Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Plasma drug screening using paper spray mass spectrometry with integrated solid phase extraction

Drug overdoses have risen dramatically in recent years. We developed a simple nontargeted method using a disposable paper spray cartridge with an integrated solid phase extraction column. This method was used to screen for ~160 fentanyl analogs, synthetic cannabinoids, other synthetic drugs, and traditional drugs of abuse in over 300 authentic overdose samples collected at emergency departments in Indianapolis. A solid phase extraction step was implemented on the paper spray cartridge to enable subnanograms per milliliter synthetic drugs screening in plasma. Analysis was performed on a quadrupole orbitrap mass spectrometer using the sequential window acquisition of all theoretical fragment ion spectra approach in which tandem mass spectrometry was performed using 7 m/z isolation windows in the quadrupole. Calibration curves with isotopically labeled internal standards were constructed for 35 of the most frequently encountered synthetic and traditional illicit drugs by US toxicology labs. Additional qualitative-only drugs in a suspect screening list were also included. Limits of detection in plasma for synthetic cannabinoids ranged from 0.1 to 0.5 and 0.1 to 0.3 ng/mL for fentanyl and its analogs and between 1 and 5 ng/mL for most other drugs. Relative matrix effects were evaluated by determining the variation of the calibration slope in 10 different lots of biofluid and found to be between 3% and 20%. The method was validated on authentic overdose samples collected from two emergency departments in Indianapolis, Indiana, from suspected or known overdoses. Commonly detected synthetic drugs included fentanyl related substances, designer benzodiazepines such as flubromazolam, and the synthetic cannabinoid 5F-PB-22.

Enhanced Compound Analysis Using Reactive Paper Spray Mass Spectrometry: Leveraging Schiff Base Reaction for Amino Acid Detection

Paper spray mass spectrometry (PS-MS) has evolved into a promising tool for monitoring reactions in thin films and microdroplets, known as reactive PS, alongside its established role in ambient and direct ionization. This study addresses the need for rapid, cost-effective methods to improve analyte identification in biofluids by leveraging reactive PS-MS in clinical chemistry environments. The technique has proven effective in derivatizing target analytes, altering hydrophobicity to enhance elution and ionization efficiency, and refining detection through thin-film reactions on paper, significantly expediting reaction rates by using amino acids (AAs) as model analytes. These molecules are prone to interacting with substrates like paper, impeding elution and detection. Additionally, highly abundant species in biofluids, such as lipids, often suppress AA ionization. This study employs the Schiff base (SB) reaction utilizing aromatic aldehydes for AA derivatization to optimize reaction conditions time, temperature, and catalyst presence and dramatically increasing the conversion ratio (CR) of formed SB. For instance, using leucine as a model AA, the CR surged from 57% at room temperature to 89% at 70 °C, with added pyridine during and after 7.5 min, displaying a 43% CR compared to the bulk reaction. Evaluation of various aromatic aldehydes as derivatization agents highlighted the importance of specific oxygen substituents for achieving higher conversion rates. Furthermore, diverse derivatization agents unveiled unique fragmentation pathways, aiding in-depth annotation of the target analyte. Successfully applied to quantify AAs in human and rat plasma, this reactive PS-MS approach showcases promising potential in efficiently detecting conventionally challenging compounds in PS-MS analysis.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Integrating Post-Ionization Separation via Differential Mobility Spectrometry into Direct Analysis in Real Time Mass Spectrometry for Toy Safety Screening

Direct analysis in real time (DART) enables direct desorption and ionization of analytes, bypassing the time-consuming chromatographic separation traditionally required for mass spectrometry (MS) analysis. However, DART-MS suffers from matrix interference of complex samples, resulting in compromised detection sensitivity and quantitation accuracy. In this study, DART-MS was combined with differential mobility spectrometry (DMS) to provide an additional dimension of post-ionization ion mobility separation within a millisecond time scale, compensating for the lack of separation in DART-MS analysis. As proof-of-concept, primary aromatic amines (PAAs), a class of potentially hazardous chemicals, were analyzed in various toy products, including bubble solutions, finger paints, and plush toys. In addition to commercial Dip-it glass rod and metal mesh sampling tools, a customized rapid extractive evaporation device was designed for the accelerated extraction and sensitive analysis of solid toy samples. The incorporation of DMS in DART-MS analysis enabled the rapid separation and differentiation of isomeric analytes, leading to improved accuracy and reliability. The developed protocols were optimized and validated, achieving good linearity with correlation coefficients greater than 0.99 and acceptable repeatability with relative standard deviations less than 10%. Moreover, satisfactory sensitivity was realized with limits of detection and quantitation ranges of 0.2-5 and 1-20 μg/kg (μg/L) for the 11 PAA analytes. The established methodology was applied for the analysis of real toy samples (n = 18), which confirmed its appealing potential for toy safety screening and consumer health protection.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI-MS) and multivariate classification

This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fibromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire-physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classification using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fibromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classification sensitivity, and associated with the VAS symptom achieved 100% classification specificity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially significant for classification according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fibromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classification ability of different datasets. The good classification results obtained confirm this technique is as a good analytical tool for the detection of fibromyalgia, and provides theoretical support for other studies about fibromyalgia diagnosis.

Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study

Cancer tissues reflect a greater number of pathological characteristics of cancer compared to cancer cells, so the evaluation of cancer tissues can be effective in determining cancer treatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues and even identify molecules while preserving spatial information. Cluster analysis of cancer tissues' MSI data is currently used to evaluate the phenotype heterogeneity of the tissues. Interestingly, it has been reported that phenotype heterogeneity does not always coincide with genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few gene mutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminal breast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides (TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly the proportion of PC and TG correlated with the proportion of cancer and stroma on HE images. Furthermore, the number of carbons in these lipid class varied from cluster to cluster. This was consistent with the fact that enzymes that synthesize long-chain fatty acids are increased through cancer metabolism. It was then thought that clusters containing PCs with high carbon counts might reflect high malignancy. These results indicate that lipidomics-based phenotype heterogeneity could potentially be used to classify cancer for which genetic analysis alone is insufficient for classification.

20 abnormal metabolites of Stage IV Grade C periodontitis was discovered by CPSI-MS

Saliva is a noninvasive biofluid that contains the metabolic signature of severe periodontitis (SP, Stage IV and Grade C). Conductive polymer spray ionization mass spectrometry (CPSI-MS) was used to record a wide range of metabolites within a few seconds, making this technique a promising point-of-care method for the early detection of SP (Stage IV and Grade C). Saliva samples from 31 volunteers, consisting of 16 healthy controls (HC) and 15 patients with SP (Stage IV and Grade C), were collected to identify dysregulated metabolites. Twenty metabolites were screened out, including seven amino acids. Moreover, the results showed that amino acid metabolism is closely related to the development of periodontitis. The present study further confirmed that salivary metabolites in the oral cavity were significantly altered after plaque removal. These results suggest that the combination of CPSI-MS is a feasible tool for preclinical screening of SP (Stage IV and Grade C).

Ambient Ionization Mass Spectrometry: Application and Prospective

Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry

Frozen section examination could provide pathological diagnosis for surgery of thyroid nodules, which is time-consuming, skill- and experience-dependent. This study developed a rapid classification method for thyroid nodules and machine learning. Total 69 tissues were collected including 43 nodules and 26 nodule-adjacent tissues. Intraoperative frozen section was first performed to give accurate diagnosis, and the rest frozen specimen were pretreated for probe electrospray ionization mass measurement. By multivariate analysis of mass scan data, a series compounds were found downregulated in the extraction solution of papillary thyroid carcinoma (PTC), but some were found upregulated by mass spectrometry imaging. m/z 758.5713 ([PC[34:2] + H]⁺ ), m/z 772.5845 ([PC[32:0] + K]⁺ ), and m/z 786.6037 ([PC[36:2] + H]⁺ ) were firstly identified as potential biomarkers for nodular goiter (NG). Machine learning was employed by means of support vector machine (SVM) and random forest (RF) algorithms. For classification of PTC from NG, SVM and RF algorithms exhibited the same performance and the concordance was 94.2% and 94.4% between prediction and pathological diagnosis with positive and negative mass dataset, respectively. For the classification of PTC from PTC adjacent tissues, SVM was better than RF and the concordance was 93.8% and 83.3% with positive and negative mass dataset, respectively. With the identified compounds as training features, the sensitivity and specificity are 87.5% and 88.9% for the test set. The developed method could also correctly predict the malignancy of one medullary thyroid carcinoma and one adenomatous goiter (benign). The diagnosis time is about 10 min for one specimen, and it is very promising for the intraoperative diagnosis of papillary thyroid carcinoma.

Identification of alkaloidal compounds from leaves and roots of Stephania succifera by HPLC-QTOF-MS and prediction of potential bioactivity with PharmMapper

INTRODUCTION

The roots of Stephania succifera are used in traditional medicine for the treatment of several diseases. Research on this plant has mainly focused on bioactive alkaloids from the roots, and no previous work on compounds from the abundant leaves has yet been reported.

OBJECTIVE

To identify and compare alkaloidal compounds in S. succifera roots and leaves and to predict the potential bioactivity of some alkaloids.

METHODS

High-performance liquid chromatography with quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF-MS/MS) was employed to identify alkaloidal compounds from S. succifera. The potential targets and bioactivities of most alkaloids were predicted using the PharmMapper server.

RESULTS

Fifty-six alkaloidal compounds, including protoberberine-, aporphine-, proaporphine-, benzylisoquinoline-, and lactam-type alkaloids, were identified or tentatively identified in S. succifera roots and leaves based on the HPLC-MS data. Forty-one compounds have not been previously reported in S. succifera and eight of them have not been previously reported in the literature. Twenty-four alkaloidal compounds were found in both roots and leaves. Twelve potential targets with different indications were predicted for some alkaloids.

CONCLUSION

Comparison of chemical constituents and their potential bioactivities for S. succifera roots and leaves indicated that diverse bioactive alkaloids were present in the leaves as well as the roots. PharmMapper provided new directions for bioactivity screening. This study will be helpful for further understanding the medicinal components of S. succifera and the rational utilisation of plant resources.

Systematic Optimization of Ambient Ionization Ion Mobility Mass Spectrometry for Rapid Separation of Isomers

Current methods typically used for metabolite screening and disease diagnosis often require extensive sample preparation, which increases analysis time and associated costs. While ambient ionization techniques enable the analysis of various samples in complex matrices with little or no sample preparation in a short time (typically within a minute), their reduced selectivity, even when coupled with high-resolution mass spectrometers, limits their application in certain fields. In this study, we have optimized the coupling of paper spray (PS) and leaf spray (LS) ambient ionization techniques with a commercially available ion mobility mass spectrometer (IM-MS) and demonstrated the separation of geometric and constitutional isomers. Ambient ionization techniques allow simultaneous introduction and ionization of samples, while background noise and matrix interference from paper and leaf substrates are filtered out by IM separation, resulting in high sensitivity and selectivity of the PS-IM-MS and LS-IM-MS workflows. In addition, we introduced a novel approach to perform single-field collision cross section (CCS) measurements, which resulted in CCS values that differ by 0.15% and 0.25% from traditional stepped-field and single-field methods, respectively. In addition, we used advanced computational tools to confidently identify analyte structures by comparing CCS values from experimental IM measurements and theoretical calculations. These results suggest that the coupling of ambient ionization methods with ion mobility techniques enables rapid, sensitive, and highly selective analysis that can be used in different fields, such as agrochemical screening and disease diagnostics.

Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

Distinguishing between Isobaric Ions Using Microdroplet Hydrogen-Deuterium Exchange Mass Spectrometry

Isobaric ions having the same mass-to-charge ratio cannot be separately identified by mass spectrometry (MS) alone, but this limitation can be overcome by using hydrogen-deuterium exchange (HDX) in microdroplets. Because isobaric ions may contain a varied number of exchangeable sites and different types of functional groups, each one produces a unique MS spectral pattern after droplet spray HDX without the need for MS/MS experiments or introduction of ion mobility measurements. As an example of the power of this approach, isobaric ions in urinary metabolic profiles are identified and used to distinguish between healthy individuals and those having bladder cancer.

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

On-Surface Alcohol Oxidation Monitored by Paper Spray Mass Spectrometry: The Role of Ruthenium as Catalyst

Paper spray ionization mass spectrometry (PS-MS) is employed herein as a convenient platform to investigate an on-surface catalytic process, that is, the oxidation of alcohols induced by ruthenium salts. The tag-charged benzyl alcohol 1 (m/z 166), used as a suitable prototype starting substrate, is quickly oxidized by tert-butyl hydroperoxide (TBHP) in an on-surface process catalyzed by ruthenium trichloride (RuCl₃). The PS(+)-MS revealed the formation of products from the oxidation of alcohol 1. RuCl₃ and TBHP played a crucial role in this process since when salts of other metals (platinum, palladium, and iron) and another oxidizing agent (hydrogen peroxide) are employed, no reaction is observed. Moreover, UV radiation and heating accelerate the on-surface alcohol 1 oxidation. Finally, an exciting possibility is to employ PS-MS to investigate similar organic catalytic reactions to accelerate them and detect unstable intermediates, indiscernible in the condensed phase.

Ultrasonic Sputter Desorption Mass Spectrometry Technique for Minimally Invasive Tissue Analysis

Minimally invasive techniques for in vivo tissue analysis are desired by life science and medical research. Herein, a new ionization interface coupled with ultrasonic sputter desorption (USD) was developed for in vitro and in vivo tissue analysis. Sample molecules were effectively sputtered out when the high-frequency ultrasonic probe touched the tissue. Then, the sputtered molecules were collected and ionized by a custom-made heated quartz tube and finally analyzed by mass spectrometry (MS) online. The sample pretreatment of the USD-MS technique was quite simple and required no other steps except for wetting the tissue surface with ethanol to assist molecular extraction. Experimental results demonstrated that the proposed method was suitable for the analysis of different morphologies of tissues (such as liver, brain, kidney, and lung) and performed well in the analysis of liver tumors and paracancerous tissues. Moreover, as the proposed method caused little damage to the tissues during analysis, rats and mice with orthotopic tumors still survived after the experiments. Overall, the newly developed USD-MS technique was an effective tool for minimally invasive tissue analysis and could be used as a new candidate method for in situ and real-time analysis of biological tissues in vitro and in vivo.

Applications of ambient ionization mass spectrometry in 2020: An annual review

Recent developments in mass spectrometry (MS) analyses have seen a concerted effort to reduce the complexity of analytical workflows through the simplification (or removal) of sample preparation and the shortening of run-to-run analysis times. Ambient ionization mass spectrometry (AIMS) is an exemplar MS-based technology that has swiftly developed into a popular and powerful tool in analytical science. This increase in interest and demonstrable applications is down to its capacity to enable the rapid analysis of a diverse range of samples, typically in their native state or following a minimalistic sample preparation approach. The field of AIMS is constantly improving and expanding, with developments of powerful and novel techniques, improvements to existing instrumentation, and exciting new applications added with each year that passes. This annual review provides an overview of applications of AIMS techniques over the past year (2020), with a particular focus on the application of AIMS in a number of key fields of research including biomedical sciences, forensics and security, food sciences, the environment, and chemical synthesis. Novel ambient ionization techniques are introduced, including picolitre pressure-probe electrospray ionization and fiber spray ionization, in addition to modifications and improvements to existing techniques such as hand-held devices for ease of use, and USB-powered ion sources for on-site analysis. In all, the information provided in this review supports the view that AIMS has become a leading approach in MS-based analyses and that improvements to existing methods, alongside the development of novel approaches, will continue across the foreseeable future.

Remodeling nanoDESI Platform with Ion Mobility Spectrometry to Expand Protein Coverage in Cancerous Tissue

Nanospray desorption electrospray ionization mass spectrometry is an ambient ionization technique that is capable of mapping proteins in tissue sections. However, high-abundant molecules or isobaric interference in biological samples hampers its broad applications in probing low-abundant proteins. To address this challenge, herein we demonstrated an integrated module that coupled pneumatic-assisted nanospray desorption electrospray ionization mass spectrometry with high-field asymmetric ion mobility spectrometry. Using this module to analyze mouse brain sections, the protein coverage was significantly increased. This improvement allowed the mapping of low-abundant proteins in tissue sections with a 5 μm spatial resolution enabled by computationally assisted fusion with optical microscopic images. Moreover, the module was successfully applied to characterize melanoma in skin tissues based on the enhanced protein profiles. The results suggested that this integrating module will be potentially applied to discover novel proteins in cancers.

Data-driven development of liquid chromatography-mass spectrometry methods for combined sample matrices

Herbal medicine formulas (HMFs), the combinations of two or more herbal medicine (HM) ingredients required in a single prescription, are a typical kind of combined sample matrices. LC-MS is a powerful platform for the analyses of such complex samples. The optimization of separation conditions may require a lot of experiments, because multiple analytes need to be separated from a plethora of possible interfering compounds in the sample mixture containing different herbal medicines. To greatly reduce the complexity needed for the optimization of separation conditions, this work proposes a data-driven approach for the systematic development of LC-MS methods for HMFs, using six HMFs created from four HMs (Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Corydalis Rhizoma and Ophiopogonis Radix) as case-studies. In this approach, the chromatographic peak parameters (like retention times) of the analytes and interfering compounds under different separation conditions were extracted from the LC-MS database of the HMs. Then data-driven models between the chromatographic peak parameters and the separation parameters were built with machine learning methods (r > 0.996 for all the compounds) and used to predict the chromatographic peaks of the analytes and interfering compounds in HMF analyses. Based on the predictions, all of the separation parameters were optimized without any previous experiments on the HMFs. In the validation experiments for the six HMFs, all of the analytes were well separated. The data-driven approach demonstrated enables systematic and rapid development of LC-MS methods for HMFs, and the separation conditions can be efficiently adjusted for different analytes.

One-step hexaplex immunoassays by on-line paper substrate-based electrospray ionization mass spectrometry for combined cancer biomarker screening

Mass spectrometry (MS) is attractive as a multiplexed immunoassay readout benefiting from its high sensitivity, speed and mass resolution. Here, a simple paper-based hexaplex immunoassay with an on-line MS readout was proposed, using functionalized paper as the immune substrates, along with rhodamine-based mass tags assembled on gold nanoparticles prepared as the mass probes (MPs). Simultaneous immune capture and labeling were conducted in one step on paper substrates in 96-well plates with a high throughput within 30 minutes, and the on-line efficient dissociation of the mass tags highly facilitated the hexaplex readout of the immune signals by a newly established on-line paper substrate-based electrospray ionization-MS setup. Six MPs were synthesized for the simultaneous quantification of six important cancer protein markers (cancer antigen 15-3, cancer antigen 19-9, carcinoma embryonic antigen, cancer antigen 125, human epididymis protein 4, and alpha fetoprotein) using only 10 μL serum, presenting satisfactory sensitivity, accuracy and specificity. This platform was further tested in screening for the six biomarkers in serum samples of patients with breast, liver and gastric cancers, showing its high potential for sensitive and specific early cancer diagnosis.

Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model's molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

Paper Spray Ionization Mass Spectrometry as a Potential Tool for Early Diagnosis of Cervical Cancer

Squamous intraepithelial lesion is an abnormal growth of epithelial cells on the surface of the cervix that may lead to cervical cancer. Analytical protocols for the determination of squamous intraepithelial lesions are in high demand, since cervical cancer is the fourth most diagnosed cancer among women in the world. Here, paper spray ionization mass spectrometry (PSI-MS) is used to distinguish between healthy (negative for intraepithelial lesion or malignancy) and diseased (high-grade squamous intraepithelial lesion) blood plasmas. A total of 86 blood samples of different women (49 healthy samples, 37 diseased samples) were collected, and the plasmas were prepared. Then, 10 μL of each plasma sample was deposited onto triangular papers for PSI-MS analysis. No additional step of sample preparation was necessary. The interval-successive projection algorithm linear discriminant analysis (iSPA-LDA) was applied to the PSI mass spectra, showing six ions (mostly phospholipids) that were predictive of healthy and diseased plasmas. Values of 77% accuracy, 86% sensitivity, 80% positive predictive value (PPV), and 75% negative predictive value (NPV) were achieved. This study provides evidence that PSI-MS may potentially be used as a fast and simple analytical technique for the early diagnosis of cervical cancer.

Coulometry-assisted quantitation in spray ionization mass spectrometry

The concentration of target analyte in a mixture can be quantified by combining coulometric measurements with spray ionization mass spectrometry. A three-electrode system screen printed on the polymer support acts both as the coulometry platform for electrochemical oxidation and the sample loading tip for spray ionization. After loading a droplet of the analyte solution onto the tip, two steps were taken to implement quantitation. First, the electrochemical oxidation potential was optimized with cyclic voltammetry followed by coulometric measurements to calculate the amount of oxidized analyte under a constant low voltage within a fixed period of time (5 s). Then, a high voltage (+4.5 kV) was applied to the tip to trigger spray ionization for measuring the oxidation yield from the native analyte ion and its oxidized product ion intensities by mass spectrometry. The analyte's native concentration is quantified by dividing the oxidized product's concentration (based on Coulomb's law) and the oxidation yield (estimated from mass spectrometry [MS] assuming that the parent and oxidation product have nearly the same ionization efficiencies). The workflow has an advantage in being free of any standard for constructing the quantitation curve. Several model compounds (tyrosine, dopamine, and angiotensin II) were selected for method validation. It was demonstrated that this strategy was feasible with an accuracy of ~15% for a wide coverage of different species including endogenous metabolites and peptides. As an example of its possible practical use, it was initially employed to make a bilirubin assay in urine.

Covalent organic frameworks-based paper solid phase microextraction combined with paper spray mass spectrometry for highly enhanced analysis of tetrabromobisphenol A

COFs-based paper solid phase microextraction-paper spray mass spectrometry was developed for tetrabromobisphenol A detection with enhanced analysis performance.