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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.

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.

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.

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. SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.

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.

Single particle tracking (SPT) has long been utilized for investigation of complex system dynamics such as nanoparticle-cell interaction, however, the analysis of individual particle motions is always a difficult issue. Existing methods treat each data point or fragment on the recorded trajectory as an isolated “atom” and determine their relationship based on externally predefined models or physical states, which inevitably lead to oversimplification of the associated spatiotemporal complexity. Herein, inspired by the historical analysis in social science, we propose a modeless preprocessing framework for SPT analysis based on the “history” of the particle. This new strategy consists of 3 steps: (1) assign a “history” to each data point and construct successive overlapped historical vectors; (2) perform unsupervised clustering in the vector space to find their relative differences; (3) project differences back to the trajectory by coloring each point accordingly for visualization. As a result, the inner heterogeneity of the particle motion self-emerges as a colored trajectory, exhibiting a global picture of the local state transitions and providing valuable information for further model-based analysis. Since the complexity issues at various spatiotemporal scales have attracted increasing attention, and individual objects such as single molecules, cells, vehicles and even stars in the universe could all be treated as “single particles”, this presuppositionless data preprocessing approach could help the investigations of many complex systems in fundamental research.

Liquid-liquid phase separation (LLPS) underlies the formation mechanism of membraneless biomolecular condensates locally to perform important physiological functions such as selective autophagy, but little is known about the relationship between their dynamic structural organization and biophysical properties. Here, a dark-field microscopy based single plasmonic nanoparticle tracking (DFSPT) technique was introduced to simultaneously monitor the diffusion dynamics of multiple gold nanorod (AuNR) probes in a protein LLPS system and to quantitatively characterize the spatiotemporal heterogeneity of the LLPS condensates during their phase transformation. Based on spatially and temporally resolved analysis of the diffusional behavior of the AuNRs, structure and material properties of p62 condensates, such as the viscoelasticity, the compartmentalization, and the recruitment of protein-covered nanoparticles into the large droplet, have been observed. Moreover, the nonsmooth droplet interface, its solidification after further phase transition or maturation, and the size effect of the inner vacuoles have also been revealed. Our method can be potentially applied to in vitro investigation of different reconstituted membrane-free biomolecular condensates and in vivo study of their dynamic evolution.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The Zhusileng–Hangwula tectonic zone, located in the northern Alxa area, is a key site for understanding the evolution of the Paleo-Asian Ocean (PAO) and related mineral formation. We report the major and trace elements and zircon U-Pb ages of newly discovered mafic and intermediate to felsic intrusive rocks in the Zhusileng area. LA-ICP-MS zircon U-Pb analyses of the Echenghei diabase and Haierhan and Heipingshan granites yielded crystallization ages of 305 Ma, 284 Ma, and 271 Ma, respectively. The Late Carboniferous mafic rocks have high MgO and Fe2O3T contents and low K2O and TiO2 contents, and are characterized by depletion of HREEs and notably negative Nb-Ta anomalies, with enrichment of Rb, Ba, and U, which indicate an enriched mantle source. The Early Permian granites of the Zhusileng area are highly differentiated I-type granitoids sourced from mixing of mantle and crust, are high-K calc-alkaline and enriched in LREEs, and exhibit moderately to strongly negative Eu anomalies. All the intrusive rocks formed in a post-collisional extensional environment, which indicates that the branch of the PAO in the northern Alxa area had closed before the Early Permian. The Cu-Mo/Au mineralization in the Zhusileng area formed in the Middle to Late Permian and is genetically linked to the Late Paleozoic post-collisional rifting magmatism from after the closure of the Paleo-Asian Ocean.

With the fast development of wearable medical device in recent years, it becomes critical to conduct research on continuously measured physiological signals. Entropy is a key metric for quantifying the irregularity and/or complexity contained in human physiological signals. In this review, we focus on exploring how entropy changes in various physiological signals in cardiovascular diseases. Our review concludes that the direction of entropy change relies on the physiological signals under investigation. For heart rate variability and pulse index, the entropy of a healthy person is higher than that of a patient with cardiovascular diseases. For diastolic period variability and diastolic heart sound, the direction of entropy change is reversed. Our conclusion should not only give valuable guidance for further research on the application of entropy in cardiovascular diseases but also provide a foundation for using entropy to analyze the irregularity and/or complexity of physiological signals measured by wearable medical device.

Some anticarcinogen could treat cancers through regulating the synthesis and degradation of proteins. Therefore, the rules of newly synthesized proteins would be used to evaluate the effect of anticarcinogen. In this study, we coupled noncanonical amino acid tagging with click reaction to label the newly synthesized proteins in normal cells and cancerous cells. We studied the influence of six different drugs on the protein synthesis. The results showed the differences of protein synthesis rate and spatial distribution in normal cells and cancerous cells, as well as their different responding to anticarcinogen stimulation. This approach could help us to understand the growth of proteins and distinguish cancerous cells from normal cells, which would benefit the diagnosis and monitoring of cancer, as well as evaluating the curative effect of drugs. Based on this strategy, more significant biological process about newly synthesized biomolecules would be studied in-depth.

Analyzing single particle trajectories is a prominent issue in understanding complex dynamics such as nanoparticle-cell interactions. Existing methods treat data points as isolated “atoms” and use predefined mechanical models to “frame” their complicated relationship. Herein, we propose a “historical evolution” based model-free strategy. It allows spatiotemporal heterogeneity embedded in a trajectory to self-emerge as consecutive colored segments before any model assumption, provide both an overall picture and local state transitions on the particle movement with minimum information loss, and inspire further model-based investigation. We demonstrate with simulations and experiments that the underlying mechanisms of various time-series and motion states of single nanoparticles on live cell membranes could all be revealed successfully. Since complexity studies at different levels of molecules, particles, cells, human beings, vehicles, and even stars could all be reduced to analyzing spatiotemporal trajectories of “single particles”, this presuppositionless approach will help fundamental researches on many important systems.Impact StatementA preprocessing strategy for single particle trajectory analysis is established by providing an intuitive global pattern from “historical experiences” of the particle without predefining any mechanical models.

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.

Extracellular acidity is correlated with the development of various pathological states and bulk pH measurements could not report surface acidity. In this study, we have developed a ratiometric fluorescent probe that aggregates upon interaction with cells, allowing persistent labeling of cells and in situ measurement of cell surface pH. The ternary nanoplatform is constructed by a convenient noncovalent combination of bovine serum albumin protected gold nanoclusters (BSA-AuNCs), fluorescein isothiocyanate (FITC) labeled cationic peptides (CPs), and FITC-free CPs. The red fluorescent AuNCs serve as reference fluorophore, while FITC labeled peptides act as specific recognition element for H+ and FITC unlabeled peptides are used for delivery. The probe displays a sensitive fluorescence ratiometric response for pH in the range of 5.0-9.5 with calculated p Ka of 7.2. Further studies have demonstrated that this nanosensor also has properties of high selectivity, reversibility to pH fluctuations, as well as low cytotoxicity. The new surface pH-measurement tool was validated in mapping extracellular pH and monitoring acidification regarding cell metabolism, demonstrating its potential for bioimaging and biosensing.

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.

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.

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.

After comparing with different extant editions of Nei jing (Inner Canon), the author holds that the portion of Su wen (Plain Questions) in Lei jing (Classified Canon) was similar to Hu's 12 - volume system of Gulinshutang edition, Zhao's Jujingtang edition, while its portion of Ling shu (Miraculous Pivot) is closer to the proofed edition of Zhou's and Ma Shi's annotated 9 - volume edition. After his determination of the original edition, he further made collation with comparison with other editions, and checked the original texts accordingly. Hence it was different from all other available editions.

There were not a few editions of Lei jing (Classified Canon) spread now, some of which had certain relationship in their origin. The block - printed edition of the Ming dynasty in the ministry library of Japan National Cabinet Document Library may probably be the earliest edition of Lei jing. There were two major types of edition of Lei jing in circulation, that is, the editions with and without the original contents of Nei jing (Inner Canon). The Yongquan edition of Jin Changtong and edition of Tiande Hall were the source of all the block - printed editions in China, while the Japanese block - printed edition was derived from the edition with the original contents of Nei jing of the Ming dynasty.