Sequential Detection of Lipids, Metabolites, and Proteins in One Tissue for Improved Cancer Differentiation Accuracy

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Combination of Internal Extractive Electrospray Ionization Mass Spectrometry and Statistical Analysis for High-Throughput Molecular Differentiation of Rehmannia glutinosa Samples

Rehmannia glutinosa (Gaert.) Libosch. ex Fisch. et Mey. is a perennial herb of the Scrophulariaceae family, which has long enjoyed a good reputation in China, and has a wide range of pharmacological effects and clinical applications. The place of origin is an important factor affecting the chemical composition of R. glutinosa, resulting in different pharmacological effects. Herein, internal extractive electrospray ionization mass spectrometry (iEESI-MS) combined with statistical techniques was established for high-throughput molecular differentiation of different R. glutinosa samples. Dried and processed R. glutinosa samples from four different places of origin were analyzed by iEESI-MS with high throughput (>200 peaks) and rapidness (<2 min/sample) without sample pretreatment. Clear separation models created by OPLS-DA were then established for distinguishing the places of origin of dried and processed R. glutinosa by using the obtained MS data. In addition, the molecular differences between the pharmacological effects of dried and processed R. glutinosa were also investigated by OPLS-DA, and 31 different components were screened out. This work provides a promising method for evaluating the quality of traditional Chinese medicines and studying the biochemical mechanism of processing.

Online Sequential Determination of Organic/Inorganic Lead Speciation in PM2.5 Using Electrochemical Mass Spectrometry

The information regarding the occurrence and abundance of lead (Pb) in PM2.5 is useful for the evaluation of air pollution status and tracing the pollution source. Herein, electrochemical mass spectrometry (EC-MS) for sequential determination of Pb species in PM2.5 samples without sample pretreatment has been developed using the combination of online sequential extraction with mass spectrometry (MS) detection. Four kinds of Pb species including water-soluble Pb compounds, fat-soluble Pb compounds, water/fat-insoluble Pb compounds, and a water/fat-insoluble Pb element were sequentially extracted from PM2.5 samples, in which water-soluble Pb compounds, fat-soluble Pb compounds, and water/fat-insoluble Pb compounds were extracted sequentially by elution using H₂O, CH₃OH, and EDTA-2Na as the eluent respectively, while the water/fat-insoluble Pb element was extracted by electrolysis using EDTA-2Na as the electrolyte. The extracted water-soluble Pb compounds, water/fat-insoluble Pb compounds, and water/fat-insoluble Pb element were transformed into EDTA-Pb in real time for online electrospray ionization mass spectrometry analysis, while the extracted fat-soluble Pb compounds were directly detected by electrospray ionization mass spectrometry. The advantages of the reported method include the obviation of sample pretreatment, high speed of analysis (<60 min/sample), low detection limit (0.16 pg), low sample consumption (30 μg), and high accuracy (>90%), which indicates the potential of this method for the rapid quantitative species detection of metals in environmental particulate matter samples.

Metabolite Fingerprinting for Identification of Panax ginseng Metabolites Using Internal Extractive Electrospray Ionization Mass Spectrometry

Ginseng, a kind of functional food and medicine with high nutritional value, contains various pharmacological metabolites that influence human metabolic functions. Therefore, it is very important to analyze the composition and metabolites of ginseng. However, the analysis of active metabolites in ginseng samples usually involves various experimental steps, such as extraction, chromatographic separation, and characterization, which may be time-consuming and laborious. In this study, an internal extractive electrospray ionization mass spectrometry (iEESI-MS) method was developed to analyze active metabolites in ginseng samples with sequential sampling and no pretreatment. A total of 44 metabolites, with 32 ginsenosides, 6 sugars, and 6 organic acids, were identified in the ginseng samples. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot showed a clear separation of ginseng samples from different origins, indicating that metabolic changes occurred under different growing conditions. This study demonstrated that different cultivation conditions of ginseng can be successfully discriminated when using iEESI-MS-based metabolite fingerprints, which provide an alternative solution for the quality identification of plant drugs.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Panax notoginseng is a highly valuable and widely used herb in traditional Chinese medicine. The quality and efficacy of Panax notoginseng grown under different conditions can greatly vary due to the differences in chemical composition. The analysis of chemical composition in Panax notoginseng typically involves various experimental steps including extraction, chromatographic separation and characterization, which can be time- and labor-consuming. Therefore, the efficient quality assessment and control of Panax notoginseng requires the development of more rapid methods for the chemical characterization and classification of Panax notoginseng. In this study, a method based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) was developed to characterize chemical components of Panax notoginseng samples under different growth conditions (e.g., place of origin, soil quality, growth season) at the speed of 0.5 min per sample, without sample pretreatment and chromatographic separation. A total of 35 chemical components, including sugars, saponins, organic acids, etc., were identified in Panax notoginseng samples. Clear separation was observed in the multivariate analysis of the iEESI-MS data from Panax notoginseng samples grown under different conditions. The difference in the content of sucrose, fructose, Rg1, Rf, Rb1, Noto-R1, malonyl-Rb1, malonyl-Rg1, malonyl-Rf, Rd, Re, linoleic acid, palmitic acid and malic acid can be used as key characteristic indicators to discriminate origin, commercial specifications, and cultivation conditions of Panax notoginseng samples. The results of our study indicate the high power of iEESI-MS for the rapid molecular characterization and classification of Panax notoginseng under different growth conditions, which can be used for the quality assessment of traditional herbal medicines as well as in pharmaceutical and clinical analysis.

Review of Recent Advances in Lipid Analysis of Biological Samples via Ambient Ionization Mass Spectrometry

The rapid and direct structural characterization of lipids proves to be critical for studying the functional roles of lipids in many biological processes. Among numerous analytical techniques, ambient ionization mass spectrometry (AIMS) allows for a direct molecular characterization of lipids from various complex biological samples with no/minimal sample pretreatment. Over the recent years, researchers have expanded the applications of the AIMS techniques to lipid structural elucidation via a combination with a series of derivatization strategies (e.g., the Paternò–Büchi (PB) reaction, ozone-induced dissociation (OzID), and epoxidation reaction), including carbon–carbon double bond (C=C) locations and sn-positions isomers. Herein, this review summarizes the reaction mechanisms of various derivatization strategies for C=C bond analysis, typical instrumental setup, and applications of AIMS in the structural elucidation of lipids from various biological samples (e.g., tissues, cells, and biofluids). In addition, future directions of AIMS for lipid structural elucidation are discussed.

Ambient electric arc ionization for versatile sample analysis using mass spectrometry

A novel, convenient ambient electric arc ionization (AEAI) device was developed as a mass spectrometry ion source for versatile sample analysis. AEAI could be considered as a soft ionization technique in which the protonated ion ([M + H]⁺) is the main ion species with little or no in-source fragmentation for most analytes. Coupled with a high-resolution Orbitrap mass spectrometer, AEAI could be applied to the analysis of a variety of organic compounds having a wide range of polarities, ranging from non-polar species such as polybenzenoid aromatic hydrocarbons (PAHs) to highly polar species such as amino acids. With its versatile capabilities in the mass spectrometric analysis of small molecules, AEAI has the potential to be an alternative to traditional ionization methods such as electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and electron impact (EI) ionization. The limitations of AEAI are also discussed.

Evaluation of the phytotoxicity of nano-particles on mung beans by internal extractive electrospray ionization mass spectrometry

The wide application of nano-particles (NPs) has raised a serious concern over their impact on plants. However, evaluation of the effects of NPs on plant metabolism by direct detection of chemicals inside solid tissues presents a challenge. In this study, we report on a direct ionization method in mass spectrometry, internal extractive electrospray ionization (iEESI), for the direct evaluation of phytotoxicity of three different NPs (including CdTe quantum dots (CdTe QDs), gold nano-particles (Au NPs), and silver nano-particles (Ag NPs)) both on surfaces and inside solid tissues from the mung bean seeds (Vigna radiata) that were cultured in aqueous solutions of three NPs at 50 μg mL^-1. The results showed that NPs could stimulate the biological accumulation of trigonelline and the decomposition of polysaccharides/oligosaccharides to glucose and maltose within 21 h of culture. To the best of our knowledge, this is the first study to apply internal extractive electrospray ionization mass spectrometry (iEESI-MS) for the direct measurement of solid tissue samples to evaluate the phytotoxicity of NPs on mung bean sprouts. Our study lays a solid foundation for further examination of other NPs-induced damaging effects such as apoptosis/necrosis, helping us to understand the phytotoxicity of NPs on plants.

Direct analysis of metabolites in the liver tissue of zebrafish exposed to fiproles by internal extractive electrospray ionization mass spectrometry

Exploring the metabolic disturbance of fipronil and its derivatives in aquatic organisms may provide a more comprehensive understanding of the impact of fipronil on the ecological environment. In this work, internal extractive electrospray ionization mass spectrometry (iEESI-MS) was used to directly analyze metabolites in the liver tissue of zebrafish exposed to fipronil and its three derivatives. Partial least squares-discriminant analysis (PLS-DA) revealed that 32 signals were considered as differential signals in zebrafish after the exposure treatment of fipronil and its derivatives, including phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), phosphatidylethanolamines (PEs), fatty acids and so on. The pathway analysis result showed that both fipronil and its derivatives have a significant impact on the glycerophospholipid metabolism of zebrafish. Besides, the intensities of PC signals in the liver samples of each group showed such a trend: mixed fiprole exposed group > fipronil sulfone exposed group ≈ fipronil sulfide exposed group > fipronil exposed group > fipronil desulfinyl exposed group > control group, indicating that mixed exposure of fipronil and its derivatives exhibited more significant metabolic disturbance in zebrafish. Taken together, iEESI-MS is applied to environmental toxicology and investigating the metabolic disturbance induced by fipronil and its derivatives in aquatic organisms, providing a new analytical method for this field.

Direct quantitative profiling of amino acids in tissues for the assessment of lung cancer

Direct molecular analysis of tissue samples is a promising approach to increase the accuracy, speed and molecular specificity of cancer diagnosis. Herein, alterations of amino acids between human lung cancer tissues and matched adjacent normal tissues were profiled by internal extraction electrospray ionization mass spectrometry (iEESI-MS). The results indicated that the levels of 11 detected amino acids (including serine, proline, valine, threonine, asparagine, aspartic acid, methionine, histidine, phenylalanine, arginine and tyrosine) in the cancerous tissues were lower than that in the adjacent normal tissues. Based on the orthogonal partial least squares discriminant analysis (OPLS-DA) model, cancerous and adjacent normal tissues were clearly discriminated, and the amino acids that played the major role in the differentiation between cancerous and adjacent normal tissues were identified. Moreover, metabolic pathway analysis revealed alterations of differential amino acids in several metabolic pathways upon lung cancer. The current study extends the power of iEESI-MS as a promising tool for quantitative characterization of amino acids in tissues, and allows the study of alterations in amino acids metabolism associated with the development of lung cancer.

Roles of Lipid Profiles in Human Non-Small Cell Lung Cancer

Aims: This review aims to identify lipid biomarkers of non-small cell lung cancer (NSCLC) in human tissue samples and discuss the roles of lipids in tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment. Methods: A review of the literature focused on lipid-related research using mass spectrometry (MS) techniques in human NSCLC tissues from January 1, 2015, to November 20, 2020, was conducted. The quality of included studies was assessed using the QUADAS-2 tool. Results: Twelve studies met the inclusion criteria and were included in the review. The risk of bias was unclear in the majority of the studies. The contents of lipids including fatty acids, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl inositol, cardiolipin, phosphatidyl serine, phosphatidyl glycerol, ceramide, lysophosphatidylethanolamine, lysophosphatidylcholine, and lysophosphatidylglycerol differed significantly between cancer and healthy tissues. The sensitivity or specificity of the discrimination model was reported in 8 studies, and the sensitivity and specificity varied among the reported methods. The lipid profiles differed between adenocarcinoma and squamous cell carcinoma NSCLC subtypes. Conclusion: In preclinical studies, MS analysis and multiple discrimination models can be combined to distinguish NSCLC tissues from healthy tissues based on lipid profiles, which provides a new opportunity to evaluate the surgical margin and cancer subtype intraoperatively. Future studies should provide guidance for selecting patients and discrimination models to develop an improved method for clinical application.

Metabolic response of Citrus limon to Asian citrus psyllid infestation revealed by EESI-MS and HPLC

Asian citrus psyllid (ACP) causes direct and indirect damage to the citrus industry. Extractive electrospray ionization mass spectrometry (EESI-MS) and high performance liquid chromatography (HPLC) were used to detect the metabolites of C. limon leaves at 0, 12, 24, and 72 h after ACP treatment. The EESI-MS results showed that ACP infestation significantly affected metabolites within a short feeding duration with 8 metabolites identified. The metabolites in leaves of these four groups could be distinguished, with 55 peaks showing significant differences including methyl N-methylanthranilate, caffeic acid, and syringic acid. The quantification of 15 phenolic compounds with HPLC-UV method in C. limon leaves after ACP infestation showed that the total content of them reached a peak of 3504.69 μg g^-1 at 12 h, with 9 phenolic compounds changing significantly (P < 0.05). A total of 21 metabolites identified in this study were involved in the biosynthesis pathways of flavonoid, flavone and flavonol, isoflavonoid and phenylpropanoid, and the degradation of aminobenzoate. Contents of epicatechin and caffeic acid increased with the feeding time of ACP as detected by both EESI-MS and HPLC. This may be related to plant defense. This study provides novel insights into the biochemical relationship of ACP and its host plants.

Ambient mass spectrometry for the molecular diagnosis of lung cancer

Lung cancer is one of the most common malignancies and the leading cause of cancer-related death worldwide. Among the technologies suitable for the rapid and accurate molecular diagnosis of lung cancer, ambient mass spectrometry (AMS) has gained increasing interest as it allows the direct profiling of molecular information from various biological samples (e.g., tissue, serum, urine and sputum) in real-time and with minimal or no sample pretreatment. This minireview summarizes the applications of AMS in lung cancer studies (including tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment), and discusses the challenges and perspectives of AMS in the clinical precision molecular diagnosis of lung cancer.

Comparative study of alterations in phospholipid profiles upon liver cancer in humans and mice

Comparative studies of molecular alterations upon cancer between mice and humans are of great importance in order to determine the relevance of research involving mouse cancer models to the development of diagnostic and therapeutic approaches in clinical practice as well as for the mechanistic studies of pathology in humans. Herein, using molecular fingerprinting by internal extractive electrospray ionization mass spectrometry (iEESI-MS), we identified 50 differential signals in mouse liver tissue and 62 differential signals in human liver tissue that undergo significant intensity alterations (variable importance in the project (VIP) >1.0) upon liver cancer, out of which only 27 were common in both mouse and human tissues. Out of the 27 common differential signals, six types of phospholipids were also identified to undergo significant alterations in human serum upon liver cancer, including PC(34:2), PC(36:4), PC(38:6), PC(36:2), PC(38:4) and PC(42:9). Statistical analysis of the relative intensity distribution of these six identified phospholipids in serum allowed confident determination of liver cancer in humans (sensitivity 91.0%, specificity 88.0%, and accuracy 90.0%). Our results indicate that, despite the significant difference in the overall alterations of phospholipid profiles upon liver cancer between humans and mice, the six identified 'core' differential phospholipids of liver cancer found in the liver tissues of both humans and mice as well as in human serum show high potential as a minimal panel for the rapid targeted diagnosis of liver cancer with high accuracy, sensitivity and specificity using direct mass spectrometry (MS) analysis.

Direct differentiation of whole blood for forensic serology analysis by thread spray mass spectrometry

Direct analysis of whole blood on bloodstained textiles is achieved with thread spray mass spectrometry (MS). This capability satisfies investigators' first priority in crime scene investigations, which is determining if a stain is blood. This thread spray method explores the use of evidentiary fabric threads for rapid determination of hemoglobin directly from whole blood within textiles without prior extraction steps. The multiplicity of information that can be derived from the thread spray MS method distinguishes it from the current presumptive Bluestar® method, by enabling the detection of hemoglobin (both α- and β-chains), the heme co-factor and lipids all from a single blood sample. Lipid composition was found to differ for blood samples originating from human, canine, and horse species. The robustness of the thread spray MS method as a forensic analytical platform was evaluated in three ways: (1) its successful applicability to samples previously tested by the Bluestar® presumptive method, offering a confirmatory test without prior sample pre-treatment, (2) successful detection of heme from previously washed fabrics, which demonstrated the unprecedented sensitivity of the thread spray method, and (3) the ability to analyze samples stored under ambient conditions for up to 30 days. These results attest to the potential capabilities of the thread spray MS platform in forensic serology, and its application for direct analysis of evidentiary garments, which confer the advantages of rapid analysis and the reduction of the false positive and negative identification rates for blood on textiles.

Selective detection of phospholipids in human blood plasma and single cells for cancer differentiation using dispersed solid-phase microextraction combined with extractive electrospray ionization mass spectrometry

Rapid and selective determination of phospholipids in microvolume biofluid samples for cancer differentiation was achieved by d-SPME–iEESI-MS.