SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment.

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM's ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization.

The heterogeneity of oxidized lipids in individual tumor cells reveals NK cell-mediated cytotoxicity by label-free mass cytometry.

NK cell-mediated immunotherapy has received increasing attention in the past decade due to its efficacy and bio-safety. The composition and content of lipids in individual cells are closely related to NK cell-mediated cytotoxicity, especially polyunsaturated fatty acids (PUFA) which are oxidized during NK cell-mediated apoptosis. Here we investigated the changes of lipids in single HepG2 cells by label-free mass cytometry and obtained information on 53 lipids and 13 oxidized lipids after the interaction with NK92 MI cells. We found that the contents of lipids and oxidized lipids of HepG2 cells changed obviously during the NK cell-mediated apoptosis. The HepG2 cells could be classified into two phenotypes after co-culturing with NK92 MI cells based on the ratio of PC(38:6-2OH)/PC(38:6) in individual cells, which may serve as a feature to evaluate NK cell-mediated cytotoxicity. The present work used the lipids and oxidized lipids of individual cells to reveal the heterogeneity in NK cell-mediated apoptosis which would be a powerful method for evaluating the cytotoxicity of NK cells at the single-cell level.

Discriminating Leukemia Cellular Heterogeneity and Screening Metabolite Biomarker Candidates using Label-Free Mass Cytometry.

Discriminating various leukocyte subsets with specific functions is critical due to their important roles in the development of many diseases. Here, we proposed a general strategy to unravel leukocytes heterogeneity and screen differentiated metabolites as biomarker candidates for leukocyte subtypes using the label-free mass cytometry (CyESI-MS) combined with a homemade data processing workflow. Taking leukemia cells as an example, metabolic fingerprints of single leukemia cells were obtained from 472 HL-60, 416 THP-1, 313 U937, 356 Jurkat, and 366 Ramos cells, with throughput up to 40 cells/min. Five leukemia subtypes were clearly distinguished by unsupervised learning t-SNE analysis of the single-cell metabolic fingerprints. Cell discrimination in the mixed leukemia cell samples was also realized by supervised learning of the single-cell metabolic fingerprints with high recovery and good repetition (98.31 ± 0.24%, -102.35 ± 4.82%). Statistical analysis and metabolite assignment were carried out to screen characteristic metabolites for discrimination and 36 metabolites with significant differences were annotated. Then, differentiated metabolites for pairwise discrimination of five leukemia subtypes were further selected as biomarker candidates. Furthermore, discriminating cultured leukemia cells from human normal leukocytes, separated from fresh human peripheral blood, was performed based on single-cell metabolic fingerprints as well as the proposed biomarker candidates, unveiling the potential of this strategy in clinical research. This work makes efforts to realize high-throughput single-leukocyte metabolic analysis and metabolite-based discrimination of leukocytes. It is expected to be a powerful means for the clinical molecular diagnosis of hematological diseases.

Combination of Structured Illumination Microscopy with Hyperspectral Imaging for Cell Analysis.

Existing structured illumination microscopy (SIM) allows super-resolution live-cell imaging in few color channels that provide merely morphological information but cannot acquire the sample spectrum that is strongly relevant to the underlying physicochemical property. We develop hyperspectral SIM which enables high-speed spectral super-resolution imaging in SIM for the first time. Through optically mapping the three-dimensional (x, y, and λ) datacube of the sample to the detector plane, hyperspectral SIM allows snapshot spectral imaging of the SIM raw image, detecting the sample spectrum while retaining the high-speed and super-resolution characteristics of SIM. We demonstrate hyperspectral SIM imaging and reconstruct a datacube containing 31 super-resolution images of different wavelengths from only 9 exposures, achieving a 15 nm spectral resolution. We show time-lapse hyperspectral SIM imaging that achieves an imaging speed of 2.7 s per datacube-31-fold faster than the existing wavelength scanning strategy. To demonstrate the great prospects for further combining hyperspectral SIM with various spectral analysis methods, we also perform spectral unmixing of the hyperspectral SIM result while imaging the spectrally overlapped sample.

Uncover Single Nanoparticle Dynamics on Live Cell Membrane with Data-Driven Historical Experience Analysis.

Understanding the spatiotemporal dynamics of particles in a complex biological environment is crucial for the study of related biological processes. To analyze the complicated trajectories recorded from single-particle tracking (SPT), we have proposed a method named SEES based on historical experience vector analysis, which allows both the global patterns and local state continuities of a trajectory to emerge by themselves as color segments without predefined models. This method implements a data-driven strategy and thus uncovers the hidden information with less prior knowledge or subjective bias. Here, we demonstrate its efficiency by comparing its performance with the Hidden Markov model (HMM), one of the most widely used methods in time series processing. The results demonstrated that the SEES operator was more sensitive in identifying rare events and could utilize multivariable observations in the dynamic processes to uncover more details. We applied the method to analyze the dynamics of nanoparticles interacting with live cells expressing programmed death ligand 1 (PD-L1) on the membrane. The results showed that the SEES operator can successfully pinpoint the transmembrane rare events, visualize the on-membrane "Brownian searching" motion, and evaluate different dynamics among multiple trajectories. Furthermore, we found that the PD-L1 expression level on the cell membrane affected the rotation behavior of the nanoparticle as well as the cellular uptake efficiency. These findings enabled by SEES could potentially help the rational design of highly efficient nanocargoes.

In Situ Identification and Spatial Mapping of Microplastic Standards in Paramecia by Secondary-Ion Mass Spectrometry Imaging.

Microplastics (MPs) are universally present in the ecosystem and pose great threats to the environment and living organisms. Research studies have shown that small MPs (<50 µm in diameter) are especially toxic and account for more than half of all MPs collected in the Atlantic Ocean. Nevertheless, current methods for the detection and analysis of MPs are incapable of achieving rapid and in situ analysis of small MPs in the biota to ultimately enable the study of their biological effects. In this work, we report a method that allows rapid in situ identification and spatial mapping of small MPs directly from paramecia with high accuracy by acquiring chemical composition information using secondary-ion mass spectrometry (SIMS) imaging. Specifically, six types of common MPs (polymethyl methacrylate, polyvinyl chloride, polypropylene, polyethylene terephthalate, polyglycidyl methacrylate, and polyamide 6) with a diameter of 1-50 µm were simultaneously imaged with high chemical specificity at a spatial resolution of 700 nm. In situ spatial mapping of a group of MPs ingested by paramecia was performed using SIMS fragments specific to the plastic composition with no sample pretreatment, revealing the aggregation of MPs in paramecia after ingestion. Compared with existing methods, one additional advantage of the developed method is that the MPs and the organism can be analyzed in the same experimental workflow to record their fingerprint spectra, acquiring biochemical information to evaluate MP fate, toxicity, and the MP-biota interaction.

Mannose Promotes Metabolic Discrimination of Osteosarcoma Cells at Single-Cell Level by Mass Spectrometry.

Discrimination of cancer cells at the single-cell metabolic level is crucial for early diagnosis. However, some cancer cells share similar metabolic information with normal cells, making them difficult to be distinguished using mass spectrometry. Herein, we propose a method by treating osteosarcoma cells and normal human osteoblasts with mannose as a stimulant, which greatly promotes the metabolic discrimination of osteosarcoma cells at the single-cell level. The low PMI (mannose 6-phosphate isomerase) level of both osteosarcoma cell lines compared to normal human osteoblasts is the reason for the abnormal metabolic pathway of two osteosarcoma cell lines with mannose treatment. We also found that the level of hexoses-6P in osteosarcoma cells significantly increased after mannose treatment, while no such increase was found in normal human osteoblasts. The proposed method is very meaningful for early diagnosis of cancer.

Metabolic fingerprinting of cell types in mouse skeletal muscle by combining TOF-SIMS with immunofluorescence staining.

Skeletal muscle tissue is composed of various muscle cell types which differ in physiological functions. Changes in cell type composition of skeletal muscle are associated with the development of metabolic diseases. Skeletal muscle cell types are currently distinguished by immunofluorescence (IF) staining based on myosin heavy chain (MHC) isoform difference. However, it remains a challenge to provide metabolic fingerprints of different muscle cell types by IF staining. Therefore, in this study, we proposed a method to examine metabolite distribution within different cell types by time-of-flight secondary ion mass spectrometry (TOF-SIMS) with high spatial resolution. Skeletal muscle samples from C57/BL6 mice were obtained by slicing. Cell types in TOF-SIMS images were labelled corresponding to IF images from the same region of serially cut sections. Mass spectra corresponding to individual muscle cells were extracted to compare metabolic fingerprints among cell types. Skeletal muscle cells were classified into two clusters based on the mass spectra of individual cells. Unsaturated diacylglycerol (DG) and fatty acid (FA) species were found to be distributed in a cell-type dependent manner. Moreover, relative quantification showed that the content of unsaturated DGs, oleic acid and linoleic acid was higher in type I and type IIA cells than in type IIB cells. TOF-SIMS in combination with IF enables us to directly visualize metabolite distribution in different cell types, to find potential biomarkers for cell type classification. TOF-SIMS imaging coupled with IF staining has been proved to be a promising tool for metabolic fingerprinting of different skeletal muscle cell types.

Metabolic Discrimination of Breast Cancer Subtypes at the Single-Cell Level by Multiple Microextraction Coupled with Mass Spectrometry.

Discrimination of cancer subtypes at the single-cell level is critical for the early diagnosis and accurate treatment of cancer. However, the discrimination of breast cancer subtypes based on their metabolite information, which could provide a new perspective of the cellular metabolomics, is still in its infancy. Herein, a high-coverage single cell metabolic analysis was carried out for the discrimination of breast cancer subtypes by combining multiple microextraction with mass spectrometry (MS). About 4300 ion signals were extracted from each cell and assigned to lipids, energy metabolites, and so on. Based on the multivariate analysis of the metabolite information, four subtypes of breast cancer were successfully discriminated. Characteristic components of each subtype were also identified as potential biomarkers such as phosphatidylcholine (PC; PC (32:1), PC (34:1)), UDP/UDP-HexNAc, and Hex-bis-P/Hex-P). Moreover, metabolomics correlation analysis at the single-cell level further revealed the coregulation clusters of the identified components, which provided more metabolites data for bioinformatics studies. Overall, our results on single cell metabolic analysis could give new insights to precision medicine, early diagnosis, and cancer treatments.

Label-free Mass Cytometry for Unveiling Cellular Metabolic Heterogeneity.

Comprehensive analysis of single-cell metabolites is critical since differences in cellular chemical compositions give rise to specialized biological functions. Herein, we propose a label-free mass cytometry by coupling flow cytometry to ESI-MS (named CyESI-MS) for high-coverage and high-throughput detection of cellular metabolites. Cells in suspension were isolated, online extracted by sheath fluid, and lysed during gas-assisted electrospray, followed by real-time MS analysis. Hundreds of metabolites, including nucleotides, amino acids, peptides, carbohydrates, fatty acyls, glycerolipids, glycerophospholipids, and sphingolipids, were detected and identified from one single cell. Discrimination of four types of cancer cell lines and even three subtypes of breast cancer cells was readily achieved using their distinct metabolic profiles. Furthermore, we screened out 102 characteristic ions from 615 detected peak signals for distinguishing breast cancer cell subtypes and identified 40 characteristic molecules which exhibited significant differences among these subtypes and would be potential metabolic markers for clinical diagnosis. CyESI-MS is expected to be a new-generation mass cytometry for studying cell heterogeneity on the metabolic level.

Spatiotemporal Heterogeneity of Reactions in Solution Observed with High-Speed Single-Nanorod Rotational Sensing.

A high-speed darkfield microscope has been developed to monitor the rapid rotation of single gold nanorods (AuNRs) and used to study the spatiotemporal heterogeneity of chemical reactions in free solution. A wide range of viscosities from 237 cP to 0.8 cP could be detected conveniently. We studied H2 O2 decomposition reactions that were catalyzed by AuNRs coated with Pt nanodots (AuNR@PtNDs) and observed two different rotational states. The two states and their transitions are related to the production and the amalgamation of O2 nanobubbles on the nanorod surface depending on H2 O2 concentration. In addition, the local fluidic environment of pure water was found to be non-uniform in time and space. This technique could be applied to study other chemical and biochemical reactions in solution.

Characterize Collective Lysosome Heterogeneous Dynamics in Live Cell with a Space- and Time-Resolved Method.

While studies of collective cell migration and bacteria swarming have tremendously promoted our fundamental knowledge of the complex systematic phenomena, the quantitative characterization of the collective organelles movement at subcellular level is yet to be fully explored. Here we tagged the lysosomes in live cells with fluorescent probe and imaged their spatial motion with wide field microscopy. To quantitatively characterize the collective lysosomal behavior with high spatiotemporal heterogeneity dynamics, we developed the particle collective analysis (PECAN) method based on the single particle tracking techniques. Thousands of trajectories were detected and analyzed in each single cell. The reliability was validated by comparing with traditional PIV method, simulated and experimental data sets. We show that the lysosomes in live cells move collectively with spatial heterogeneous and temporal long-term correlated dynamics. Furthermore, the continuous wavelet analysis suggested the existence of collective lysosomal oscillation in mouse neural cells. Generally, our method provides a practical workflow for characterizing the collective lysosomal motions which can benefit related areas such as organelles mediated drug delivery and cell activity profiling.

Combination of Droplet Extraction and Pico-ESI-MS Allows the Identification of Metabolites from Single Cancer Cells.

We have combined droplet extraction and a pulsed direct current electrospray ionization mass spectrometry method (Pico-ESI-MS) to obtain information-rich metabolite profiling from single cells. We studied normal human astrocyte cells and glioblastoma cancer cells. Over 600 tandem mass spectra (MS2) of metabolites from a single cell were recorded, allowing the successful identification of more than 300 phospholipids. We found the ratios of unsaturated phosphatidylcholines (PCs) to saturated PCs were significantly higher in glioblastoma cells compared to normal cells. In addition, both isomeric PC (17:1) and (phosphatidylethanolamine) PE (20:1) were found in glioblastoma cells, whereas only PC (17:1) was observed in astrocyte cells. Our method paves the way to characterize the chemical contents of single cells, providing rich metabolome information. We suggest that this technique is general and can be applied to other life science studies such as differentiation and drug resistance of individual cells.

Identification and Quantitation of C═C Location Isomers of Unsaturated Fatty Acids by Epoxidation Reaction and Tandem Mass Spectrometry.

Unsaturated fatty acids (FAs) serve as nutrients, energy sources, and signaling molecules for organisms, which are the major components for a large variety of lipids. However, structural characterization and quantitation of unsaturated FAs by mass spectrometry remain an analytical challenge. Here, we report the coupling of epoxidation reaction of the C═C in unsaturated FAs and tandem mass spectrometry (MS) for rapid and accurate identification and quantitation of C═C isomers of FAs in a shotgun lipidomics approach. Epoxidation of the C═C leads to the production of an epoxide which, upon collision induced dissociation (CID), produces abundant diagnostic ions indicative of the C═C location. The total intensity of the same set of diagnostic ions for one specific FA C═C isomer was also used for its relative and absolute quantitation. The simple experimental setup, rapid reaction kinetics (<2 min), high reaction yield (>90% for monounsaturated FAs), and easy-to-interpret tandem MS spectra enable a promising methodology particularly for the analysis of unsaturated FAs in complex biological samples such as human plasma and animal tissues.

In Situ Ion-Transmission Mass Spectrometry for Paper-Based Analytical Devices.

Current detection methods for paper-based analytical devices (PADs) rely on spectroscopic and electrochemical properties, which place special requirements on the analyte or need analyte labeling. Here, ion-transmission mass spectrometry (MS) was proposed for coupling with PADs to enable rapid in situ MS analysis of the sample on paper. The sample was analyzed directly on paper via analyte ionization by ions transmitted through the paper, generated by a low-temperature plasma probe. Prior to MS analysis, the sample can be separated by paper electrophoresis or by paper chromatography, among a variety of other features offered by PADs. The versatility of this technique was demonstrated by MS analysis of a paper microarray, a mixture of amino acids, and whole blood doped with drugs on PADs.

[The influence of additional roll test on the repositioning procedure by SRM-vertigo diagnosis system for horizontal canal benign paroxysmal positional vertigo].

Objective:To evaluate the influence of an additional roll test on the repositioning procedure by SRM-vertigo diagnosis system for horizontal canal benign paroxysmal positional vertigo（HC-BPPV）. Methods:A total of 713 patients diagnosed with HC-BPPV in Department of Otolaryngology Head and Neck Surgery，the First Affiliated Hospital of Xi'an Jiaotong University from Jan 2020 to Feb 2022 were enrolled. The patients were divided into two groups by hospital card numbers, in which the number is odd were considered as group A, and the number is even were considered as group B. The group A underwent two circles of Barbecue repositioning procedure by SRM-vertigo diagnosis system, while the group B first performed an additional roll test and then underwent two circles of Barbecue repositioning procedure by SRM-vertigo diagnosis system, to observe the cure rate and compare influence of HC-BPPV by an additional roll test. The quality of life and sleep of patients before and one-month after the treatment were assessed by the dizziness handicap inventory（DHI） and the pittsburgh sleep quality（PSQI）. Results:The cure rate of group A was 63.21%, and the cure rate of group B was 87.68%，the difference between the two groups was statistically significant（P<0.05）; The DHI score of patients after the repositioning was significantly lower than that before the repositioning（P<0.05）. The PSQI score after the repositioning was significantly lower than that before the repositioning（P<0.05）. The DHI and the PSQI scores after the repositioning were significantly lower than that before the repositioning, with a statistically significant difference （P< 0.05）. The total score of DHI in group B after treatment was lower than that in group A, with a statistically significant difference（P<0.05）. The total score of PSQI in group B after treatment was lower than that in group A, with non-statistically significant difference （P< 0.05）. Conclusion:An additional roll test before the repositioning procedure by SRM-vertigo diagnosis system can significantly improve the cure rate of HC-BPPV, relieve anxiety, and improve the quality of life.

[Study on the influence of Barbecure combined with Epley on residual dizziness of horizontal canal benign paroxysmal positional vertigo by SRM-vertigo diagnosis system].

Objective:To investigate the influence of Barbecure combined with Epley on residual dizziness of horizontal canal benign paroxysmal positional vertigo（HC-BPPV） by SRM-vertigo diagnosis system. Methods:A total of 406 patients diagnosed with HC-BPPV from Nov 2021 to Nov 2022 were enrolled by rapid axial roll test and Dix-Hallpike in the department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xi'an Jiaotong University. The patients were divided into two groups by hospital card numbers, in which the numbers that were odd were considered as group A, and the numbers that were even were considered as group B. The group A underwent two circles of Barbecure repositioning procedure by SRM-vertigo diagnosis system, while the group B underwent two circles Barbecure combined with Epley repositioning procedure by SRM-vertigo diagnosis system. The treatment was stopped on the next day when two groups of patients were cured, and those who were not cured will continue treatment with the same method. Results:The cure rate of group A was 83.41%, and the cure rate of group B was 80.51%, the difference between the two groups was not-statistically significant difference（P>0.05）. The rate of residual dizziness of group A was 23.30%, the rate of residual dizziness of group B was 11.46%, the difference between the two groups was statistically significant（P<0.05）. Conclusion:The Barbecure combined with Epley otoliths repositioning maneuver by SRM-vertigo diagnosis system can significantly reduce the rate of residual dizziness after the treatment of HC-BPPV, and improve the quality of life of patients.

Dynamic metabolic change of cancer cells induced by natural killer cells at the single-cell level studied by label-free mass cytometry.

Natural killer cells (NK cells) are important immune cells which have attracted increasing attention in cancer immunotherapy. Due to the heterogeneity of cells, individual cancer cells show different resistance to NK cytotoxicity, which has been revealed by flow cytometry. Here we used label-free mass cytometry (CyESI-MS) as a new tool to analyze the metabolites in Human Hepatocellular Carcinoma (HepG2) cells at the single-cell level after the interaction with different numbers of NK92 MI cells. A large amount of chemical information from individual HepG2 cells was obtained showing the process of cell apoptosis induced by NK cells. Nineteen metabolites which consecutively change during cell apoptosis were revealed by calculating their average relative intensity. Four metabolic pathways were impacted during cell apoptosis which hit 4 metabolites including glutathione (GSH), creatine, glutamic acid and taurine. We found that the HepG2 cells could be divided into two phenotypes after co-culturing with NK cells according to the bimodal distribution of concentration of these 4 metabolites. The correlation between metabolites and different apoptotic pathways in the early apoptosis cell group was established by the 4 metabolites at the single-cell level. This is a new idea of using single-cell specific metabolites to reveal the metabolic heterogeneity in cell apoptosis which would be a powerful means for evaluating the cytotoxicity of NK cells.

Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease-related wild-type α-synuclein and six pathogenic mutants.

α-Synuclein is a central player in Parkinson's disease (PD) pathology. Various point mutations in α-synuclein have been identified to alter the protein-phospholipid binding behavior and cause PD. Therefore, exploration of α-synuclein-phospholipid interaction is important for understanding the PD pathogenesis and helping the early diagnosis of PD. Herein, a phospholipid-decorated liquid crystal (LC)-aqueous interface is constructed to investigate the binding between α-synucleins (wild-type and six familial mutant A30P, E46K, H50Q, G51D, A53E and A53T) and phospholipid. The application of deep learning analyzes and reveals distinct LC signatures generated by the binding of α-synuclein and phospholipid. This system allows for the identification of single point mutant α-synucleins with an average accuracy of 98.3±1.3% in a fast and efficient manner. We propose that this analytical methodology provides a new platform to understand α-synuclein-lipid interactions, and can be potentially developed for easy identification of α-synuclein mutations in common clinic.

Intact living-cell electrolaunching ionization mass spectrometry for single-cell metabolomics.

While single-cell mass spectrometry can reveal cellular heterogeneity and the molecular mechanisms of intracellular biochemical reactions, its application is limited by the insufficient detection sensitivity resulting from matrix interference and sample dilution. Herein, we propose an intact living-cell electrolaunching ionization mass spectrometry (ILCEI-MS) method. A capillary emitter with a narrow-bore, constant-inner-diameter ensures that the entire living cell enters the MS ion-transfer tube. Inlet ionization improves sample utilization, and no solvent is required, preventing sample dilution and matrix interference. Based on these features, the detection sensitivity is greatly improved, and the average signal-to-noise (S/N) ratio is about 20 : 1 of single-cell peaks in the TIC of ILCEI-MS. A high detection throughput of 51 cells per min was achieved by ILCEI-MS for the single-cell metabolic profiling of multiple cell lines, and 368 cellular metabolites were identified. Further, more than 4000 primary single cells digested from the fresh multi-organ tissues of mice were detected by ILCEI-MS, demonstrating its applicability and reliability.