Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints.

Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.

Accurate and High-Resolution Particle Mass Measurement Using a Peak Filtering Algorithm.

Charge detection quadrupole ion trap mass spectrometry (CD-QIT MS) is an effective way of achieving the mass analysis of microparticles with ultrahigh mass. However, its mass accuracy and resolution are still poor. To enhance the performance of CD-QIT MS, the resolution Rpeak of each peak in the mass spectra resulting from an individual particle was assessed, and a peak filtering algorithm that can filter out particle adducts and clusters with a lower Rpeak was proposed. By using this strategy, more accurate mass information about the analyzed particles could be obtained, and the mass resolution of CD-QIT MS was improved by nearly 2-fold, which was demonstrated by using the polystyrene (PS) particle size standards and red blood cells (RBCs). Benefiting from these advantages of the peak filtering algorithm, the baseline separation and relative quantification of 3 and 4 µm PS particles were achieved. To prove the application value of this algorithm in a biological system, the mass of yeast cells harvested at different times was measured, and it was found that the mixed unbudded and budded yeast cells, which otherwise would not be differentiable, were distinguished and quantified with the algorithm.

Rapid analysis and authentication of Chinese propolis using nanoelectrospray ionization mass spectrometry combined with machine learning.

In this study, we established a simple, rapid, and high-throughput method for the analysis and classification of propolis samples. We utilized nanoESI-MS to analyze 37 samples of propolis from China for the first time, obtaining characteristic fingerprint spectra in negative ion mode, which were then integrated with multivariate analysis to explore variations between water extract of propolis (WEP) and ethanol extract of propolis (EEP). Furthermore, we categorized propolis samples based on different climate zones and colors, screening 10 differential metabolites among propolis from various climate zones, and 11 differential metabolites among propolis samples of different color. By employing machine learning models, we achieved high-precision discrimination and prediction between samples from different climate zones and colors, achieving predictive accuracies of 95.6% and 85.6%, respectively. These results highlight the significant potential of the nanoESI-MS coupled with machine learning methodology for precise classification within the realm of food products.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Recent advances in entirely hand-held ionization sources for mass spectrometry.

Ambient ionization mass spectrometry (AIMS) has been developing explosively since its first debut. The ionization process was hence able to be achieved under atmospheric pressure, facilitating on-site field analysis in a variety of areas, such as clinical diagnosis, metabolic phenotyping, and surface analysis. As part of the ambitious goal of making MS a general device that can be used in everyday life, lots of efforts have been paid to miniaturize the ionization source. This review discusses avant-garde sources that could be entirely hand-held without any accessories. The structure and applications of the devices are described in detail as well. They could be expediently used in real-time and on-site analysis, presenting a great future potential for the routinizing of MS.

Correction: Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

Correction for 'Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis' by Qiuyao Du et al., Analyst, 2023, https://doi.org/10.1039/d3an01051a.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry.

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification.

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

Rapid characterization of Maillard reaction products in heat-treated honey by nanoelectrospray ionization mass spectrometry.

Amadori rearrangement products (ARPs) and α-dicarbonyl compounds (α-DCs) are critical intermediates in the Maillard chemistry. The screening of artificially heated honey (AH) is currently based on chromatography-mass spectrometry, which is commonly accompanied with the longer pretreatment and detection time. Here, low-abundance ARPs were detected directly in high-sugar environment by nanoelectrospray ionization mass spectrometry (nanoESI-MS) coupled with borosilicate glass capillaries (O-tips). When O-tips were replaced by borosilicate theta capillaries (θ-tips), the microdroplets allowed the derivatization of α-DCs to be accomplished on the millisecond timescale, rather than hours in conventional protocols. The results indicated that two ARPs and α-DCs of m/z 235 were significantly up-regulated in AH. Meanwhile, the straightforward differentiation between naturally matured honey (NH) and AH was achieved by nanoESI-MS fingerprints combined with multivariate analysis. The method may provide a rapid characterization of Maillard reaction products (MRPs), which exhibits the great application potential in other complex food matrix.

Pocket-Size Wireless Nanoelectrospray Ionization Mass Spectrometry for Metabolic Analysis of Salty Biofluids and Single Cells.

Analysis of volume-limited biological samples such as single cells and biofluids not only benefits clinical purposes but also promotes fundamental research in life sciences. Detection of these samples, however, imposes strict requirements on measurement performance because of the minimal volume and concentrated salts of the samples. Herein, we developed a self-cleaning nanoelectrospray ionization device powered by a pocket-size "MasSpec Pointer" (MSP-nanoESI) for metabolic analysis of salty biological samples with limited volume. The self-cleaning effect induced by Maxwell-Wagner electric stress helps with keeping the borosilicate glass capillary tip free from clogging and thus increasing salt tolerance. This device possesses a high sample economy (about 0.1 µL per test) due to its pulsed high voltage supply, sampling method (dipping the nanoESI tip into analyte solution), and contact-free electrospray ionization (ESI) (the electrode does not touch the analyte solution during ESI). High repeatable results could be acquired by the device with a relative standard deviation (RSD) of 1.02% for voltage output and 12.94% for MS signals of caffeine standard. Single MCF-7 cells were metabolically analyzed directly from phosphate buffered saline, and two types of untreated cerebrospinal fluid from hydrocephalus patients were distinguished with 84% accuracy. MSP-nanoESI gets rid of the bulky apparatus and could be held in hand or put into one's pocket for transportation, and it could operate for more than 4 h without recharge. We believe this device will boost scientific research and clinical usage of volume-limited biological samples with high-concentration salts in a low-cost, convenient, and rapid manner.

Revealing the In Situ Behavior of Aggregation-Induced Emission Nanoparticles and Their Biometabolic Effects via Mass Spectrometry Imaging.

Simultaneous imaging of exogenous nanomaterials and endogenous metabolites in situ remains challenging and is beneficial for a systemic understanding of the biological behavior of nanomaterials at the molecular level. Here, combined with label-free mass spectrometry imaging, visualization and quantification of the aggregation-induced emission nanoparticles (NPs) in tissue were realized as well as related endogenous spatial metabolic changes simultaneously. Our approach enables us to identify the heterogeneous deposition and clearance behavior of nanoparticles in organs. The accumulation of nanoparticles in normal tissues results in distinct endogenous metabolic changes such as oxidative stress as indicated by glutathione depletion. The low passive delivery efficiency of nanoparticles to tumor foci suggested that the enrichment of NPs in tumors did not benefit from the abundant tumor vessels. Moreover, spatial-selective metabolic changes upon NPs mediated photodynamic therapy was identified, which enables understanding of the NPs induced apoptosis in the process of cancer therapy. This strategy allows us to simultaneously detect exogenous nanomaterials and endogenous metabolites in situ, hence to decipher spatial selective metabolic changes in drug delivery and cancer therapy processes.

Screening of hepatocellular carcinoma via machine learning based on atmospheric pressure glow discharge mass spectrometry.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The diagnosis of HCC is currently based on alpha-fetoprotein detection, imaging examinations, and liver biopsy, which are expensive or invasive. Here, we developed a cost-effective, time-saving, and painless method for the screening of HCC via machine learning based on atmospheric pressure glow discharge mass spectrometry (APGD-MS). Ninety urine samples from HCC patients and healthy control (HC) participants were analyzed. The relative quantification data were utilized to train machine learning models. Neural network was chosen as the best classifier with a classification accuracy of 94%. Besides, the levels of eleven urinary carbonyl metabolites were found to be significantly different between HCC and HC, including glycolic acid, pyroglutamic acid, acetic acid, etc. The possible reasons for the regulation were tentatively proposed. This method realizes the screening of HCC via potential urine metabolic biomarkers based on APGD-MS, bringing a hopeful point-of-care diagnosis of HCC in a patient-friendly manner.

Altered prefrontal neurochemistry in the DJ-1 knockout mouse model of Parkinson's disease: complementary semi-quantitative analyses with in vivo magnetic resonance spectroscopy and MALDI-MSI.

In vivo proton magnetic resonance spectroscopy (1H-MRS) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) are two semi-quantitative analytical methods commonly used in neurochemical research. In this study, the two methods were used complementarily, in parallel, to investigate neurochemical perturbations in the medial prefrontal cortex (mPFC) of 9-month-old DJ-1 knockout mice, a well-established transgenic model for Parkinson's diseases. Convergingly, the results obtained with the two methods demonstrated that, compared with the wild-type (WT) mice, the DJ-1 knockout mice had significantly increased glutathione (GSH) level and GSH/glutamate (Glu) ratio in the mPFC, which likely presented an astrocytic compensatory mechanism in response to elevated regional oxidative stress induced by the loss of DJ-1 function. The results from this study also highlighted (1) the need to be cautious when interpreting the in vivo 1H-MRS results obtained from aged transgenic animals, in which the concentration of internal reference, being whether water or total creatine, could no longer be assumed to be the same as that in the age-matched WT animals, and (2) the necessity and importance of complementary analyses with more than one method under such circumstances.

Rapid detection and classification of hongmu by atmospheric pressure ionization mass spectrometry.

Hongmu, a Chinese customary noun representing 29 kinds of wood species such as some Pterocarpus species (abbreviated as spp. hereinafter), Dalbergia spp. and Diospyros spp., is popular among Chinese people due to the furniture made from it. The slow regeneration of hongmu resources led to a decline in production, making hongmu prices high and illegal businesses profit from it. Therefore, it is necessary to identify and distinguish different varieties of hongmu for commercial trade. Herein, a cost-effective and rapid methodology was first developed via atmospheric pressure glow discharge mass spectrometry (APGD-MS) to classify three Dalbergia spp. and three Pterocarpus spp. Meanwhile, principal component analysis (PCA) was further applied to distinguish wood species and six kinds of hongmu extracts were able to be approximately separated into six units. Besides, hongmu could be clearly distinguished from their counterfeits, such as Guibourtia spp., using the method provided here. This method may provide a timely and necessary way for the determination of ingredients and identification of the authenticity of hongmu.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine.

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancer management for early diagnosis and treatment. Urine is a highly accessible specimen type and urine metabolic fingerprints (UMFs) reflect underlying metabolite signatures of GU cancers. Herein, rapid screening of GU cancers is performed using high-throughput extraction of UMFs by mass spectrometry and efficient recognition by machine learning (ML). GU cancer patients can be distinguished with an accuracy of 90.1%. Besides, key biomarkers such as citric acid were found remarkably upregulated in cancer groups, indicating the dysregulated pathways. This approach highlights the potential role of ML in clinical application and demonstrates the expanding utility of UMFs in disease screening.

Distribution and metabolism of daidzein and its benzene sulfonates in vivo (in mice) based on MALDI-TOF MSI.

Daidzein (D1) has been proved to be of great benefit to human health. More and more attention was paid to the metabolic process of D1. Most studies focused on the metabolites of D1 and analogs were determined through the excretion of animals and humans by traditional HPLC-MS, while their in situ distribution and metabolism in organs in vivo has not been reported. In our group, novel daidzein sulfonate derivatives were synthesized and confirmed to have excellent pharmaceutical properties. They exhibited good anti-inflammatory, inhibitory activities on human vascular smooth muscle cell proliferation and other bioactivities. Compared with traditional analytical methods, matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging (MALDI-TOF MSI) can directly analyze the distribution of compounds in tissues and organs. In this study, we investigate the in situ distribution and metabolism of D1 and its derivatives (DD2, DD3) in the organs of mice based on MALDI-TOF MSI for the first time. Trace prototype compounds were detected in the plasma 4 h after the intravenous injection of D1, DD2, and DD3. Seven phase I metabolites and seven phase II metabolites were detected. D1 sulfates were found in the plasma and in organs except the heart. The presence of D1 and DD3 monosulfates in the brain indicated that they could penetrate the blood-brain barrier. DD2 and DD3 could be hydrolyzed into D1 and their metabolic pathways were similar to those of D1. In addition, a ligand-receptor docking of D1 and DD2 with mitogen-activated protein kinase 8 (JNK1) was performed because of their significant anti-inflammatory activities through the JNK signaling pathway. It showed that the binding energy of DD2 with JNK1 was obviously lower than that of D1 which was consistent with their anti-inflammatory activities. It provided a theoretical basis for further validation of their anti-inflammatory mechanism at the protein level. In summary, the research will provide beneficial guidance for further pharmacological, toxicological studies and the clinical-use research of these compounds.

Biofluids Metabolic Profiling Based on PS@Fe3O4-NH2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening.

Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH2-coated polystyrene@Fe3O4 magnetic beads (PS@Fe3O4-NH2 MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe3O4-NH2 MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.