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Laser desorption ionisation (LDI) is generally considered to be an inferior ionisation modality to matrix assisted LDI (MALDI), providing information solely on lipids with low sensitivity. The current study demonstrates that the combination of ambient LDI with in-source surface-induced declustering provides sensitivity and chemical coverage comparable to MALDI. The setup was characterised for infrared laser desorption using two different laser systems and was successfully used for ambient mass spectrometric imaging. 20 µm spatial resolution was achieved with oversampling, approaching single-cell resolution, while metabolites and lipids ranging from amino acids through carbohydrates and nuclear bases to complex glycolipids were successfully detected. The technique was also tested as a platform for MS-guided surgery, raising the possibility of using a single technique for generating histological and in-vivo data. The results suggest that the new method can form the basis for a new histological classification system for surgery and pathology environments closing this 150 year old diagnostic gap.

Significance Clearance of surgical margins in early cervical cancer prevents the need for adjuvant chemoradiation and associated morbidity and allows fertility preservation. Clearance of disease is also crucial in the surgical management of local recurrence of cervical tumors with exenterative surgery. In this study intelligent knife technology was able to discriminate healthy from abnormal lesions on the cervix with high accuracy, highlighting the potential to improve intraoperative management of women treated surgically for cervical cancer and, as a result, patient outcomes. While pilot experiments in vivo are encouraging, accuracy remains to be validated in larger patient cohorts. Future studies could also explore whether this technology could be used for management of cervical preinvasive disease., Clearance of surgical margins in cervical cancer prevents the need for adjuvant chemoradiation and allows fertility preservation. In this study, we determined the capacity of the rapid evaporative ionization mass spectrometry (REIMS), also known as intelligent knife (iKnife), to discriminate between healthy, preinvasive, and invasive cervical tissue. Cervical tissue samples were collected from women with healthy, human papilloma virus (HPV) ± cervical intraepithelial neoplasia (CIN), or cervical cancer. A handheld diathermy device generated surgical aerosol, which was transferred into a mass spectrometer for subsequent chemical analysis. Combination of principal component and linear discriminant analysis and least absolute shrinkage and selection operator was employed to study the spectral differences between groups. Significance of discriminatory m/z features was tested using univariate statistics and tandem MS performed to elucidate the structure of the significant peaks allowing separation of the two classes. We analyzed 87 samples (normal = 16, HPV ± CIN = 50, cancer = 21 patients). The iKnife discriminated with 100% accuracy normal (100%) vs. HPV ± CIN (100%) vs. cancer (100%) when compared to histology as the gold standard. When comparing normal vs. cancer samples, the accuracy was 100% with a sensitivity of 100% (95% CI 83.9 to 100) and specificity 100% (79.4 to 100). Univariate analysis revealed significant MS peaks in the cancer-to-normal separation belonging to various classes of complex lipids. The iKnife discriminates healthy from premalignant and invasive cervical lesions with high accuracy and can improve oncological outcomes and fertility preservation of women treated surgically for cervical cancer. Larger in vivo research cohorts are required to validate these findings.

Rapid evaporative ionization mass spectrometry (REIMS) is a highly versatile technique allowing the sampling of a range of biological solid or liquid samples with no sample preparation. The cost of such a direct approach is that certain sample types provide only moderate amounts of chemical information. Here, we introduce a matrix assisted version of the technique (MA-REIMS), where an aerosol of a pure solvent, such as isopropanol, is mixed with the sample aerosol generated by rapid evaporation of the sample, and it is shown to enhance the signal intensity obtained from a REIMS sampling event by over 2 orders of magnitude. Such an increase greatly expands the scope of the technique, while providing additional benefits such as reducing the fouling of the REIMS source and allowing for a simple method of constant introduction of a calibration correction compound for accurate mass measurements. A range of experiments are presented in order to investigate the processes that occur within this modified approach, and applications where such enhancements are critical, such as intrasurgical tissue identification, are discussed.

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.

Introduction/Background Endometrial cancer is the most common gynaecological cancer in the western world with 88’000 new cases diagnosed per year in the European Union. Diagnosis of endometrial cancer is confirmed by histological examination of endometrial curetting’s or outpatient endometrial biopsies e.g. pipelle; but can take up to two weeks. To-date there is no Point-Of-Care (POC) diagnosis available. The intelligent surgical knife (’iKnife’) analyses tissue real-time but has never been used to diagnose endometrial cancer. This study aims to establish whether the iKnife could distinguish between normal and malignant endometrial pipelle biopsy samples based on differences in their lipidomic profiles. Methodology Research pipelle biopsy samples were obtained for women needing biopsies for clinical reasons (samples concurrently sent to conventional histopathology). A Waters G2-XS Xevo Q-Tof mass spectrometer (MS) was used in this study in conjunction with a modified handheld diathermy (collectively coined the ‘iKnife’). The resultant surgical aerosol containing ionic species produced during diathermy was then analysed with this technology; producing spectra that are background subtracted, lock mass corrected and in the phospholipid range. Principal component analysis (PCA) and linear discriminant analysis (LDA) were then performed to find the variance in spectral signatures. A leave one patient out cross validation was used to obtain diagnostic accuracy. Results 134 pipelle biopsy samples (73 normal and 61 malignant) were obtained. Each tissue sample was processed as described; producing an individual spectrum per burn. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differences in their unique phospholipid spectral signatures. Cross validation revealed a diagnostic accuracy of 80%. Conclusion This pilot study is the first to use the iKnife as a tool to differentiate between normal and malignant endometrial pipelle samples. These results are encouraging and suggest that the iKnife could be used as an adjunct in clinic to provide a POC diagnosis. Disclosure Nothing to disclose.

The morphological transformation of beef tissues after various processing treatments facilitates the addition of cheap offal products. Undetectable to the naked eye, analytical techniques are required to identify such scenarios within minced and processed products. DNA methodologies are ill-equipped to detect adulteration of offal cuts from the same species and vibrational spectroscopic studies, although rapid and non-destructive, have proved inconclusive as to whether the specific adulterant can be identified. For the first time we present a mass spectrometric approach employing an ambient ionisation process to eliminate sample preparation and provide near-instantaneous results. Rapid evaporative ionisation mass spectrometry (REIMS) was used to assess its capabilities of detecting minced beef adulteration with beef brain, heart, kidney, large intestine and liver tissues and chemometric analysis enabled unique or significant markers to be identified. The adulteration levels detected with the REIMS technology when analysing raw adulterated beef burgers were; brain (5%); heart (1–10%); kidney (1–5%); large intestine (1–10%) and liver (5–10%). For boiled adulterated samples; brain (5–10%); heart (1–10%); kidney (1–5%); large intestine (1–10%) and liver (5–10%). REIMS allows rapid and specific identification of offal cuts within adulterated beef burgers and could provide a paradigm shift across many authenticity applications.

International audience; Rapid, sensitive, precise and accurate analysis of samples in their native in vivo environment is critical to better decipher physiological and physiopathological mechanisms. SpiderMass is an ambient mass spectrometry (MS) system designed for mobile in vivo and real-time surface analyses of biological tissues. The system uses a fibered laser, which is tuned to excite the most intense vibrational band of water, resulting in a process termed water-assisted laser desorption/ionization (WALDI). The water molecules act as an endogenous matrix in a matrix-assisted laser desorption ionization (MALDI)-like scenario, leading to the desorption/ionization of biomolecules (lipids, metabolites and proteins). The ejected material is transferred to the mass spectrometer through an atmospheric interface and a transfer line that is several meters long. Here, we formulate a three-stage procedure that includes (i) a laser system setup coupled to a Waters Q-TOF or Thermo Fisher Q Exactive mass analyzer, (ii) analysis of specimens and (iii) data processing. We also describe the optimal setup for the analysis of cell cultures, fresh-frozen tissue sections and in vivo experiments on skin. With proper optimization, the system can be used for a variety of different targets and applications. The entire procedure takes 1-2 d for complex samples.

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has been shown to quickly and accurately speciate microorganisms based upon their species-specific lipid profile. Previous work by members of this group showed that the use of a handheld bipolar probe allowed REIMS to analyse microbial cultures directly from culture plates, without any prior preparation. However, this method of analysis would likely be unsuitable for a high-throughput clinical microbiology laboratory. Here, we report on the creation of a customised platform which enables automated, high-throughput REIMS analysis, which requires minimal user input and operation; and suitable for use in clinical microbiology laboratories. The ability of this high-throughput platform to speciate clinically important microorganisms was tested through the analysis of 375 different clinical isolates, collected from distinct patient samples, from 25 microbial species. After optimisation of our data analysis approach, we achieved substantially similar results between the two REIMS approaches. For handheld bipolar probe REIMS a speciation accuracy of 96.3% was achieved, whilst for high-throughput REIMS, an accuracy of 93.9% was achieved. Thus, high-throughput REIMS offers an alternative mass spectrometry based method for the rapid and accurate identification of clinically important microorganisms in clinical laboratories without any pre-analysis preparative steps.

International audience; Histopathological diagnosis of biopsy samples and margin assessment of surgical specimens are challenging aspects in sarcoma. Using dog patient tissues, we assessed the performance of a recently developed technology for fast ex vivo molecular lipid-based diagnosis of sarcomas. The instrument is based on mass spectrometry (MS) molecular analysis through a laser microprobe operating under ambient conditions using excitation of endogenous water molecules. Classification models based on cancer/normal/necrotic, tumor grade, and subtypes showed a minimum of 97.63% correct classification. Specific markers of normal, cancer, and necrotic regions were identified by tandem MS and validated by MS imaging. Real-time detection capabilities were demonstrated by ex vivo analysis with direct interrogation of classification models.

Background Survival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection. Methods Aerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed. Results A total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P

Introduction Fish fraud detection is mainly carried out using a genomic profiling approach requiring long and complex sample preparations and assay running times. Rapid evaporative ionisation mass spectrometry (REIMS) can circumvent these issues without sacrificing a loss in the quality of results. Objectives To demonstrate that REIMS can be used as a fast profiling technique capable of achieving accurate species identification without the need for any sample preparation. Additionally, we wanted to demonstrate that other aspects of fish fraud other than speciation are detectable using REIMS. Methods 478 samples of five different white fish species were subjected to REIMS analysis using an electrosurgical knife. Each sample was cut 8–12 times with each one lasting 3–5 s and chemometric models were generated based on the mass range m/z 600–950 of each sample. Results The identification of 99 validation samples provided a 98.99% correct classification in which species identification was obtained near-instantaneously (≈ 2 s) unlike any other form of food fraud analysis. Significant time comparisons between REIMS and polymerase chain reaction (PCR) were observed when analysing 6 mislabelled samples demonstrating how REIMS can be used as a complimentary technique to detect fish fraud. Additionally, we have demonstrated that the catch method of fish products is capable of detection using REIMS, a concept never previously reported. Conclusions REIMS has been proven to be an innovative technique to help aid the detection of fish fraud and has the potential to be utilised by fisheries to conduct their own quality control (QC) checks for fast accurate results. Electronic supplementary material The online version of this article (10.1007/s11306-017-1291-y) contains supplementary material, which is available to authorized users.

The recently developed automated, high-throughput monopolar REIMS platform is suited for the identification of clinically important microorganisms. Although already comparable to the previously reported bipolar forceps method, optimization of the geometry of monopolar electrodes, at the heart of the system, holds the most scope for further improvements to be made. For this, sharp tip and round shaped electrodes were optimized to maximize species-level classification accuracy. Following optimization of the distance between the sample contact point and tube inlet with the sharp tip electrodes, the overall cross-validation accuracy improved from 77% to 93% in negative and from 33% to 63% in positive ion detection modes, compared with the original 4 mm distance electrode. As an alternative geometry, round tube shaped electrodes were developed. Geometry optimization of these included hole size, number, and position, which were also required to prevent plate pick-up due to vacuum formation. Additional features, namely a metal “X”-shaped insert and a pin in the middle were included to increase the contact surface with a microbial biomass to maximize aerosol production. Following optimization, cross-validation scores showed improvement in classification accuracy from 77% to 93% in negative and from 33% to 91% in positive ion detection modes. Supervised models were also built, and after the leave 20% out cross-validation, the overall classification accuracy was 98.5% in negative and 99% in positive ion detection modes. This suggests that the new generation of monopolar REIMS electrodes could provide substantially improved species level identification accuracies in both polarity detection modes. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s13361-017-1818-5) contains supplementary material, which is available to authorized users.

Background Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. Methods Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. Results A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). Conclusions The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment. Electronic supplementary material The online version of this article (doi:10.1186/s13058-017-0845-2) contains supplementary material, which is available to authorized users.

Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities.

Increasingly abundant food fraud cases have brought food authenticity and safety into major focus. This study presents a fast and effective way to identify meat products using rapid evaporative ionization mass spectrometry (REIMS). The experimental setup was demonstrated to be able to record a mass spectrometric profile of meat specimens in a time frame of

Negative ion desorption electrospray ionization (DESI) was used for the analysis of an ex vivo tissue sample set comprising primary colorectal adenocarcinoma samples and colorectal adenocarcinoma liver metastasis samples. Frozen sections (12 μm thick) were analyzed by means of DESI imaging mass spectrometry (IMS) with spatial resolution of 100 μm using a computer-controlled DESI imaging stage mounted on a high resolution Orbitrap mass spectrometer. DESI-IMS data were found to predominantly feature complex lipids, including phosphatidyl-inositols, phophatidyl-ethanolamines, phosphatidyl-serines, phosphatidyl-ethanolamine plasmalogens, phosphatidic acids, phosphatidyl-glycerols, ceramides, sphingolipids, and sulfatides among others. Molecular constituents were identified based on their exact mass and MS/MS fragmentation spectra. An identified set of molecules was found to be in good agreement with previously reported DESI imaging data. Different histological tissue types were found to yield characteristic mass spectrometric data in each individual section. Histological features were identified by comparison to hematoxylin-eosin stained neighboring sections. Ions specific to certain histological tissue types (connective tissue, smooth muscle, healthy mucosa, healthy liver parenchyma, and adenocarcinoma) were identified by semi-automated screening of data. While each section featured a number of tissue-specific species, no potential global biomarker was found in the full sample set for any of the tissue types. As an alternative approach, data were analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) which resulted in efficient separation of data points based on their histological types. A pixel-by-pixel tissue identification method was developed, featuring the PCA/LDA analysis of authentic data set, and localization of unknowns in the resulting 60D, histologically assigned LDA space. Novel approach was found to yield results which are in 95% agreement with the results of classical histology. KRAS mutation status was determined for each sample by standard molecular biology methods and a similar PCA/LDA approach was developed to assess the feasibility of the determination of this important parameter using solely DESI imaging data. Results showed that the mutant and wild-type samples fully separated. DESI-MS and molecular biology results were in agreement in 90% of the cases.

Introduction: Positive tumour margins following attempted breast conserving surgery (BCS) is an important risk factor for local recurrence. Nationally in the United Kingdom on average approximately 25% of patients undergoing BCS require additional surgery for positive margins. Traditional techniques such as specimen xray, frozen section & imprint cytology to optimise margin clearance have significant limitations. Various research methods under investigation include optical spectroscopy, high resolution imaging and radiofrequency spectroscopy. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a new method that uses mass spectrometric analysis of the tissue specific ionic content of the surgical diathermy smoke plume for the rapid identification of dissected breast tissues as an intelligent knife (iKnife). We investigate the ability of the "iKnife" to analyze heterogeneous breast tissue intraoperatively using mass spectrometric techniques. Method: The study involved three stages that comprised: method development, tissue specific ex-vivo database construction and intraoperative analysis. Smoke aerosol produced as a result of electrosurgical diathermy from a variety of frozen, fresh and in-vivo breast samples were aspirated into a mass spectrometer via a modified surgical handpiece. Tissue diagnosis was confirmed by subsequent histopathological validation. The data underwent computational analysis using multivariate statistics –predominantly Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), along with leave one patient out cross-validation. A total of 128 patients (n=40 method development, n=66 ex-vivo database, n=22 intraoperative analysis) undergoing breast surgery were enrolled in this study. Ethical approval was obtained from the Research Ethics Committee. Results: 40 patients contributed breast samples (normal and cancerous) for method optimisation to enable analysis of high intensity spectra from heterogeneous breast tissue. Following optimisation an ex-vivo database was constructed from 89 excised fresh breast tissue samples from 66 patients using 330 spectra (246 Normal, 60 Tumour – IDC, ILC, IMC and 24 Benign - fibroadenoma). Multivariate statistical analysis of data revealed classification of tumour compared to normal tissue with sensitivities of 93.0% and specificity of 91.9%. The iKnife was used intraoperatively during the entire operation of 25 surgeries. Spectral data was obtained within 1-2 seconds. Specific margin analysis correctly identified negative margins in 10 cases. Conclusions: The iKnife has been successfully developed for analysis of intraoperative heterogeneous breast tissue. Preliminary data suggests that this technique is suitable with high accuracy for the separation of normal, benign (fibroadenoma) and cancerous (invasive ductal and invasive lobular carcinoma) breast tissues. In comparison to the normal breast, cancerous tissues exhibit statistically different spectral profiles. Further work is aimed at the development of a real time algorithm able to match intraoperative data with the pre-existing database for the rapid interpretation and real time feedback of intraoperative data towards detecting positive margins intraoperatively. Citation Format: St John ER, Al-Khudairi R, Balog J, Rossi M, Gildea L, Speller A, Ramakrishnan R, Shousha S, Takats Z, Leff DR, Darzi A. Rapid evaporative ionisation mass spectrometry towards real time intraoperative oncological margin status determination in breast conserving surgery. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-12-20.

Laser desorption ionization-mass spectrometric (LDI-MS) analysis of vital biological tissues and native, ex vivo tissue specimens is described. It was found that LDI-MS analysis yields tissue specific data using lasers both in the ultraviolet and far-infrared wavelength regimes, while visible and near IR lasers did not produce informative MS data. LDI mass spectra feature predominantly phospholipid-type molecular ions both in positive and negative ion modes, similar to other desorption ionization methods. Spectra were practically identical to rapid evaporative ionization MS (REIMS) spectra of corresponding tissues, indicating a similar ion formation mechanism. LDI-MS analysis of intact tissues was characterized in detail. The effect of laser fluence on the spectral characteristics (intensity and pattern) was investigated in the case of both continuous wave and pulsed lasers at various wavelengths. Since lasers are utilized in various fields of surgery, a surgical laser system was combined with a mass spectrometer in order to develop an intraoperative tissue identification device. A surgical CO(2) laser was found to yield sufficiently high ion current during normal use. The principal component analysis-based real-time data analysis method was developed for the quasi real-time identification of mass spectra. Performance of the system was demonstrated in the case of various malignant tumors of the gastrointestinal tract.

Gastrointestinal cancers are a leading cause of mortality, accounting for 23 % of cancer-related deaths worldwide. In order to improve outcomes from these cancers, novel tissue characterization methods are needed to facilitate accurate diagnosis. Rapid evaporative ionization mass spectrometry (REIMS) is a technique developed for the in vivo classification of human tissue through mass spectrometric analysis of aerosols released during electrosurgical dissection. This ionization technique was further developed by utilizing surface induced dissociation and was integrated with an endoscopic polypectomy snare to allow in vivo analysis of the gastrointestinal tract. We tested the classification performance of this novel endoscopic REIMS method in vivo. It was shown to be capable of differentiating between healthy layers of the intestinal wall, cancer, and adenomatous polyps based on the REIMS fingerprint of each tissue type in vivo.

Rapid evaporative ionization mass spectrometry (REIMS) was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode, and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.9%, 97.8%, and 100% on species, genus, and Gram-stain level, respectively. These results were not affected by the resolution of the mass spectral data. Blind identification tests were performed for strains cultured on different culture media and analyzed using different instrumental platforms which led to 97.8–100% correct identification. Seven different Escherichia coli strains were subjected to different culture conditions and were distinguishable with 88% accuracy. In addition, the technique proved suitable to distinguish five pathogenic Candida species with 98.8% accuracy without any further modification to the experimental workflow. These results prove that REIMS is sufficiently specific to serve as a culture condition-independent tool for the identification and characterization of microorganisms.

Here we present a proof of concept cross-platform normalization approach to convert raw mass spectra acquired by distinct desorption ionization methods and/or instrumental setups to cross-platform normalized analyte profiles. The initial step of the workflow is database driven peak annotation followed by summarization of peak intensities of different ions from the same molecule. The resulting compound-intensity spectra are adjusted to a method-independent intensity scale by using predetermined, compound-specific normalization factors. The method is based on the assumption that distinct MS-based platforms capture a similar set of chemical species in a biological sample, though these species may exhibit platform-specific molecular ion intensity distribution patterns. The method was validated on two sample sets of (1) porcine tissue analyzed by laser desorption ionization (LDI), desorption electrospray ionization (DESI), and rapid evaporative ionization mass spectrometric (REIMS) in combination with Fourier transformation-based mass spectrometry; and (2) healthy/cancerous colorectal tissue analyzed by DESI and REIMS with the latter being combined with time-of-flight mass spectrometry. We demonstrate the capacity of our method to reduce MS-platform specific variation resulting in (1) high inter-platform concordance coefficients of analyte intensities; (2) clear principal component based clustering of analyte profiles according to histological tissue types, irrespective of the used desorption ionization technique or mass spectrometer; and (3) accurate “blind” classification of histologic tissue types using cross-platform normalized analyte profiles.

Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near-real-time characterization of human tissue in vivo by analysis of the aerosol ("smoke") released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

Background Real time electrospray ionization mass spectrometry (REIMS) enables detailed analysis of tumour lipid chemistry, based on real time analysis of electrocautery smoke plumes. Methods: This was a prospective, observational study performed at St. Mary's Hospital, London, UK. Patients undergoing elective surgical resections for colorectal cancer were recruited and fresh samples were analyzed ex-vivo using a typical electrosurgery hand piece and monopolar diathermy. Sampling was performed using cutting mode with a standard generator and 30W of output power (ValleylabTM). The hand piece was modified to allow aspiration of the electrosurgical aerosol to a Xevo G2-S iKnife QTof mass spectrometer (Waters Corporation). Raw mass spectrometric data were converted to imzML format (MSConvert) and imported into MATLAB (R2014a) for pre-processing. A prospective database of healthy, dysplastic and malignant colorectal tissues was built and multivariate analysis was performed using principal component analysis and linear discriminant analysis. Classification of each individual tissue type was performed using leave-one-patient-out cross-validation. Results 40 consecutive patients were recruited (22 male, median age 68y, range 47-90). Of the 23 tumor samples 10 were rectal adenocarcinoma and 13 colonic adenocarcinoma. TNM staging of the tumour samples was as follows: T2 (8), T3 (11) T4 (4), N0 (12), N1 (6) N2 (5), M0 (22), M1 (1). Distinction of healthy and malignant colorectal tissue for the whole data set demonstrated an overall classification accuracy of 94.4% and a sensitivity of 92.4%, Specificity 96.8% (ROC AUC 0.98). The diagnostic accuracy for dysplasia was 93.7% (Specificity 95.1%, sensitivity 85.7%, AUC 0.97). Increases in glycerophospholipids (p Conclusion REIMS chemical histology provides near real time diagnostic and prognostic information for stratifying oncological and surgical therapy. Citation Format: James Macalister Kinross, Laura Muirhead, James Alexander, Julia Balog, Cristina Guallar-Hoya, Abigail Speller, Ottmar Golff, Rob Goldin, Ara Darzi, Jeremy Nicholson, Zoltan Takats. iKnife: Rapid evaporative ionization mass spectrometry (REIMS) enables real-time chemical analysis of the mucosal lipidome for diagnostic and prognostic use in colorectal cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3977.

Background: Minimal technologies exist that provide tissue specific molecular information to the surgeon in real-unsupervised principal components time. Rapid evaporative ionization mass spectrometry allows near real-time characterization of tissue by mass spectrometric analysis of the smoke plume released during electrosurgical dissection. Objectives: To develop and optimize a statistical strategy for the real-time recognition of cancer margin status during surgical excision of liver metastases. Methods: Fresh tissue samples from 25 patients with liver metastasis from colorectal adenocarcinoma were collected and analyzed using monopolar diathermy coupled to ion trap mass spectrometry. At the point of rapid evaporation of tissues during electrosurgical dissection, tissue specific charged particles are formed and these ionized molecular species are transferred within the diathermy plume to the mass spectrometer using Venturi air jet pump and PTFE tubing. Intense spectral profiles are produced (m/z range: 600-900) which are associated with the structural phospholipid content of tissues and vary significantly between the distinct histological tissue types. Results: The resulting dataset was analyzed by unsupervised principal components analysis for explorative analysis of similarities/differences in molecular ion composition between samples. The maximum margin criterion analysis, a supervised dimension reduction technique, was subsequently applied to extract tissue specific discriminating molecular ion patterns. The 3-nearest neighbour classification algorithm was applied on a reduced set of discriminant features and 10-fold cross validation carried out. Discrimination of healthy and malignant tissue was possible with a sensitivity of 96.8% and cross validation demonstrated the validity of the supervised methods. Conclusion: When paired with real-time data analysis, the iKnife is a viable potential method of real-time tissue identification including the intra-operative assessment of oncological resection margins.

Direct combination of cavitron ultrasonic surgical aspirator (CUSA) and sonic spray ionization mass spectrometry is presented. A commercially available ultrasonic surgical device was coupled to a Venturi easy ambient sonic-spray ionization (V-EASI) source by directly introducing liquified tissue debris into the Venturi air jet pump. The Venturi air jet pump was found to efficiently nebulize the suspended tissue material for gas phase ion production. The ionization mechanism involving solely pneumatic spraying was associated with that of sonic spray ionization. Positive and negative ionization spectra were obtained from brain and liver samples reflecting the primary application areas of the surgical device. Mass spectra were found to feature predominantly complex lipid-type constituents of tissues in both ion polarity modes. Multiply charged peptide anions were also detected. The influence of instrumental settings was characterized in detail. Venturi pump geometry and flow parameters were found to be critically important in ionization efficiency. Standard solutions of phospholipids and peptides were analyzed in order to test the dynamic range, sensitivity, and suppression effects. The spectra of the intact tissue specimens were found to be highly specific to the histological tissue type. The principal component analysis (PCA) and linear discriminant analysis (LDA) based data analysis method was developed for real-time tissue identification in a surgical environment. The method has been successfully tested on post-mortem and ex vivo human samples including astrocytomas, meningeomas, metastatic brain tumors, and healthy brain tissue.

The feasibility of electrospray (ES) ionization of aerosols generated by electrosurgical disintegration methods was investigated. Although electrosurgery itself was demonstrated to produce gaseous ions, post-ionization methods were implemented to enhance the ion yield, especially in those cases when the ion current produced by the applied electrosurgical method is not sufficient for MS analysis. Post-ionization was implemented by mounting an ES emitter onto a Venturi pump, which is used for ion transfer. The effect of various parameters including geometry, high voltage setting, flow parameters, and solvent composition was investigated in detail. Experimental setups were optimized accordingly. ES post-ionization was found to yield spectra similar to those obtained by the REIMS technique, featuring predominantly lipid-type species. Signal enhancement was 20- to 50-fold compared with electrosurgical disintegration in positive mode, while no improvement was observed in negative mode. ES post-ionization was also demonstrated to allow the detection of non-lipid type species in the electrosurgical aerosol, including drug molecules. Since the tissue specificity of the MS data was preserved in the ES post-ionization setup, feasibility of tissue identification was demonstrated using different electrosurgical methods.

The newly developed rapid evaporative ionization mass spectrometry (REIMS) provides the possibility of in vivo, in situ mass spectrometric tissue analysis. The experimental setup for REIMS is characterized in detail for the first time, and the description and testing of an equipment capable of in vivo analysis is presented. The spectra obtained by various standard surgical equipments were compared and found highly specific to the histological type of the tissues. The tissue analysis is based on their different phospholipid distribution; the identification algorithm uses a combination of principal component analysis (PCA) and linear discriminant analysis (LDA). The characterized method was proven to be sensitive for any perturbation such as age or diet in rats, but it was still perfectly suitable for tissue identification. Tissue identification accuracy higher than 97% was achieved with the PCA/LDA algorithm using a spectral database collected from various tissue species. In vivo, ex vivo, and post mortem REIMS studies were performed, and the method was found to be applicable for histological tissue analysis during surgical interventions, endoscopy, or after surgery in pathology.

Immediate, real-time accurate tissue characterisation is a crucial unmet need during surgery for the maximal resection of CNS tumours whilst causing minimum damage to surrounding functioning brain tissue. We describe the first ever use of mass spectrometry and Raman spectroscopy to immediately characterize brain tumours during surgery with accuracy rivaling that of traditional histopathological techniques. A prospective observational study was designed consisting of 40 patients undergoing Neurosurgical resection of a range of WHO grade I-IV brain tumours and metastases. A Neuronavigational platform was devised combining advanced spectrometry technologies together with intraoperative imaging to allow accurate 3D spectral acquisition during surgery taking into account brain shift. The resulting data from 40 cases consists of over 1,500 brain spectra acquired during surgery with high spatial localization. Analysis shows unique spectra for each type of tumour with sensitivity and specificity comparable to the current gold standard of histopathological analysis. The spatial heterogeneity of tumour spectra sheds important light into underlying genomic and metabolomic profiles of the tumours studied including IDH-1 mutation and MGMT status. We have demonstrated that the use of molecular data with high spatial resolution obtained through mass spectral and Raman spectral analysis shows that real-time biopsies are obtainable and represent an invaluable resource in the decision making process during surgery. This also represents an opportunity to fast track vital post surgical therapies currently offered to high grade brain tumours and investigate novel aspects of in-vivo tumour biology through a new modality with the possibility of novel biomarker discovery. This sets the stage for a formal clinical trial looking into the benefits of spectral guided resection in terms of safer and more complete resection, rapid progress to further treatment and improved overall survival.

The purpose of this study was to prove that rapid intraoperative tissue identification of brain tumours using Rapid Evaporative Ionization Mass Spectrometry (REIMS, used surgically as the iKnife) with high spatial resolution is possible in order to help establish a tissue diagnosis and to maximise the extent of tumour resection. A single centre prospective observational study was designed involving a consecutive series of patients undergoing craniotomy and resection of WHO grade II, III and IV tumours. A neuronavigation system was used to register iKnife readings for the first time during surgery for brain tumour resection. Precise intraoperative readings from different tumour zones were taken and compared to matched core biopsy samples verified by routine histopathology. This protocol worked well, allowing in-vivo mass spectrometry readings to be taken with accurate histology. Analysis of data from the first 6 cases shows distinct phospholipid spectral patterns for Grade II and IV tumours. An increased intensity of sulfatides were observed in the white matter of the grade II tumours. Our neuronavigational platform for the iKnife has revealed unique in-vivo spectra for different intrinsic brain tumours. As more cases are added to our spectral database we hope to describe in-vivo intra-tumoural variations which may shed important light into intrinsic brain tumour biology. Citation Format: Babar Vaqas, Julia Balog, Federico Roncaroli, Zoltan Takats, Kevin O'Neill. iKnife in neurosurgery: intraoperative real-time, in vivo biochemical characterization of brain tumors with high spatial resolution. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-287. doi:10.1158/1538-7445.AM2015-LB-287

localization of the colonoscope tip may not be reliable. We aimed to assess accuracy of colonoscopist localization of polyps at the time of colonoscopy and determine if patient, procedure or colonoscopist variables influence the accuracy of localization. Methods: We conducted a prospective observational study in consecutive patients presenting for elective, outpatient colonoscopy at an academic center. All procedures were performed by board-certified colonoscopists. Polyps found during colonoscope withdrawal were included. The endoscopic location of each polyp was reported by the colonoscopist at the time of resection and prospectively recorded. Magnetic endoscope imaging (ScopeGuide, Olympus, Japan) was used to generate a reference standard for polyp location. For each procedure, a ScopeGuide image of the instrument location on insertion, caecal intubation and during polypectomy was prospectively obtained. Colonoscopists were blinded to the ScopeGuide image. Three separate expert colonoscopists, blinded to the endoscopist’s assessment of polyp location, independently reviewed the ScopeGuide images to obtain a reference standard for polyp location (Cronbach alpha 0.98). We assessed the accuracy of colonoscopist polyp localisation using the ScopeGuide-derived reference standard. We assessed extent of deviation between colonoscopist location and ScopeGuide location, by dividing the colon in 18 segments from caecum to distal rectum. Results: A total of 282 polyps were resected in 155 patients by 16 colonoscopists. Overall accuracy of polyp localization was 85%. Accuracy varied significantly (p!0.001) by colonic segment: caecum 100%, ascending 77%, transverse 82%, descending 56%, sigmoid 90%, rectum 95%. There was no significant deviation between ScopeGuide location and colonoscopist location for polyps located in the distal sigmoid and rectum (mean deviation 0.3 segments, range 0-3 segments, pZ0.32). There was significant deviation between ScopeGuide location and colonoscopist location from caecum to mid-sigmoid colon (mean deviation 0.8, range 0-6 segments, p!0.001). Extent of deviation between actual and predicted location was not influenced by patient sex, age, body mass index, surgical history, bowel preparation or years of colonoscopy experience. Conclusion: Accuracy of localization of polyps is imprecise, and affected by position within the colon. Methods to improve lesion localization at colonoscopy should be studied.

Background This pilot study assessed the diagnostic accuracy of rapid evaporative ionization mass spectrometry (REIMS) in colorectal cancer (CRC) and colonic adenomas. Methods Patients undergoing elective surgical resection for CRC were recruited at St. Mary’s Hospital London and The Royal Marsden Hospital, UK. Ex vivo analysis was performed using a standard electrosurgery handpiece with aspiration of the electrosurgical aerosol to a Xevo G2-S iKnife QTof mass spectrometer (Waters Corporation). Histological examination was performed for validation purposes. Multivariate analysis was performed using principal component analysis and linear discriminant analysis in Matlab 2015a (Mathworks, Natick, MA). A modified REIMS endoscopic snare was developed (Medwork) and used prospectively in five patients to assess its feasibility during hot snare polypectomy. Results Twenty-eight patients were recruited (12 males, median age 71, range 35–89). REIMS was able to reliably distinguish between cancer and normal adjacent mucosa (NAM) (AUC 0.96) and between NAM and adenoma (AUC 0.99). It had an overall accuracy of 94.4 % for the detection of cancer versus adenoma and an adenoma sensitivity of 78.6 % and specificity of 97.3 % (AUC 0.99) versus cancer. Long-chain phosphatidylserines (e.g., PS 22:0) and bacterial phosphatidylglycerols were over-expressed on cancer samples, while NAM was defined by raised plasmalogens and triacylglycerols expression and adenomas demonstrated an over-expression of ceramides. REIMS was able to classify samples according to tumor differentiation, tumor budding, lymphovascular invasion, extramural vascular invasion and lymph node micrometastases (AUC’s 0.88, 0.87, 0.83, 0.81 and 0.81, respectively). During endoscopic deployment, colonoscopic REIMS was able to detect target lipid species such as ceramides during hot snare polypectomy. Conclusion REIMS demonstrates high diagnostic accuracy for tumor type and for established histological features of poor prognostic outcome in CRC based on a multivariate analysis of the mucosal lipidome. REIMS could augment endoscopic and imaging technologies for precision phenotyping of colorectal cancer. Electronic supplementary material The online version of this article (doi:10.1007/s00464-016-5121-5) contains supplementary material, which is available to authorized users.