Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behaviors in mice.

BACKGROUND: Major depressive disorder (MDD) is a common but severe psychiatric illness characterized by depressive mood and diminished interest. Both nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 1 (NLRP1) inflammasome and autophagy have been reported to implicate in the pathological processes of depression. However, the mechanistic interplay between NLRP1 inflammasome, autophagy, and depression is still poorly known. METHODS: Animal model of depression was established by chronic social defeat stress (CSDS). Depressive-like behaviors were determined by social interaction test (SIT), sucrose preference test (SPT), open field test (OFT), forced swim test (FST), and tail-suspension test (TST). The protein expression levels of NLRP1 inflammasome complexes, pro-inflammatory cytokines, phosphorylated-phosphatidylinositol 3-kinase (p-PI3K)/PI3K, phosphorylated-AKT (p-AKT)/AKT, phosphorylated-mechanistic target of rapamycin (p-mTOR)/mTOR, brain-derived neurotrophic factor (BDNF), phosphorylated-tyrosine kinase receptor B (p-TrkB)/TrkB, Bcl-2-associated X protein (Bax)/B-cell lymphoma-2 (Bcl2) and cleaved cysteinyl aspartate-specific proteinase-3 (caspase-3) were examined by western blotting. The mRNA expression levels of pro-inflammatory cytokines were tested by quantitative real-time PCR. The interaction between proteins was detected by immunofluorescence and coimmunoprecipitation. Neuronal injury was assessed by Nissl staining. The autophagosomes were visualized by transmission electron microscopy. Nlrp1a knockdown was performed using an adeno-associated virus (AAV) vector containing Nlrp1a-shRNA-eGFP infusion. RESULTS: CSDS exposure caused a bidirectional change in hippocampal autophagy function, which was activated in the initial period but impaired at the later stage. In addition, CSDS exposure increased the expression levels of hippocampal NLRP1 inflammasome complexes, pro-inflammatory cytokines, p-PI3K, p-AKT and p-mTOR in a time-dependent manner. Interestingly, NLRP1 is immunoprecipitated with mTOR but not PI3K/AKT and CSDS exposure facilitated the immunoprecipitation between them. Hippocampal Nlrp1a knockdown inhibited the activity of PI3K/AKT/mTOR signaling, rescued the impaired autophagy and ameliorated depressive-like behavior induced by CSDS. In addition, rapamycin, an autophagy inducer, abolished NLRP1 inflammasome-driven inflammatory reactions, alleviated depressive-like behavior and exerted a neuroprotective effect. CONCLUSIONS: Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behavior in mice and the regulation of autophagy could be a valuable therapeutic strategy for the management of depression.

Factor of DNA methylation 1 affects woodland strawberry plant stature and organ size via DNA methylation.

RNA-directed DNA methylation (RdDM) is an epigenetic process that directs silencing to specific genomic regions and loci. The biological functions of RdDM are not well studied in horticultural plants. Here, we isolated the ethyl methane-sulfonate-induced mutant reduced organ size (ros) producing small leaves, flowers, and fruits in woodland strawberry (Fragaria vesca) due to reduced cell numbers compared with that in the wild-type (WT). The candidate mutation causes a premature stop codon in FvH4_6g28780, which shares high similarity to Arabidopsis (Arabidopsis thaliana) Factor of DNA Methylation1 (FDM1) encoding an RdDM pathway component and was named FveFDM1. Consistently, the fvefdm1CR mutants generated by CRISPR/Cas9 also produced smaller organs. Overexpressing FveFDM1 in an Arabidopsis fdm1-1 fdm2-1 double mutant restored DNA methylation at the RdDM target loci. FveFDM1 acts in a protein complex with its homolog Involved in De Novo 2 (FveIDN2). Furthermore, whole-genome bisulfite sequencing revealed that DNA methylation, especially in the CHH context, was remarkably reduced throughout the genome in fvefdm1. Common and specific differentially expressed genes were identified in different tissues of fvefdm1 compared to in WT tissues. DNA methylation and expression levels of several gibberellic acid (GA) biosynthesis and cell cycle genes were validated. Moreover, the contents of GA and auxin were substantially reduced in the young leaves of fvefdm1 compared to in the WT. However, exogenous application of GA and auxin could not recover the organ size of fvefdm1. In addition, expression levels of FveFDM1, FveIDN2, Nuclear RNA Polymerase D1 (FveNRPD1), Domains Rearranged Methylase 2 (FveDRM2), and cell cycle genes were greatly induced by GA treatment. Overall, our work demonstrated the critical roles of FveFDM1 in plant growth and development via RdDM-mediated DNA methylation in horticultural crops.

A cancer-targeted glutathione-gated probe for self-sufficient ST/CDT combination therapy and FRET-based miRNA imaging.

A cancer-targeted glutathione (GSH)-gated theranostic probe (CGT probe) for intracellular miRNA imaging and combined treatment of self-sufficient starvation therapy (ST) and chemodynamic therapy (CDT) was developed. The CGT probe is constructed using MnO2 nanosheet (MS) as carrier material to adsorb the elaborately designed functional DNAs. It can be internalized by cancer cells via specific recognition between the AS1411 aptamer and nucleolin. After CGT probe entering the cancer cells, the overexpressed GSH, as gate-control, can degrade MS to Mn2+ which can be used for CDT by Fenton-like reaction. Simultaneously, Mn2+-mediated CDT can further cascade with the enzyme-like activities (catalase-like activity and glucose oxidase-like activity) of CGT probe, achieving self-sufficient ST/CDT synergistic therapy. Meanwhile, the anchored DNAs are released, achieving in situ signal amplification via disubstituted-catalytic hairpin assembly (DCHA) and FRET (fluorescence resonance energy transfer) imaging of miR-21. The in vitro and in vivo experiments demonstrated that accurate and sensitive miRNA detection can be achieved using the CGT probe. Overall, the ingenious CGT probe opens a new avenue for the development of early clinical diagnosis and cancer therapy.

An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation.

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 105, with excellent repeatability. Additionally, a prototype 4 × 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

CD8+ T and NK cells characterized by upregulation of NPEPPS and ABHD17A are associated with the co-occurrence of type 2 diabetes and coronary artery disease.

Background: Coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) are closely related. The function of immunocytes in the pathogenesis of CAD and T2DM has not been extensively studied. The quantitative bioinformatics analysis of the public RNA sequencing database was applied to study the key genes that mediate both CAD and T2DM. The biological characteristics of associated key genes and mechanism of CD8+ T and NK cells in CAD and T2DM are our research focus. Methods: With expression profiles of GSE66360 and GSE78721 from the Gene Expression Omnibus (GEO) database, we identified core modules associated with gene co-expression relationships and up-regulated genes in CAD and T2DM using Weighted Gene Co-expression Network Analysis (WGCNA) and the 'limma' software package. The enriched pathways of the candidate hub genes were then explored using GO, KEGG and GSEA in conjunction with the immune gene set (from the MSigDB database). A diagnostic model was constructed using logistic regression analysis composed of candidate hub genes in CAD and T2DM. Univariate Cox regression analysis revealed hazard ratios (HRs), 95% confidence intervals (CIs), and p-values for candidate hub genes in diagnostic model, while CIBERSORT and immune infiltration were used to assess the immune microenvironment. Finally, monocytes from peripheral blood samples and their immune cell ratios were analyzed by flow cytometry to validate our findings. Results: Sixteen candidate hub genes were identified as being correlated with immune infiltration. Univariate Cox regression analysis revealed that NPEPPS and ABHD17A were highly correlated with the diagnosis of CAD and T2DM. The results indicate that CD8+ T cells (p = 0.04) and NKbright cells (p = 3.7e-3) are significantly higher in healthy controls than in individuals with CAD or CAD combined with T2DM. The bioinformatics results on immune infiltration were well validated by flow cytometry. Conclusions: A series of bioinformatics studies have shown ABHD17A and NPEPPS as key genes for the co-occurrence of CAD and T2DM. Our study highlights the important effect of CD8+ T and NK cells in the pathogenesis of both diseases, indicating that they may serve as viable targets for diagnosis and therapeutic intervention.

Automated and Miniaturized Pico-Liter Metabolite Extraction System for Single-Cell Mass Spectrometry.

OBJECTIVE: Mass spectrometry has become the method of choice for single cell analysis due to its high sensitivity of detection and capability in analyzing a large number of metabolites simultaneously. For a long time, an automated and miniaturized system capable of extracting cellular contents from single cells at the pico-liter level for pico-ESI analysis has been lacking. METHODS: This paper presents a first-of-its-kind automated and miniaturized pico-liter extraction system for single-cell MS. The key modules, including imaging, bus controller, and fluidic driving are customized to achieve satisfactory performance at affordable costs, resulting in a miniaturized system movable on a trolley and connectable with the MS. To enable automation, a single cell trapping device, new image-based one-pixel accuracy positioning methods for cells and micropipette, and a surface-tension-based 1-pL accuracy volume control scheme are developed. RESULTS: The system is able to control the solvent loading at 1.97 ± 0.05 nL, solvent dispensing at 14-15 pL, and solvent evaporation at 689±48 pL. MS experiments demonstrate a throughput of 20 cells/h. CONCLUSION: The system has achieved better performance in consistency (∼21%), sensitivity (∼28%), and success rate (up to 40%) than manual operation. SIGNIFICANCE: This automated and miniaturized system lays a solid basis for applying pico-ESI MS analysis in the automated and high-throughput single cell MS analysis, such as single-cell metabolomics and lipidomics.

Study on endogenous inhibitors against PD-L1: cAMP as a potential candidate.

The discovery of new anti-cancer drugs targeting the PD-1/PD-L1 pathway has been a research hotspot in recent years. In this study, biological affinity ultrafiltration (BAU), UPLC-HRMS, molecular dynamic (MD) simulations and molecular docking methods were applied to search for endogenous active compounds that can inhibit the binding of PD-L1 to PD-1. We screened dozens of potential cancer related endogenous compounds. Surprisingly, cyclic adenosine monophosphate (cAMP) was found to have a direct inhibitory effect on the PD-1/PD-L1 binding with an in vitro IC50 value of about 36.4 ± 9.3 µM determined by homogeneous time-resolved fluorescence (HTRF) assay. cAMP could recover the proliferation of Jurkat T cells co-cultured with DU-145 cells and may suppress PD-L1 expression of DU-145 cells. cAMP was demonstrated to bind and induce PD-L1 dimerization by FRET assay, and also predicted by MD simulations and molecular docking. The finding of cAMP as a potential inhibitor directly targeting the PD-1/PD-L1 interaction could advance our understanding of the activity of endogenous compounds regulating PD-L1.

Metabolic Pathway of Monounsaturated Lipids Revealed by In-Depth Structural Lipidomics by Mass Spectrometry.

The study of lipid metabolism relies on the characterization of the lipidome, which is quite complex due to the structure variations of the lipid species. New analytical tools have been developed recently for characterizing fine structures of lipids, with C=C location identification as one of the major improvements. In this study, we studied the lipid metabolism reprograming by analyzing glycerol phospholipid compositions in breast cancer cell lines with structural specification extended to the C=C location level. Inhibition of the lipid desaturase, stearoyl-CoA desaturase 1, increased the proportion of n-10 isomers that are produced via an alternative fatty acid desaturase 2 pathway. However, there were different variations of the ratio of n-9/n-7 isomers in C18:1-containing glycerol phospholipids after stearoyl-CoA desaturase 1 inhibition, showing increased tendency in MCF-7 cells, MDA-MB-468 cells, and BT-474 cells, but decreased tendency in MDA-MB-231 cells. No consistent change of the ratio of n-9/n-7 isomers was observed in SK-BR-3 cells. This type of heterogeneity in reprogrammed lipid metabolism can be rationalized by considering both lipid desaturation and fatty acid oxidation, highlighting the critical roles of comprehensive lipid analysis in both fundamental and biomedical applications.

Recent developments in ionization techniques for single-cell mass spectrometry.

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

Ultrathin Hydrogel Films toward Breathable Skin-Integrated Electronics.

On-skin electronics that offer revolutionary capabilities in personalized diagnosis, therapeutics, and human-machine interfaces require seamless integration between the skin and electronics. A common question remains whether an ideal interface can be introduced to directly bridge thin-film electronics with the soft skin, allowing the skin to breathe freely and the skin-integrated electronics to function stably. Here, an ever-thinnest hydrogel is reported that is compliant to the glyphic lines and subtle minutiae on the skin without forming air gaps, produced by a facile cold-lamination method. The hydrogels exhibit high water-vapor permeability, allowing nearly unimpeded transepidermal water loss and free breathing of the skin underneath. Hydrogel-interfaced flexible (opto)electronics without causing skin irritation or accelerated device performance deterioration are demonstrated. The long-term applicability is recorded for over one week. With combined features of extreme mechanical compliance, high permeability, and biocompatibility, the ultrathin hydrogel interface promotes the general applicability of skin-integrated electronics.

Near-Infrared Organic Photodetectors toward Skin-Integrated Photoplethysmography-Electrocardiography Multimodal Sensing System.

In the fast-evolving landscape of decentralized and personalized healthcare, the need for multimodal biosensing systems that integrate seamlessly with the human body is growing rapidly. This presents a significant challenge in devising ultraflexible configurations that can accommodate multiple sensors and designing high-performance sensing components that remain stable over long periods. To overcome these challenges, ultraflexible organic photodetectors (OPDs) that exhibit exceptional performance under near-infrared illumination while maintaining long-term stability are developed. These ultraflexible OPDs demonstrate a photoresponsivity of 0.53 A W-1 under 940 nm, shot-noise-limited specific detectivity of 3.4 × 1013 Jones, and cut-off response frequency beyond 1 MHz at -3 dB. As a result, the flexible photoplethysmography sensor boasts a high signal-to-noise ratio and stable peak-to-peak amplitude under hypoxic and hypoperfusion conditions, outperforming commercial finger pulse oximeters. This ensures precise extraction of blood oxygen saturation in dynamic working conditions. Ultraflexible OPDs are further integrated with conductive polymer electrodes on an ultrathin hydrogel substrate, allowing for direct interface with soft and dynamic skin. This skin-integrated sensing platform provides accurate measurement of photoelectric and biopotential signals in a time-synchronized manner, reproducing the functionality of conventional technologies without their inherent limitations.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images.

Objective: In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload. Methods: A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance. Results: The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886. Conclusion: A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS: The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION: The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

MnO2 nanosheet-mediated generalist probe: Cancer-targeted dual-microRNAs detection and enhanced CDT/PDT synergistic therapy.

While integrated nanoplatform for diagnosis and therapy has received much recent interest, its widespread application has been hampered by the complicated preparation process, high-cost and low-efficacy. Herein, we designed a MnO2 nanosheet-mediated generalist probe (MNSGP), for intracellular dual-microRNAs (miRNAs) imaging and enhanced synergistic therapy of chemodynamic therapy (CDT) and photodynamic therapy (PDT). Because MNSGP can specifically target nucleolin receptor overexpressed on the cancer cell surface, it can be internalized via a receptor-mediated endocytosis pathway. After entering the cells, MnO2 NS was degraded to Mn2+ by the excessive glutathione (GSH), releasing the DNA probes for cyclic amplification detection of miR-155 and miR-21 based on toehold-mediated strand displacement amplification (TSDA). Meanwhile, the produced O2 by MnO2 NS catalysis can promote the photosensitizer TMPyP4 to produce singlet oxygen (1O2) for PDT. The degraded Mn2+, as Fenton reagent, can convert endogenous H2O2 to cytotoxic hydroxyl radical (·OH) for CDT. In addition, the depletion of GSH impairs the antioxidant defense system (ADS), enhancing the CDT/PDT synergistic effect. The prepared generalist probe was fully characterized. Accuracy of dual-miRNAs detection and the high curative effect of enhanced CDT/PDT synergistic therapy were attested via in vitro and in vivo experiments. Unarguably, MNSGP broadens new horizons in the design of nucleic acid nanoplatform, cancer-targeted detection and theranostic application.

Single-cell lipidomics with high structural specificity by mass spectrometry.

Single-cell analysis is critical to revealing cell-to-cell heterogeneity that would otherwise be lost in ensemble analysis. Detailed lipidome characterization for single cells is still far from mature, especially when considering the highly complex structural diversity of lipids and the limited sample amounts available from a single cell. We report the development of a general strategy enabling single-cell lipidomic analysis with high structural specificity. Cell fixation is applied to retain lipids in the cell during batch treatments prior to single-cell analysis. In addition to tandem mass spectrometry analysis revealing the class and fatty acyl-chain for lipids, batch photochemical derivatization and single-cell droplet treatment are performed to identify the C=C locations and sn-positions of lipids, respectively. Electro-migration combined with droplet-assisted electrospray ionization enables single-cell mass spectrometry analysis with easy operation but high efficiency in sample usage. Four subtypes of human breast cancer cells are correctly classified through quantitative analysis of lipid C=C location or sn-position isomers in ~160 cells. Most importantly, the single-cell deep lipidomics strategy successfully discriminates gefitinib-resistant cells from a population of wild-type human lung cancer cells (HCC827), highlighting its unique capability to promote precision medicine.

Large-scale lipid analysis with C=C location and sn-position isomer resolving power.

Lipids play a pivotal role in biological processes and lipid analysis by mass spectrometry (MS) has significantly advanced lipidomic studies. While the structure specificity of lipid analysis proves to be critical for studying the biological functions of lipids, current mainstream methods for large-scale lipid analysis can only identify the lipid classes and fatty acyl chains, leaving the C=C location and sn-position unidentified. In this study, combining photochemistry and tandem MS we develop a simple but effective workflow to enable large-scale and near-complete lipid structure characterization with a powerful capability of identifying C=C location(s) and sn-position(s) simultaneously. Quantitation of lipid structure isomers at multiple levels of specificity is achieved and different subtypes of human breast cancer cells are successfully discriminated. Remarkably, human lung cancer tissues can only be distinguished from adjacent normal tissues using quantitative results of both lipid C=C location and sn-position isomers.