Laser-assisted rapid evaporative ionisation mass spectrometry (LA-REIMS) as a metabolomics platform in cervical cancer screening

Characterisation of Canine and Feline Breast Tumours, Their Metastases, and Corresponding Primary Cell Lines Using LA-REIMS and DESI-MS Imaging

Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

Mass spectrometry for metabolomics analysis: Applications in neonatal and cancer screening

Chemical analysis by analytical instrumentation has played a major role in disease diagnosis, which is a necessary step for disease treatment. While the treatment process often targets specific organs or compounds, the diagnostic step can occur through various means, including physical or chemical examination. Chemically, the genome may be evaluated to give information about potential genetic outcomes, the transcriptome to provide information about expression actively occurring, the proteome to offer insight on functions causing metabolite expression, or the metabolome to provide a picture of both past and ongoing physiological function in the body. Mass spectrometry (MS) has been elevated among other analytical instrumentation because it can be used to evaluate all four biological machineries of the body. In addition, MS provides enhanced sensitivity, selectivity, versatility, and speed for rapid turnaround time, qualities that are important for instance in clinical procedures involving the diagnosis of a pediatric patient in intensive care or a cancer patient undergoing surgery. In this review, we provide a summary of the use of MS to evaluate biomarkers for newborn screening and cancer diagnosis. As many reviews have recently appeared focusing on MS methods and instrumentation for metabolite analysis, we sought to describe the biological basis for many metabolomic and additional omics biomarkers used in newborn screening and how tandem MS methods have recently been applied, in comparison to traditional methods. Similar comparison is done for cancer screening, with emphasis on emerging MS approaches that allow biological fluids, tissues, and breath to be analyzed for the presence of diagnostic metabolites yielding insight for treatment options based on the understanding of prior and current physiological functions of the body.

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications

Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.

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.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

Research progress of metabolomics in cervical cancer

INTRODUCTION

Cervical cancer threatens women's health seriously. In recent years, the incidence of cervical cancer is on the rise, and the age of onset tends to be younger. Prevention, early diagnosis and specific treatment have become the main means to change the prognosis of cervical cancer patients. Metabolomics research can directly reflect the changes of biochemical processes and microenvironment in the body, which can provide a comprehensive understanding of the changes of metabolites in the process of disease occurrence and development, and provide new ways for the prevention and diagnosis of diseases.

OBJECTIVES

The aim of this study is to review the metabolic changes in cervical cancer and the application of metabolomics in the diagnosis and treatment.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2022.

RESULTS

With the emergence of metabolomics, metabolic regulation and cancer research are further becoming a focus of attention. By directly reflecting the changes in the microenvironment of the body, metabolomics research can provide a comprehensive understanding of the patterns of metabolites in the occurrence and development of diseases, thus providing new ideas for disease prevention and diagnosis.

CONCLUSION

With the continuous, in-depth research on metabolomics research technology, it will bring more benefits in the screening, diagnosis and treatment of cervical cancer with its advantages of holistic and dynamic nature.

Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

INTRODUCTION

In the UK alone, the incidence of cervical cancer is increasing, hence an urgent need for early and rapid detection of cancer before it develops. Spectroscopy in conjunction with machine learning offers a disruptive technology that promises to be pick up cancer early as compared to the current diagnostic techniques used.

AREAS COVERED

This review article explores the different spectroscopy techniques that have been used for the analysis of cervical cancer. Along with the extensive description of spectroscopic techniques, the various machine learning techniques are also described as well as the applications that have been explored in the diagnosis of cervical cancer. This review delimits the literature specifically associated with cervical cancer studies performed solely with the use of a spectroscopy technique, and machine learning.

EXPERT OPINION

Although there are several methods and techniques to detect cervical cancer, the clinical sector requires to introduce new diagnostic technologies that help improving the quality of life of patient. These innovative technologies involve spectroscopy as a qualitative method and machine learning as a quantitative method. In this article, both the techniques and methodologies that allow and promise to be a new screening tool for the detection of cervical cancer is covered.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Human Milk from Tandem Feeding Dyads Does Not Differ in Metabolite and Metataxonomic Features When Compared to Single Nursling Dyads under Six Months of Age

Given the long-term advantages of exclusive breastfeeding to infants and their mothers, there is both an individual and public health benefit to its promotion and support. Data on the composition of human milk over the course of a full period of lactation for a single nursling is sparse, but data on human milk composition during tandem feeding (feeding children of different ages from different pregnancies) is almost entirely absent. This leaves an important knowledge gap that potentially endangers the ability of parents to make a fully informed choice on infant feeding. We compared the metataxonomic and metabolite fingerprints of human milk samples from 15 tandem feeding dyads to that collected from ten exclusively breastfeeding single nursling dyads where the nursling is under six months of age. Uniquely, our cohort also included three tandem feeding nursling dyads where each child showed a preferential side for feeding-allowing a direct comparison between human milk compositions for different aged nurslings. Across our analysis of volume, total fat, estimation of total microbial load, metabolite fingerprinting, and metataxonomics, we showed no statistically significant differences between tandem feeding and single nursling dyads. This included comparisons of preferential side nurslings of different ages. Together, our findings support the practice of tandem feeding of nurslings, even when feeding an infant under six months.

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this study was to develop and test an algorithm for the differential diagnosis of cervical lesions based on lipid levels in scrapings from the cervical epithelium and cervicovaginal canal. The lipid composition of the samples was analyzed by high-performance chromato-mass spectrometry. Lipid markers were selected using the Mann–Whitney test with a cutoff value of 0.05 and by projections to latent structures discriminant analysis, where a projection threshold of one was chosen. The final selection of variables for binomial logistic regressions was carried out using the Akaike information criterion. As a result, a final neoplasia classification method, based on 20 logistic regression sub-models, has an accuracy of 79% for discrimination NILM/cervicitis/LSIL/HSIL/cancer. The model has a sensitivity of 83% and a specificity of 88% for discrimination of several lesions (HSIL and cancer). This allows us to discuss the prospective viability of further validation of the developed non-invasive method of differential diagnosis.

Rapid Evaporative Ionization Mass Spectrometry (REIMS): a Potential and Rapid Tool for the Identification of Insecticide Resistance in Mosquito Larvae

Insecticide resistance is a significant challenge facing the successful control of mosquito vectors globally. Bioassays are currently the only method for phenotyping resistance. They require large numbers of mosquitoes for testing, the availability of a susceptible comparator strain, and often insectary facilities. This study aimed to trial the novel use of rapid evaporative ionization mass spectrometry (REIMS) for the identification of insecticide resistance in mosquitoes. No sample preparation is required for REIMS and analysis can be rapidly conducted within hours. Temephos resistant Aedes aegypti (Linnaeus) larvae from Cúcuta, Colombia and temephos susceptible larvae from two origins (Bello, Colombia, and the lab reference strain New Orleans) were analyzed using REIMS. We tested the ability of REIMS to differentiate three relevant variants: population source, lab versus field origin, and response to insecticide. The classification of these data was undertaken using linear discriminant analysis (LDA) and random forest. Classification models built using REIMS data were able to differentiate between Ae. aegypti larvae from different populations with 82% (±0.01) accuracy, between mosquitoes of field and lab origin with 89% (±0.01) accuracy and between susceptible and resistant larvae with 85% (±0.01) accuracy. LDA classifiers had higher efficiency than random forest with this data set. The high accuracy observed here identifies REIMS as a potential new tool for rapid identification of resistance in mosquitoes. We argue that REIMS and similar modern phenotyping alternatives should complement existing insecticide resistance management tools.

Identification of Soil Properties Associated with the Incidence of Banana Wilt Using Supervised Methods

Over the last few decades, a growing incidence of Banana Wilt (BW) has been detected in the banana-producing areas of the central zone of Venezuela. This disease is thought to be caused by a fungal−bacterial complex, coupled with the influence of specific soil properties. However, until now, there was no consensus on the soil characteristics associated with a high incidence of BW. The objective of this study was to identify the soil properties potentially associated with BW incidence, using supervised methods. The soil samples associated with banana plant lots in Venezuela, showing low (n = 29) and high (n = 49) incidence of BW, were collected during two consecutive years (2016 and 2017). On those soils, sixteen soil variables, including the percentage of sand, silt and clay, pH, electrical conductivity, organic matter, available contents of K, Na, Mg, Ca, Mn, Fe, Zn, Cu, S and P, were determined. The Wilcoxon test identified the occurrence of significant differences in the soil variables between the two groups of BW incidence. In addition, Orthogonal Least Squares Discriminant Analysis (OPLS-DA) and the Random Forest (RF) algorithm was applied to find soil variables capable of distinguishing banana lots showing high or low BW incidence. The OPLS-DA model showed a proper fitting of the data (R2Y: 0.61, p value < 0.01), and exhibited good predictive power (Q2: 0.50, p value < 0.01). The analysis of the Receiver Operating Characteristics (ROC) curves by RF revealed that the combination of Zn, Fe, Ca, K, Mn and Clay was able to accurately differentiate 84.1% of the banana lots with a sensitivity of 89.80% and a specificity of 72.40%. So far, this is the first study that identifies these six soil variables as possible new indicators associated with BW incidence in soils of lacustrine origin in Venezuela.

Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

Automated 3D Sampling and Imaging of Uneven Sample Surfaces with LA-REIMS

The analysis of samples with large height variations remains a challenge for mass spectrometry imaging (MSI), despite many technological advantages. Ambient sampling and ionization MS techniques allow for the molecular analysis of sample surfaces with height variations, but most techniques lack MSI capabilities. We developed a 3D MS scanner for the automated sampling and imaging of a 3D surface with laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS). The sample is moved automatically with a constant distance between the laser probe and sample surface in the 3D MS Scanner. The topography of the surface was scanned with a laser point distance sensor to define the MS measurement points. MS acquisition was performed with LA-REIMS using a surgical CO₂ laser coupled to a qTOF instrument. The topographical scan and MS acquisition can be completed within 1 h using the 3D MS scanner for 300 measurement points on uneven samples with a spatial resolution of 2 mm in the top view, corresponding to 22.04 cm². Comparison between the automated acquisition with the 3D MS scanner and manual acquisition by hand showed that the automation resulted in increased reproducibility between the measurement points. 3D visualizations of molecular distributions related to structural differences were shown for an apple, a marrowbone, and a human femoral head to demonstrate the imaging feasibility of the system. The developed 3D MS scanner allows for the automated sampling of surfaces with uneven topographies with LA-REIMS, which can be used for the 3D visualization of molecular distributions of these surfaces.

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.

Molecular markers for cervical cancer screening

INTRODUCTION

Cervical cancer remains a significant healthcare problem, notably in low- to middle-income countries. While a negative test for hrHPV has a predictive value of more than 99.5%, its positive predictive value is less than 10% for CIN2+ stages. This makes the use of a so-called triage test indispensable for population-based screening to avoid referring women, that are ultimately at low risk of developing cervical cancer, to a gynecologist. This review will give an overview of tests that are based on epigenetic marker panels and protein markers.

AREAS COVERED

There is a medical need for molecular markers with a better predictive value to discriminate hrHPV-positive women that are at risk of developing cervical cancer from those that are not. Areas covered are epigenetic and protein markers as well as health economic considerations in view of the fact that most cases of cervical cancer arise in low-to-middle-income countries.

EXPERT OPINION

While there are biomarker assays based on changes at the nucleic acid (DNA methylation patterns, miRNAs) and at the protein level, they are not widely used in population screening. Combining nucleic acid-based and protein-based tests could improve the overall specificity for discriminating CIN2+ lesions that carry a low risk of progressing to cervical cancer within the screening interval from those that carry an elevated risk. The challenge is to reduce unnecessary referrals without an undesired increase in false-negative diagnoses resulting in cases of cervical cancer that could have been prevented. A further challenge is to develop tests for low-and middle-income countries, which is critical to reduce the worldwide burden of cervical cancer.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

High-Throughput UHPLC/MS/MS-Based Metabolic Profiling Using a Vacuum Jacketed Column

In UHPLC, frictional heating from the eluent flowing through the column at pressures of ca. 10-15 Kpsi causes radial diffusion via temperature differences between the center of the column and its walls. Longitudinal dispersion also occurs due to temperature gradients between the inlet and outlet. These effects cause band broadening but can be mitigated via a combination of vacuum jacketed stainless steel tubing, reduced column end nut mass, and a constant temperature in the column from heating the inlet fitting. Here, vacuum jacketed column (VJC) technology, employing a novel column housing located on the source of the mass spectrometer and minimized tubing from the column outlet to the electrospray probe, was applied to profiling metabolites in urine. For a 75 s reversed-phase gradient separation, the average peak widths for endogenous compounds in urine were 1.2 and 0.6 s for conventional LC/MS and VJC systems, respectively. The peak tailing factor was reduced from 1.25 to 1.13 when using the VJC system compared to conventional UHPLC, and the peak capacity increased from 65 to 120, with a 25% increase in features detected in urine. The increased resolving power of the VJC system reduced co-elution, simplifying MS and MS/MS spectra, providing a more confident metabolite identification. The increased LC performance also gave more intense MS peaks, with a 10-120% increase in response, improving the quality of the MS data and detection limits. Reducing the LC gradient duration to 37 s gave peak widths of ca. 0.4 s and a peak capacity of 84.

Sample Preparation Free Mass Spectrometry Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology, Metabolic Biofluid Phenotyping, and Food Authenticity

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO₂) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO₂ laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.