Rapid Evaporative Ionization Mass Spectrometry Research Articles

Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery

IntroductionThe main goal of brain tumour surgery is to maximize tumour resection while avoiding neurological deficits. Accurate characterization of tissue and delineation of resection margins are, therefore, essential to achieve optimal surgical results. ObjectivesThe primary objective of this study was to develop and validate a mass spectrometry- based technique for the molecular characterization of high- and low-grade glioma tissue during surgery. MethodsAn electrosurgical knife is connected to a mass spectrometer (iKnife). Using this system, an aerosol created during electrosurgical resection is aspirated to a mass spectrometer to determine the molecular profile of the tissue within seconds. This rapid evaporative ionization mass spectrometry (REIMS) technique is used to create a chemical profile database and develop a real-time tissue recognition system based on machine learning. ResultsClassification models were built by analysing biopsies from 36 patients who underwent brain tumour resection. Our multivariate statistical model could differentiate between astrocytoma grade II and III, glioblastoma, oligodendroglioma grade II and III, and normal brain tissue with an 88% overall accuracy. Astrocytoma and oligodendroglioma grade II were separated from normal brain with a 96% correct classification rate. REIMS could differentiate between different percentages of GBM with 99.2% sensitivity and different percentages of astrocytoma grade II with 97.5% sensitivity. ConclusionReal-time information during electrosurgical dissection can improve intra-operative decision-making, leading to a more accurate tumour removal for different glioma subtypes.

Metabolic fingerprinting using Rapid evaporative ionisation mass spectrometry can discriminate meat quality and composition of lambs from different sexes, breeds and forage systems

Assurance of food quality and allied farming systems is increasingly sought by consumers and food processors. Yet, there are no validated analytical approaches for food-based verification of farming systems. Rapid evaporative ionisation mass spectrometry (REIMS) is an emerging analytical tool that can provide sufficient details to meet this need. M. Longissimus lumborum of 10 groups of lambs (n = 140) from 3 farms, varying by breed, sex, and forage type, were measured using REIMS fingerprinting. Modelling of features detected by REIMS could discriminate for most comparisons of sex (including castration status), breed, and diet. Tentative identification suggested that lipids, hormone-related compounds, amino acids and dipeptides were the main discriminatory features. Several REIMS features were correlated with pH and shear force in Merino lambs. REIMS was able to detect features related to breed, sex and feed in lamb meat, suggesting that these characteristics can be independently measured using rapid metabolic fingerprinting.

An innovative approach for real-time authentication of cocoa butter using a combination of rapid evaporative ionization mass spectrometry and chemometrics

Cocoa butter (CB) is one of the most valuable raw materials in the chocolate industry, and its authenticity is essential to guarantee the safety and quality of chocolate products. For the first time, a real-time and high-throughput method using rapid evaporative ionization mass spectrometry (REIMS) was developed to determine the authenticity of CB. Various types of CB and cocoa butter equivalents (CBEs), as well as CB adulterated by the addition of CBEs, were measured, and different chemometric approaches were implemented for different adulteration scenarios. Hierarchical cluster analysis (HCA) was performed to obtain an overview of the dataset, exploring similarities and differences between CB and CBEs. An orthogonal partial least squares-discriminant analysis (OPLS-DA) model was developed to differentiate between authentic and adulterated CB. A classification accuracy of 100% was achieved, and CBEs could be detected at a level of 10%. A one-class support vector machine (OCSVM) model capable of detecting unsuspected adulterants was successfully built. This model had 95% specificity and 100% sensitivity. Single-origin CB was successfully differentiated from blended CB. This study demonstrated that REIMS could be used as a rapid and sensitive approach for authentication and adulteration analysis of CB. The methodology could enhance the ability of chocolate manufacturers in ensuring the authenticity of their products, helping to avoid complex and ever-changing fraudulent activities.

Real-time authentication of minced shrimp by rapid evaporative ionization mass spectrometry

Minced shrimp is popular seafood due to its delicious flavor and nutritional value. However, the biological species of raw material of minced shrimp are not distinguished by naked eyes after processing. Thus, an in situ and real-time minced shrimp authentication method was established using iKnife rapid evaporative ionization mass spectrometry (REIMS) based lipidomics. The samples were analyzed under ambient ionization without any tedious preparation step. Seven economic shrimp samples were tested, whose phenotypes were used to develop a real-time recognition model. A total of 19 fatty acids and 45 phospholipid molecular species were efficiently identified and statistically analyzed by multivariate statistical analysis. The results showed that the seven shrimp species were well distinguished, and the most contributing ions at m/z 255.2, 279.2, 301.2, 327.2, 699.5, 742.5, etc., were revealed by variable importance in projection. The proposed iKnife REIMS showed excellent performance in minced shrimp authentication.

Rapid evaporative ionization mass spectrometry (intelligent knife) for point-of-care testing in acute aortic dissection surgery.

OBJECTIVESRapid evaporative ionization mass spectrometry (REIMS) can discriminate aneurysmal from normal aortic tissue. Our objective in this work was to probe the integrity of acute dissection tissue using biomechanical, biochemical and histological techniques and demonstrate that REIMS can be used to discriminate identified differences.METHODSHuman aortic tissue was obtained from patients undergoing surgery for acute aortic dissection. Biomechanical, biochemical and histological assessment was carried out to probe mechanical properties and elastin, collagen and glycosaminoglycan composition of the tissue. Monopolar electrocautery was applied to samples and surgical aerosol aspirated and analysed by REIMS to produce mass spectral data.RESULTSTissue was obtained from 10 patients giving rise to 26 tissue pieces: 10 false lumen (FL), 10 dissection flap and 6 true lumen samples. Models generated from biomechanical and biochemical data showed that FL tissue was distinct from true lumen and dissection flap tissue. REIMS identified the same pattern being able to classify tissue types with 72.4% accuracy and 69.3% precision. Further analysis of REIMS data for FL tissue suggested patients formed 3 distinct clusters. Histological and biochemical assessment revealed patterns of extracellular matrix degradation within the clusters that are associated with altered tissue integrity identified using biomechanical testing.CONCLUSIONSStructural integrity of the FL in acute Type A dissection could dictate future clinical distal disease progression. REIMS can detect differences in tissue integrity, supporting its development as a point-of-care test to guide surgical intraoperative decision-making.

Metabolomic profiling to detect different forms of beef fraud using rapid evaporative ionisation mass spectrometry (REIMS)

Organic food fraud is a significant challenge in the food testing sector—high price premiums, ease of access to produce to be relabelled and difficulties in developing testing strategies that can detect such frauds make organic foods particularly attractive and thus highly vulnerable to fraud. Samples of conventional and organic cattle taken across meat plants in Ireland and the United Kingdom, consisting of the neck (supraspinatus), rump (gluteus), and shin (flexor carpi radialis) regions of the carcass were analysed using a high resolution time-of-flight based rapid evaporative ionisation mass spectrometry (REIMS) system. The resulting untargeted lipidomic data (m/z 600–1000) was used to generate PCA-LDA models for production system and for muscle type, for these models, it was found that the production system model could differentiate organic from conventional beef with an accuracy of 84%, whilst the muscle type model could identify the cut of meat with a 98% accuracy; additionally, samples can be assessed against multiple models simultaneously, reducing analysis time and sample numbers. The use of REIMS showed considerable promise in its ability to detect different forms of meat fraud; its accuracy in differentiating organic from conventional beef is superior to stable isotope ratio mass spectrometry, with the added advantages of substantially shorter analysis times and lower sample analysis costs. The ability to rapidly confirm the cut of meat also demonstrates the potential of REIMS to concurrently determine multiple aspects of beef authenticity in a close to real time analysis.

Smart knife: technological advances towards smart cutting tools in meat industry automation

PurposeModern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.Design/methodology/approachThis paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.FindingsOptical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.Originality/valueThis paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

A better way to determine tumour margins intraoperatively, using ambient mass spectrometry (interim report)

Introduction: Resection of head and neck cancer with positive margins is a crucial prognostic factor, yet no reliable intraoperative diagnostic adjuncts exist. Rapid Evaporative Ionisation Mass Spectrometry (REIMS/iKnife) can analyse smoke generated by dissection with electrocautery to provide near instant tissue classification. However, REIMS has never been investigated in head and neck cancer in vivo. We designed an IDEAL stage 2a developmental study, aiming to evaluate the validity of our diagnostic model, and develop a feasible protocol for a subsequent clinical trial. Methods: Previously-irradiated patients undergoing transoral robotic surgery at the Royal Marsden for recurrence were prospectively recruited (REC: 14/EE/0024). Intraoperative REIMS data was captured and synchronised with the video feed. The surgeon was blinded to REIMS data throughout the procedure. Univariate and multivariate analyses were conducted using AMX, R and Python. Results: 7 patients have been recruited to date (target 20). All were male. Median age was 63 (range 58-74). 4/7 had HPV-positive disease. REIMS signal was successfully acquired in 100% of cases, but was less reliable during deeper dissection. Tumour signals were successfully classified, as were normal tissue types such as muscle and mucosa, in over 95% of captured signal. Metabolic species potentially contributing to classification were identified, including phospholipids, glycerophospholipids and ceramides previously associated with carcinogenesis. Conclusions: REIMS near-instantaneously distinguishes head and neck cancer from normal tissue intraoperatively. We are iteratively developing engineering solutions to optimise signal collection. Recruitment is ongoing. An optimised protocol will allow design of a clinical trial to evaluate the impact of REIMS on survival outcomes. Introduction: Resection of head and neck cancer with positive margins is a crucial prognostic factor, yet no reliable intraoperative diagnostic adjuncts exist. Rapid Evaporative Ionisation Mass Spectrometry (REIMS/iKnife) can analyse smoke generated by dissection with electrocautery to provide near instant tissue classification. However, REIMS has never been investigated in head and neck cancer in vivo. We designed an IDEAL stage 2a developmental study, aiming to evaluate the validity of our diagnostic model, and develop a feasible protocol for a subsequent clinical trial. Methods: Previously-irradiated patients undergoing transoral robotic surgery at the Royal Marsden for recurrence were prospectively recruited (REC: 14/EE/0024). Intraoperative REIMS data was captured and synchronised with the video feed. The surgeon was blinded to REIMS data throughout the procedure. Univariate and multivariate analyses were conducted using AMX, R and Python. Results: 7 patients have been recruited to date (target 20). All were male. Median age was 63 (range 58-74). 4/7 had HPV-positive disease. REIMS signal was successfully acquired in 100% of cases, but was less reliable during deeper dissection. Tumour signals were successfully classified, as were normal tissue types such as muscle and mucosa, in over 95% of captured signal. Metabolic species potentially contributing to classification were identified, including phospholipids, glycerophospholipids and ceramides previously associated with carcinogenesis. Conclusions: REIMS near-instantaneously distinguishes head and neck cancer from normal tissue intraoperatively. We are iteratively developing engineering solutions to optimise signal collection. Recruitment is ongoing. An optimised protocol will allow design of a clinical trial to evaluate the impact of REIMS on survival outcomes.

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.

In Situ Quality Assessment of Dried Sea Cucumber (Stichopus japonicus) Oxidation Characteristics during Storage by iKnife Rapid Evaporative Ionization Mass Spectrometry.

Sea cucumber (Stichopus japonicus) is one of the most luxurious and nutritious seafoods in Asia. It is always processed into dried products to prevent autolysis, but its quality is easily destructed during storage. Herein, an extremely simplified workflow was established for real-time and in situ quality assessment of dried sea cucumbers (DSCs) during storage based on the lipid oxidation characteristics using an intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS). The lipidomic phenotypes of DSCs at different storage times were acquired successfully, which were then processed by multivariate statistical analysis. The results showed that the discrepancy in the characteristic ions in different DSCs was significant (p < 0.05) with high R2(Y) and Q2 values (0.975 and 0.986, respectively). The receiver operating characteristic curve revealed that the ions of m/z 739.5, m/z 831.5, m/z 847.6, and m/z 859.6 were the most specific and characteristic candidate biomarkers for quality assessment of DSCs during accelerated storage. Finally, this method was validated to be qualified in precision (RSDintraday ≤ 9.65% and RSDinterday ≤ 9.36%). In conclusion, the results showed that the well-established iKnife-REIMS method was high-throughput, rapid, and reliable in the real-time quality assessment of DSCs.

Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) is a metabolomic technique analyzing tissue metabolites, which can be applied intraoperatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification. Metabolic dysregulation is a hallmark feature of carcinogenesis; however, it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease. Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600 to 1000 m/z were used for univariate and multivariate functions including linear discriminant analysis. A total of 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96.0% accuracy and 91.8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93.5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and mono-unsaturated fatty acid-containing phospholipids. The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to riskstratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.

Lipidomic analysis and diagnosis of glioblastoma multiforme with rapid evaporative ionization mass spectrometry.

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in the central nervous system. GBM patients have a very low 5-year survival rate and most of them died within 1 year. Conventional histopathological examination for GBM diagnosis is complicated and time-consuming, which always blocks the development of more precise and effective treatments in resection operation. Rapid evaporative ionization mass spectrometry (REIMS) is a MS technique in clinical medicine research, which combines the common diathermy device with MS to acquire the lipid profiles of tissue specimens for lipidomic analysis and real-time tumor diagnosis. In this study, the REIMS method employing bipolar forceps was optimized and validated for high-throughput lipidomics and diagnosis of GBM for the first time. Total 42 lipid metabolites were tentatively identified and 12 out of 13 lipid biomarkers showed higher intensities in GBM, which were consistent with previous studies. After this, a statistic model was built with the lipidomic data for the diagnosis of GBM tumor in real-time. The diagnostic accuracy (94.74%), sensitivity (95.38%), and specificity (93.33%) were evaluated with histopathology validated brain tissue specimens that were not used in the training set. The proposed REIMS method for the lipidomic-analysis and diagnosis of GBM tumor provides a new direction for MS-based lipidomics and precision medicine and might be used to guide surgeons to precisely resect the GBM tissue and keep the normal brain tissue in operation.

Evaluation of ambient mass spectrometry tools for assessing inherent postharvest pepper quality

Horticulturists are interested in evaluating how cultivar, environment, or production system inputs can affect postharvest quality. Ambient mass spectrometry approaches enable analysis of minimally processed samples under ambient conditions and offer an attractive high-throughput alternative for assessing quality characteristics in plant products. Here, we evaluate direct analysis in real time (DART-MS) mass spectrometry and rapid evaporative ionization-mass spectrometry (REIMS) to assess quality characteristics in various pepper (Capsicum annuum L.) cultivars. DART-MS exhibited the ability to discriminate between pod colors and pungency based on chemical fingerprints, while REIMS could distinguish pepper market class (e.g., bell, lunchbox, and popper). Furthermore, DART-MS analysis resulted in the putative detection of important bioactive compounds in human diet such as vitamin C, p-coumaric acid, and capsaicin. The results of this study demonstrate the potential for these approaches as accessible and reliable tools for high throughput screening of pepper quality.

Abstract 271: High-throughput fecal metabolic profiling for the early detection of colorectal cancer using a direct mass spectrometry assay

Abstract Early stage diagnosis of colorectal cancer (CRC) is a key predictor of patient survival, highlighting the need for accurate diagnostic biomarkers. Laser Assisted - Rapid Evaporative Ionization Mass Spectrometry (LA-REIMS) demonstrates potential for direct from sample metabolic profiling of feces. Here, we present an optimized LA-REIMS approach for fecal metabolomic analysis in CRC for the first time. A prospective, observational cohort biomarker discovery study was performed at an NHS hospital trust in the UK. Patients referred through the national two week wait cancer pathway were prospectively recruited. The primary outcome was to differentiate between adenomas or CRC and non-disease controls, the secondary outcome was to compare LA-REIMS to fecal immunochemical testing (FIT) against the gold-standard of colonoscopy or CT colonography. Those under the age of 18, pregnant, or unable to undergo colonic investigation were excluded. Fecal samples were analyzed using FIT (Kyowa Medex Co Ltd, Japan), with a detection limit of 7µg haemoglobin/g feces considered a positive result, as well as a LA-REIMS setup (Xevo G2-S QTof (Waters Corporation)), with optimized laser and instrumentation parameters (improved signal-to-noise and reduced carry-over between samples). After pre-processing, univariate and multivariate statistics were carried out in MetaboAnalyst 4.0. Random forest classification (RFC) with leave one out cross-validation (LOOCV) was performed.244 subjects (120 females, mean age 67.5 (range 21-93)) were included, of which n=135 were non-disease controls, n=82 adenoma patients, and n=27 CRC patients. Partial least square-discriminant analysis (PLS-DA) demonstrated a distinction between control vs CRC (Q2= 0.60087, R2= 0.89376). RFC with LOOCV for cancer vs control showed a sensitivity of 34.6% and specificity of 99.2% compared to 81.8% sensitivity and 87.2% specificity with FIT. PLS-DA of control vs adenoma (Q2= 0.64284, R2= 0.8898) demonstrated good separation. RFC with LOOCV for adenoma vs control had a sensitivity of 61.0 % and specificity of 94.7% compared to 33.8 % sensitivity and 87.2% specificity with FIT. Univariate analysis identified a feature tentatively assigned to the glycerolipid class (area under the receiver operating characteristic curve (AUROC) 0.986 (CI=0.957-1) to be more abundant in CRC vs control (false discovery rate (FDR) corrected p&amp;lt;0.001). A tentatively assigned organic acid (AUC 0.916, CI=0.868-0.953) was associated with control (FDR corrected p&amp;lt;0.001). Multiple features in the mass-to-charge ratio (m/z) range 200-400 were found to be significantly different between control vs adenoma. Annotations of the features are underway using liquid chromatography - MS/MS and spiked standards. LA-REIMS allows direct from sample metabolite profiling of feces and has applications as a novel screening tool for the early detection of CRC. Citation Format: Petra Paizs, Monika Widlak, Alvaro Perdones-Montero, Maria Sani, Lauren Ford, James L. Alexander, Simon Cameron, Ramesh Arasaradnam, James M. Kinross, Zoltan Takats. High-throughput fecal metabolic profiling for the early detection of colorectal cancer using a direct mass spectrometry assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 271.

Discrimination of milk fermented with different starter cultures by MALDI-TOF MS and REIMS fingerprinting

Fermented milk products are valued by consumers and the food industry for their nutritional properties, pleasant taste, and texture. To rapidly discriminate the peptide and small molecular fingerprints of fermented milk with different flavour characteristics, we assessed the suitability of high-throughput protocols for the screening of these samples using rapid evaporative ionisation mass spectrometry (REIMS) and matrix-assisted laser desorption ionisation–time-of-flight mass spectrometry (MALDI-TOF MS). With minimal sample preparation, we can distinguish between different fermented milk produced from different bacterial strains. These techniques can aid in quick and cost-effective fingerprinting to screen for new and interesting fermented products and bacterial cultures. Furthermore, we have demonstrated for the first time the suitability of REIMS for the measurement of liquid dairy samples.

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.

Differentiation between Fresh and Frozen-Thawed Meat using Rapid Evaporative Ionization Mass Spectrometry: The Case of Beef Muscle.

An intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the lipidomic profiling of fresh and frozen-thawed beef muscle. The data were obtained by REIMS and then processed using multivariate statistical analysis methods including principal component analysis-linear discriminant analysis (PCA-LDA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The discrimination of fresh and frozen-thawed meat has been achieved, and the real-time identification accuracy was 92-100%. Changes in the composition and content of fatty acids and phospholipids were statistically analyzed by OPLS-DA, and the ions of m/z 279.2317, m/z 681.4830, and m/z 697.4882 were selected as differential compounds/metabolites. The developed method was also successfully applied in the discrimination of fresh and frozen-thawed meat samples. These results showed that REIMS as a high-throughput, rapid, and real-time mass spectrometry detection technology can be used for the identification of fresh and frozen-thawed meat samples.

Exploring the potential of rapid evaporative ionization mass spectrometry (Intelligent Knife) for point-of-care testing in aortic surgery.

OBJECTIVESMany intraoperative decisions regarding the extent of thoracic aortic surgery are subjective and are based on the appearance of the aorta, perceived surgical risks and likelihood of early recurrent disease. Our objective in this work was to carry out a cross-sectional study to demonstrate that rapid evaporative ionization mass spectrometry (REIMS) of electrosurgical aerosol is able to empirically discriminate ex vivo aneurysmal human thoracic aorta from normal aorta, thus providing supportive evidence for the development of the technique as a point-of-care test guiding intraoperative surgical decision-making.METHODSHuman aortic tissue was obtained from patients undergoing surgery for thoracic aortic aneurysms (n = 44). Normal aorta was obtained from a mixture of post-mortem and punch biopsies from patients undergoing coronary surgery (n = 13). Monopolar electrocautery was applied to samples and surgical aerosol aspirated and analysed by REIMS to produce mass spectral data.RESULTSModels generated from REIMS data can discriminate aneurysmal from normal aorta with accuracy and precision of 88.7% and 85.1%, respectively. In addition, further analysis investigating aneurysmal tissue from patients with bicuspid and tricuspid aortic valves was discriminated from normal tissue and each other with accuracies and precision of 93.5% and 91.4% for control, 83.8% and 76.7% for bicuspid aortic valve and 89.3% and 86.0% for tricuspid aortic valve, respectively.CONCLUSIONSAnalysis of electrosurgical aerosol from ex vivo aortic tissue using REIMS allowed us to discriminate aneurysmal from normal aorta, supporting its development as a point-of-care test (Intelligent Knife) for guiding surgical intraoperative decision-making.

Validation of Ultrasonic Harmonic Scalpel for Real-Time Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry.

In this study, we integrate rapid evaporative ionization mass spectrometry (REIMS) with the Harmonic scalpel, an advanced laparoscopic surgical instrument that utilizes ultrasound energy to dissect and coagulate tissues. It provides unparalleled manipulation capability to surgeons and has superseded traditional electrosurgical tools particularly in abdominal surgery, but is yet to be validated with REIMS. The REIMS platform coupled with the Harmonic device was shown to produce tissue-specific lipid profiles through the analysis of porcine tissues in both negative and positive ionization modes. Comparison with other methods of electrosurgical dissection, such as monopolar electrosurgery and CO2 laser, showed spectral differences in the profile dependent on the energy device used. The Harmonic device demonstrated major spectral differences in the phospholipid region of m/z 600–1000 compared with the monopolar electrosurgical and CO2 laser-generated spectra. Within the Harmonic REIMS spectra, high intensities of diglycerides and triglycerides were observed. In contrast, monopolar electrosurgical and laser spectra demonstrated high abundances of glycerophospholipids. The Harmonic scalpel was able to differentiate between the liver, muscle, colon, and small intestine, demonstrating 100% diagnostic accuracy. The validation of the Harmonic device–mass spectrometry combination will allow the platform to be used safely and robustly for real-time in vivo surgical tissue identification in a variety of clinical applications.