomics data is achieved. The benchmarking and validation results demonstrate that HeuSMA outperforms existing tools (such as MS-DIAL and MZmine) in terms of metabolite coverage and peak identification accuracy. Additionally, HeuSMA improves the accessibility of MS/MS data, thereby facilitating the metabolite annotation. The effectiveness of the HeuSMA strategy was further demonstrated through its application in serum metabolomics analysis of human hepatocellular carcinoma (HCC). To facilitate the adoption of the HeuSMA strategy, we also developed two user-friendly graphical interface software solutions (HPLG and HP), which automate the analysis process, enabling researchers to efficiently manage data and derive meaningful conclusions (https://github.com/Lacterd/HeuSMA).

HS and SO have been considered as important gaseous signaling molecules in biological systems, functioning as regulatory roles in many physiological processes of organisms. To better understand the crosstalk and synergetic effects between HS and SO in biological systems, developing a single fluorescent probe for dual-channel fast detection of HS and SO is highly urgent. We herein report a simple strategy to develop multifunctional near-infrared (NIR) fluorescent probes for simultaneous identification of HS and SO. Based on the idea of modulating the reactivity of the benzopyrylium core with electron donors, two new NIR fluorescent probes ( and ) were synthesized and evaluated. The probe could not only rapidly sense HS and SO with different fluorescence signals but also be used as a reversible probe to investigate the flux of HO and SO. Moreover, was successfully applied in visualizing HS and SO in living cells and mice. These results suggest that could serve as a useful tool in understanding the complex relationships between HS and SO in biological systems.

Metabolic dysregulation is a critical feature of various cancers, including brain tumors. Studying metabolic changes in tumor cells and tissues significantly improves our understanding of tumor development, progression, and treatment response. In this study, we utilize hyperspectral stimulated Raman scattering (SRS) imaging combined with biochemical spectral modeling to identify unique histological and molecular signatures linked to metabolic diversity across different glioma grades, without the need for labeling. By employing rapid label-free SRS histopathology and multivariate curve resolution analysis, we uncover changes in lipid profiles and varying levels of neuron demyelination from low-grade (LG) to high-grade (HG) gliomas. Quantitative analysis of key metabolites using non-negative least-squares regression spectral modeling reveals a significant increase in cellular proteins, DNA, and cholesterol levels, alongside a reduced redox ratio (flavin adenine dinucleotide (FAD)/NADH) in the glioblastoma (GBM, grade IV) tissue compared to pilocytic astrocytoma (PA, grade I) and healthy brain tissues, indicating a shift toward a pro-malignant metabolic state. A neural network diagnostic classifier, trained on 4547 SRS spectra (healthy: 1263; LG: 815; HG: 2469) from 45 patients with PA and GBM, achieves 99.6% accuracy in detecting and grading brain tumors. This study highlights the potential of hyperspectral SRS imaging for rapid, label-free, and spatially resolved analysis of metabolic heterogeneity in human gliomas, paving the way for metabolome-targeted therapeutic strategies in precision brain tumor treatment.

Mass cytometry (CyTOF) and imaging mass cytometry (IMC), as cutting-edge technologies in single-cell analysis, are capable of detecting more than 40 biomarkers simultaneously on a single cell. However, their sensitivity and multiparameter detection capabilities have been long constrained by the development of metal labeling materials. Meanwhile, as an imaging technique, IMC has suffered from a rather slow data acquisition rate. Here, we present a luminescent PCN-224-OH material that exhibits both fluorescent and mass dual-functionality and is enriched with Zr-OH/HO active sites. Without the additional need for complex postmodification or chemical coupling reactions, PCN-224-OH can be directly functionalized with antibodies/aptamers and poly(ethylene glycol) (PEG), resulting in the production of PCN-224-Ab-PEG or PCN-224-Apt-PEG probes. We demonstrated that PCN-224-Ab-PEG was compatible with commercial polymer-based probes but with superior sensitivity and specificity. Meanwhile, since PCN-224-Apt-PEG expressed both fluorescence and mass signals, we could adopt fluorescence signals for rapid tissue section scanning to swiftly identify the regions of interest (ROIs), and then adopt IMC for multiparameter imaging at the specific ROIs. The application of the PCN-224-Apt-PEG probe could significantly reduce the blind IMC scanning time by up to 90% and effectively compensate for IMC's low resolution. This study not only broadens the application scope of luminescent metal-organic frameworks but also offers a potentially novel toolbox for single-cell multiparameter detection.

Constructing in vitro blood-brain barrier (BBB) model provides an innovative approach for studying the pathophysiology of the brain and screening drugs. Although commercial Transwell was the simplest and most widely utilized in vitro model, reasonably mimicking essential characteristics of human BBB and dynamic monitoring BBB function remain a challenge. Herein, inspired by the highly permeable extracellular matrix membrane in human BBB, a novel in vitro BBB model was established by combining functionalized anodic alumina oxide (AAO) membrane with nanochannel electrochemistry (ANE-BBB). Benefiting from the topographical nanostructures and modified cell-adhesive peptide on the AAO surface, a tight endothelial barrier was formed, which can be directly visualized by phase-contrast microscope, and the barrier function can be real time monitored by nanochannel electrochemistry. More importantly, according to the current signal induced by the diffusion of redox species through the nanochannels toward the underlying electrode surface, dynamic evaluation of BBB-crossing behavior and precise screening of brain-targeted nanodrugs can be achieved. The constructed ANE-BBB overcomes the shortcomings of invisible cell culture, low permeability, and inaccurate real-time monitoring of screening drugs in traditional Transwell and provides a reliable tool for the design of nanodrugs to the central nervous system.

This work develops a semiempirical 1D numerical model with average measured [OH] (denoted as [OH]) as the boundary condition and measured [OH] (denoted as [OH]) to calibrate the accumulated [OH] modeled (denoted as [OH]) in the solution treated by a plasma jet. The [OH] obtained in the plasma plume is integrated from the [OH] distribution detected in the radial direction at position 1 mm above the interface. The [OH] in the solutions is determined from the fluorescence measurements by exciting 2-hydroxyterephthalic acid at 310 nm and detecting the fluorescence emitted at 425 nm for cases with different plasma treatment times. The developed numerical model considers both the diffusion and convection for the domain covering 1 mm above the interface with the dominant generation and consumption mechanisms considered in the discharge plume to evaluate the incoming flux of OH through the interface, which is calibrated with [OH] in the solution treated. The simulated results show that the transport behavior (i.e., diffusion and convection) plays only a minor role in the contribution of [OH], while the electron-impact dissociation reactions play significant roles in the generation of OH in the discharge plume, leading to the high local [OH] and incoming flux of OH to the interface. The self-association reactions of OH contribute to the remarkable consumption of OH. The simulated [OH] distribution increases from the [OH] determined at the upstream boundary to its maximum near the central region as the density reaches 9.5 × 10 m and decreases rapidly above the interface.

Untargeted liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is a powerful tool for comprehensive chemical analysis. Such techniques allow the detection and quantification of thousands of compounds in a sample. However, the complexity and variability in the data can introduce significant errors, impacting the reliability of the results. This study investigates ensemble averaging to mitigate these errors and improve signal-to-noise (S/N) ratios, feature detection, and data quality. In this work, 256 LC-qTOF/MS data sets from the analysis of Morning Glory seeds were averaged to generate merged data sets. The numbers of the pooled data sets in the merged files were varied, and the number of features, the S/N ratio, the accuracy and precision of the accurate masses, relative intensities, and migration time were examined. It was proved that ensemble averaging allows an increase in the S/N up to a factor of 10, and the relative standard deviation of the accurate masses and retention time decreased by a factor of 10. Moreover, the average number of features mined per data set increased from 1192 ± 129 with the original data set to 4408 when all data sets were averaged into one. Using known target compounds, ensemble averaging benefits on quantitative analysis were investigated. The measured and theoretical relative intensities between the [M+1]+H, [M+2]+H, and [M+3]+H and [M]+H isotopes of known alkaloids were used. The standard deviation decreased by up to a factor of 10, and the absolute error between theoretical and experimental relative intensities was below 3%, making the theoretical isotopic pattern a valid criterion for confirming a putative molecular formula. Using a targeted approach to recover quantitative data from the original data sets from information in the merged data sets provides an accurate quantitative means. Peak lists from the merged data sets and quantitative information from the original data sets were fused to obtain a robust clustering approach that allows recognizing features (adducts, isotopes, and fragments) generated by a common chemical in the ionization chamber. Two hundred and four clusters were obtained, characterized by two or more features with migration times that differ by less than 0.05 min and with similar response patterns.

Sensitive recognition of enantiomer has aroused extensive interest due to its importance in diverse fields ranging from pharmaceuticals to catalysis and biomedicine. Chiral surface-enhanced Raman scattering (SERS) by chiral plasmonic substrates has emerged as a promising tool for the recognition of enantiomers with high sensitivity as well as molecular fingerprinting capability. However, the impact of the molecular states including surface packing density and configurations of surface-adsorbed chiral molecules on chiral SERS has been largely unexplored. Herein, we demonstrate that chiral SERS by chiral plasmonic nanoparticles (NPs) is sensitively dependent on the molecular coverage of enantiomers. Au helical nanocubes with tunable optical properties and intense near-field enhancements were chosen as the chiral plasmonic substrates for chiral SERS. By changing the concentration and structure of enantiomers, the impact of the molecular states including surface packing density and configurations of enantiomers on chiral SERS is revealed. Finally, we demonstrate the use of achiral molecules as internal molecular spacers for achieving the ultrasensitive detection of enantiomer. The insights gained from this work not only shed light on the underlying mechanisms dictating the chiral SERS by chiral plasmonic NPs but also strongly suggest a promising approach to the sensitive detection of molecular chirality using Raman spectroscopy.

Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy. However, most current methods only use 1-dimensional (1D) spectral data classification, instead of considering any neighboring information in space. Despite some successes, this type of methods wastes the 3-dimensional (3D) structure of Raman hyperspectral scans. Therefore, to investigate the feasibility of preserving the spatial information on Raman spectroscopy for data analysis, spatially aware deep learning algorithms were applied into a colorectal tissue data set with 3D Raman hyperspectral scans. This data set contains Raman spectra from normal, hyperplasia, adenoma, carcinoma tissues as well as artifacts. First, a modified version of 3D U-Net was utilized for segmentation; second, another convolutional neural network (CNN) using 3D Raman patches was utilized for pixel-wise classification. Both methods were compared with the conventional 1D CNN method, which worked as baseline. Based on the results of both epithelial tissue detection and colorectal cancer detection, it is shown that using spatially neighboring information on 3D Raman scans can increase the performance of deep learning models, although it might also increase the complexity of network training. Apart from the colorectal tissue data set, experiments were also conducted on a cholangiocarcinoma data set for generalizability verification. The findings in this study can also be potentially applied into future tasks regarding spectroscopic data analysis, especially for improving model performance in a spatially aware way.

Short-chain fatty acids (SCFAs) have attracted considerable interest as potential biomarkers, therapeutic targets, and nutritional factors in athletic training. SCFAs are typically produced by the intestinal microbiome and exhibit various structural forms, including linear- and branched-chain types. In particular, branched-chain SCFAs have been associated with muscle metabolism during exercise loading. Consequently, accurate and efficient analytical methods are essential for identifying these biomarkers. Liquid chromatography-tandem mass spectrometry is a suitable and accurate technique for SCFA analysis; however, stable isotope calibrations are required for all analytes. Because of technological limitations, the available species are restricted to certain types of SCFAs. To address this issue, this study performed a simple conversion reaction involving the incorporation of O into the carboxyl group. Specifically, oxygen atoms in the carboxyl groups were substituted with O sourced from commercially available HO. An SCFA mixture standard solution was successfully labeled under optimized conditions, and the SIL purity and amount were sufficient for isotope dilution (95.2-96.9%, 250 assays using 10 μL of HO). Moreover, no reversion to O was observed during storage or analysis. Analytical validation was performed in human serum using the substituted isotopic standard mixture, achieving good accuracy (90-110%) and precision (<10% relative standard deviation) across three concentration levels. Finally, changes in SCFA patterns were examined in athletes during exercise loading.

Rapid selection methods are crucial for promoting the discovery and application of aptamers across various fields. We previously reported a highly efficient aptamer selection strategy by using unique molecular identifiers (UMIs), enabling the efficient isolation of aptamers from a single cell by only one round. The strategy integrates an ultrasensitive DNA barcoding technology with high-throughput sequencing to accurately quantify aptamer candidates, thereby mitigating issues such as PCR bias and sequence overenrichment that are inherent in traditional multiround selection. Here, we conduct a systematically theoretical analysis of this strategy in the elucidation of the theoretical basis, advantages, and applicability. The feasibility and advantages of isolating aptamers from low-enriched DNA libraries was investigated at a theoretical level, showing that this strategy is effective in reducing nonspecific binding and thus increasing the success of selecting high-affinity aptamers. Our theoretical analysis supports the broad applicability of the strategy for the single-round aptamer selection, paving the way for its widespread adoption in high-efficiency aptamer discovery and aptamer-based cell atlas.

Fatty acid esters of hydroxy fatty acid (FAHFAs) are a biologically important class of lipids known for their anti-inflammatory and antidiabetic effects in animals. The physiological activity of FAHFAs varies depending on the length of the carbon chain, number and position of double bonds (DBs), and position of the hydroxyl (OH) group. Moreover, gut bacteria produce FAHFAs with more diverse structures than those produced by the host, which necessitates a FAHFA-lipidomics approach grasping their diverse structures to fully understand the physiological and metabolic significance of FAHFAs. In this study, we developed a methodology for the in-depth structural elucidation of FAHFAs. First, FAHFAs were enriched by using a solid-phase extraction (SPE) system coated with titanium and zirconium dioxide, which separated these analytes from neutral lipids and phospholipids. The fractionated metabolites were then derivatized using -dimethylethylenediamine (DMED) to facilitate FAHFA detection in the positive ion mode of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. A data-independent acquisition technique known as sequential window acquisition of all theoretical mass spectra (SWATH-DIA) was used to collect sequential MS/MS spectra of the DMED-derivatized fatty acid metabolites. Structural elucidation was based on fragment ions generated by electron-activated dissociation (EAD). DMED-FAHFAs were annotated using the newly updated MS-DIAL program, and FAHFA isomers were quantified using the MRMPROBS program, which quantifies lipids based on SWATH-MS/MS chromatograms. This procedure was applied to profile the FAHFAs present in mouse fecal samples, characterizing seven structures at the molecular species level, 63 structures at the OH-position-resolved level, and 15 structures at both the DB- and OH-position-resolved levels, using the MS-DIAL program. In the MRMPROBS analysis, 2OH and 3OH hydroxy fatty acids with more than 20 carbon atoms were predominantly expressed, while 5OH-13OH hydroxy fatty acids with 16 or 18 carbon atoms were the major components, abundant at positions 5, 7, 9, and 10. Furthermore, age-related changes in FAHFA isomers were also observed, where FAHFA 4:0/2O(FA 26:0) and FAHFA 16:0/10O(FA 16:0) significantly increased with age. In conclusion, our study offers a novel LC-SWATH-EAD-MS/MS technique with the update of computational MS to facilitate in-depth structural lipidomics of FAHFAs.

Efficient ionization is essential for sensitive mass spectrometry (MS) analysis. Herein, a novel laser-assisted electrospray ionization (laser-assisted ESI) source was developed to efficiently ionize low-polar and thermal unstable compounds. By irradiating the electrospray nozzle with a simple and low-cost laser probe (450 nm, 500 mW), the sample droplets were stimulated with a laser beam as well as the high voltage of the electrospray, which significantly enhanced the ionization efficiency. In positive ionization mode, by further utilizing the reaction of Ag and tetrabromobisphenol A bis(allyl ether) (TBBPA-BAE), the established laser-assisted ESI strategy was able to efficiently ionize the low-polar and thermal unstable TBBPA-BAE. Specifically, a limit of detection (LOD) of 14 ng L, linear range of 0.1-10 μg L (R > 0.99), and relative standard deviations (RSDs) of 2.5% (n = 7, intraday) and 6.2% (n = 3 per day for 5 days, interday) were achieved. In negative ionization mode, laser-assisted ESI also improved the detection sensitivity of tetrabromobisphenol A (TBBPA), achieving a LOD of 3.3 ng L, linear range of 0.01-10 μg L (R > 0.99), and RSDs of 4.7% (n = 7, intraday) and 7.3% (n = 3 per day for 5 days, interday). Compared to extractive electrospray ionization mass spectrometry (EESI-MS) and ESI-MS, the LODs achieved with laser-assisted ESI-MS were 54 and 16 times lower for the detection of TBBPA-BAE and TBBPA, respectively. Notably, deep purification and preconcentration were not required to accurately detect TBBPA and TBBPA-BAE in river water and wastewater treatment plant effluent. The spiked recoveries were between 88.0% and 106.0%, demonstrating the high reliability and practicality of this method.

Individually addressable microelectrode arrays (MEAs) enable the simultaneous and independent measurement of multiple analytes and benefit from a small size scale, which enables highly localized electrochemical detection. In this work, we describe a new methodology to fabricate low-cost and tunable MEA probes in which the number, spatial arrangement, and spacing of the electrodes and electrode material can be changed and controlled. This was achieved using a 3D printed support assembly to position wires of the electrode material into designated positions and a mold to seal the electrodes in place using epoxy resin. After curing of the epoxy, mechanical polishing exposed the surface of closely spaced disk microelectrodes embedded in the insulating material, which formed the MEA. The individual electrodes of the array were characterized using electrochemical methods and optical and electron microscopy to evaluate the surface quality and the integrity of the seal with the insulating epoxy. To validate the fabrication method and to demonstrate the controlled electrode spacing, we used a dual-disk electrode device, while four-, five-, and seven-electrode probes were used to demonstrate some of the different numbers and geometric arrangements of electrodes that are possible. While the developed probes have numerous potential applications, including as probes or substrates in scanning electrochemical microscopy, we fabricated electrochemical aptamer-based sensors on the individual electrodes, for the simultaneous detection of adenosine triphosphate and dopamine in phosphate-buffered saline solution, with and without 10% fetal bovine serum.

The versatile fluorescent dyes are essential for specifically labeling plant cell walls in vivo, monitoring plasma membrane damage, and assessing cell viability. However, such dyes are rare and often discovered accidentally due to a lack of design principles. Propidium iodide, a well-known example, has limitations like low brightness, high toxicity, and poor bacterial differentiation. To address these challenges, we developed VersaDye, a modular probe designed for specific imaging of live plant cell walls and monitoring plasma membrane damage in plant cells, human cells, and certain bacteria. The design integrates impermeability principles and environment-dependent fluorophore scaffolds. VersaDye enables bright, wash-free labeling of plant cell walls and can stain various plant organs for constructing 3D tissue organization. Notably, it can selectively distinguish live Gram-positive from Gram-negative bacteria, a feature absent in other dyes. Its impermeability and targeting ability also allow it to probe membrane damage caused by physical, chemical, and biological stimuli. This study marks the first use of VersaDye in analyzing cell damage in live plants under salt stress. VersaDye offers a robust platform for wash-free, in vivo membrane damage monitoring and simultaneous cell wall labeling. Additionally, its design suggests adaptability for regulating permeability to meet specific diagnostic needs, such as identifying membrane-compromised cells in diseases or enabling high-throughput antibiotic screening targeting specific bacteria.

Tumor microenvironment (TME) is characterized by complex cellular composition and high molecular heterogeneity. Characterizing the metabolic interactions between different cells in the TME is important for understanding the molecular signatures of tumors and identifying potential metabolic vulnerabilities for tumor treatment. In this research, we develop a single-cell spatial metabolomics method to profile cell-specific metabolic signatures and cell-cell metabolic interactions using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Different low-molecular-weight metabolites and lipids including glutamate, aspartate, glutamine, taurine, phenylalanine, glutathione, fatty acids, phospholipids, etc. were successfully detected and imaged after optimizing cell culture conductive slides, cell washing, and fixation procedures. Subsequently, we carried out single-cell spatial metabolomics on H460 large-cell lung cancer cells, HT-29 colorectal cancer cells, A549 lung cancer cells, HUH-7 liver cancer cells, and cancer-fibroblasts coculture system. We revealed that the metabolic profiles of both cancer cells and fibroblasts were altered after cell coculture. Glutamate and aspartate significantly increased in fibroblasts after coculture with cancer cells, corresponding to their indispensable roles in the creation of pro-cancer microenvironment. In addition, we discovered that the expressions of fatty acids and phospholipids in tumor cells and fibroblasts were also changed after cell coculture, which is closely related to the competition for energy and nutrient metabolites between different cells. We anticipate this single-cell analysis method to be broadly used in the investigations of diverse cellular models and cell-cell metabolic interactions.