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In this study, we examine the suitability of desorption electro-flow focusing ionization (DEFFI) for mass spectrometry imaging (MSI) of biological tissue. We also compare the performance of desorption electrospray ionization (DESI) with and without the flow focusing setup. The main potential advantages of applying the flow focusing mechanism in DESI is its rotationally symmetric electrospray jet, higher intensity, more controllable parameters, and better portability due to the robustness of the sprayer. The parameters for DEFFI have therefore been thoroughly optimized, primarily for spatial resolution but also for intensity. Once the parameters have been optimized, DEFFI produces similar images to the existing DESI. MS images for mouse brain samples, acquired at a nominal pixel size of 50 µm, are comparable for both DESI setups, albeit the new sprayer design yields better sensitivity. Furthermore, the two methods are compared with regard to spectral intensity as well as the area of the desorbed crater on rhodamine-coated slides. Overall, the implementation of a flow focusing mechanism in DESI is shown to be highly suitable for imaging biological tissue and has potential to overcome some of the shortcomings experienced with the current geometrical design of DESI.

The Supplementary information file contains: (i) Supplementary Methods, concerning PCR primers, antibodies and explanation of the chemometric approach. (ii) Supplementary Figures 1-4, which provide extended data for the multivariable models of Figure 2 and 3, MS/MS analyses, and further gene expression data supporting Figure 5. (iii) Supplementary Tables 1-4, which provide demographics of the cohorts and univariate lipid analyses from Cohort 1 and 2.

Table S1: Glycerophospholipid relative abundances compared between lymph node metastases (LNM) and primary tumor by means of ANOVA. Table S2: Glycerophospholipid relative abundances compared between malignant tissue (lymph node metastases and primary tumor) and non-malignant lymph node tissue types by means of ANOVA. Table S3: Contingency table comparing immunohistochemistry reference test and mass spectrometry image tissue classification prediction images. Table S4: Contingency Table comparing immunohistochemistry reference test and mass spectrometry image tissue classification prediction images (TCPI) - with re-classification of positive capsular identification on TCPI as normal lymph nodes. Figure S1: DESI-MS of normal lymph node with carbon deposits from smoking. Figure S2: DESI-MS of lymph node with macro-metastases. Figure S3: Internal cross validation - lipidomic profile of lymph node metastases versus lymph node metastases response to chemotherapy. Figure S4: Mass spectrometry image tissue classification prediction images compared to matched AE1/AE3 immunohistochemistry images of 90 lymph node (LN) data set. Figure S5: DESI-MS of mouse brain cryo-section with a spatial resolution of 20μm per pixel. Figure S6: DESI-MS of mouse brain cryo-section with a spatial resolution of 75μm and 30 scans per second. Supplementary Figure S7: Image alignment of histology image and Mass Spectrometry Image (MSI) for tissue specific pixel labeling. Supplementary Figure S8: Overview for the creation of a composite database of tissue specific mass spectra. Tissue specific mass spectra extracted from representative pixels of Mass Spectrometry Images from individual samples are used to populate combined data units of that tissue type with further sub-classification of its sample origin.

Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were coregistered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within LNs. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched IHC images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign LN tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 89.5%, 100%, 100%, and 97.2%, respectively, when compared with gold-standard IHC. DESI-MSI has the potential to be a diagnostic tool for perioperative identification of LNM and compares favorably with techniques currently used by histopathology experts. Cancer Res; 76(19); 5647–56. ©2016 AACR.

Full methodological details of data analysis with respect to: tissue specific mass spectra extraction; data pre-processing (mass range selection, peak alignment, normalization, de-noising, data averaging); multivariate statistical models; glycerophospholipid annotation and individual glycerophospholipid comparison between tissue types.

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming.Significance:These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis.

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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.

Outcomes for oesophageal adenocarcinoma continue to be poor, as patients are often diagnosed too late for curative treatment. New tools to assist early and rapid diagnosis are needed. Tissue phospholipids quantified by mass spectrometry imaging (MSI) can objectively diagnose non-oesophageal cancers from control tissue in minutes. However, whether MSI can objectively identify oesophageal adenocarcinoma is unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Methods Desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) and bespoke chemometrics were used to assess the phospholipidomes of oesophageal adenocarcinoma and relevant control tissues, in surgical (discovery) and endoscopic (validation) cohorts. Structural annotations verified by tandem MS fragmentation patterns were performed to understand the characteristics of the key differential phospholipids. Bioinformatics, quantitative polymerase chain reaction, and immunohistochemistry were performed to describe the reprogramming of phospholipogenesis in oesophageal adenocarcinoma. Finally, genetic silencing experiments were performed to understand whether this genetic reprogramming impacts the oesophageal adenocarcinoma phospholipid signature. Results Multivariable models based on tissue phospholipids from 117 patients were highly discriminant for oesophageal adenocarcinoma both in discovery (area-under-curve = 0.97) and validation cohorts (AUC = 1). Cancer samples were enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in pre-malignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched phospholipid acyls. Mechanistically, reducing ACLY expression in oesophageal adenocarcinoma cells shortened GPL chains, therefore linking de novo lipogenesis to the oesophageal adenocarcinoma phospholipidome. Conclusion DESI-MSI (i) objectively identified invasive oesophageal adenocarcinoma from a number of pre-malignant tissues (ii) described the characteristics of cancer phospholipids, which have unknown function (iii) unveiled a new mechanism of phospholipidomic reprogramming. These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols are generated, and how they contribute to oesophageal carcinogenesis.

Objective Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a metabolomic technique analysing tissue metabolites, which can be applied intra-operatively 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. Summary background data 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. Methods 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 600-1000m/z were used for univariate and multivariate functions including linear discriminant analysis. Results 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. Conclusions The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to risk-stratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.

International audience; The ability to adapt to low-nutrient microenvironments is essential for tumor cell survival and progression in solid cancers, such as colorectal carcinoma (CRC). Signaling by the NF-kappaB transcription factor pathway associates with advanced disease stages and shorter survival in patients with CRC. NF-kappaB has been shown to drive tumor-promoting inflammation, cancer cell survival, and intestinal epithelial cell (IEC) dedifferentiation in mouse models of CRC. However, whether NF-kappaB affects the metabolic adaptations that fuel aggressive disease in patients with CRC is unknown. Here, we identified carboxylesterase 1 (CES1) as an essential NF-kappaB-regulated lipase linking obesity-associated inflammation with fat metabolism and adaptation to energy stress in aggressive CRC. CES1 promoted CRC cell survival via cell-autonomous mechanisms that fuel fatty acid oxidation (FAO) and prevent the toxic build-up of triacylglycerols. We found that elevated CES1 expression correlated with worse outcomes in overweight patients with CRC. Accordingly, NF-kappaB drove CES1 expression in CRC consensus molecular subtype 4 (CMS4), which is associated with obesity, stemness, and inflammation. CES1 was also upregulated by gene amplifications of its transcriptional regulator HNF4A in CMS2 tumors, reinforcing its clinical relevance as a driver of CRC. This subtype-based distribution and unfavorable prognostic correlation distinguished CES1 from other intracellular triacylglycerol lipases and suggest CES1 could provide a route to treat aggressive CRC.

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption – Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April – June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming. Significance: These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis.

A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 μm, which corresponds to a lateral resolution of 40–60 μm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. Graphical Abstractᅟ Electronic supplementary material The online version of this article (doi:10.1007/s13361-017-1714-z) contains supplementary material, which is available to authorized users.

Introduction/Background Surgical management of presumed early stage cervical cancer presents challenges as clearance of surgical margins avoids adjuvant chemoradiotherapy that can substantially increase morbidity and may allow fertility preservation in young women. Improved intra-operative diagnostic tools have the potential to improve outcomes. In this study, we explore whether the Rapid Evaporative Ionization Mass Spectrometry (REIMS), also known as the iKnife (intelligent Knife), can accurately discriminate between healthy, pre-invasive and invasive cervical tissue ex vivo. Methodology We prospectively collected fresh frozen cervical tissue samples in normal controls and women with Human Papilloma Virus (HPV) ± cervical intraepithelial neoplasia (CIN) and cervical cancer (Cancer) between 2006 and 2018. A handheld diathermy device was used to burn the biopsies; the surgical aerosol produced was subsequently transferred into a mass-spectrometer. The tissue was then stained for histopathological validation. We used principal component and linear discriminant analysis to calculate the accuracy of the iKnife in discriminating between the 3 groups. Cancerous and normal tissues were then processed with REIMS tandem MS (REI-MS/MS) to further delineate the most significant peaks allowing separation of the two classes and identify their lipid profile. Results The iKnife had an overall 97.8% accuracy in discriminating accurately normal (100%) vs HPV±CIN (100%) versus cancer (83.3%) when compared to histology as the gold standard. REI-MS/MS and LipidMAPS revealed twelve significant MS peaks in the cancer to normal separation. Conclusion This study shows that REIMS iKnife has the potential to accurately discriminate healthy, from precancerous and invasive lesions on the cervix. The overexperessed lipids in cancer may be used as markers of disease. If these finding are replicated in larger cohorts, the iKnife may allow real-time intra-operative diagnosis of the presence of disease in surgical margins and lymph nodes. Disclosure Nothing to disclose

Metabolites, lipids, and other small molecules are key constituents of tissues supporting cellular programs in health and disease. Here, we present METASPACE, a community-populated knowledge base of spatial metabolomes from imaging mass spectrometry data. METASPACE is enabled by a high-performance engine for metabolite annotation in a confidence-controlled way that makes results comparable between experiments and laboratories. By sharing their results publicly, engine users continuously populate a knowledge base of annotated spatial metabolomes in tissues currently including over 3000 datasets from human cancer cohorts, whole-body sections of animal models, and various organs. The spatial metabolomes can be visualized, explored and shared using a web app as well as accessed programmatically for large-scale analysis. By using novel computational methods inspired by natural language processing, we illustrate that METASPACE provides molecular coverage beyond the capacity of any individual laboratory and opens avenues towards comprehensive metabolite atlases on the levels of tissues and organs.

Background: Tumor-derived Extracellular Vesicles (EVs) are found in body fluids of cancer patients. These EVs include exosomes and microvesicles, which play an important role in tumor development and metastasis. Although EVs have great potential as liquid biopsies for early detection of breast cancer, the lipid composition of breast cancer-derived EVs is unknown. Here, we studied the lipid composition of EVs and its potential for the identification of body fluid-based biomarkers for breast cancer diagnosis. Methods: The lipid composition of EVs and their parental cells were evaluated for nine breast cancer and two non-cancerous mammary cell lines. EVs were isolated by size-exclusion chromatography (SEC). Furthermore, ten blood plasma samples were studied from women diagnosed with primary breast cancer before surgery, nine samples from patients with progressive metastatic breast cancer and ten samples from healthy women who neither had pre-existing medical conditions nor infections at the time the samples were collected or in the previous weeks. EVs were obtained from blood plasma samples by a combination of density gradient ultracentrifugation and SEC. Lipids were extracted from EVs and analyzed by reverse-phase liquid chromatography mass-spectrometry (LC-MS). Results: We found that EVs produced by breast cancer cell lines were enriched in sphingomyelins and ceramides when compared to their parental cells. Furthermore, phospholipids such as phosphatidylcholine and phosphatidylethanolamine were found to be abundant in these EVs. Multivariate analyses showed that EVs released by breast cancer cells can be distinguished from those released by non-cancerous cells based on their phospholipid and sphingolipid composition (AUCROC=0.99, p Conclusion: Our findings demonstrate the potential of the lipid content of cancer-derived EVs for the identification of lipid biomarkers for breast cancer diagnosis and its use for the development of non-invasive (or minimally invasive) diagnostic methods based on blood plasma analysis. Citation Format: Erika Dorado, M Luisa Doria, Anika Nagelkerke, James S McKenzie, Stefania Maneta-Stavrakaki, Charlotte Ion, Thomas Whittaker, Jeremy Nicholson, Molly M Stevens, R Charles Coombes, Zoltan Takats. Lipidomic analysis of extracellular vesicles and its potential for the identification of body fluid-based biomarkers for breast cancer diagnosis [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 459.

Rapid evaporative ionization mass spectrometry (REIMS) was used for the rapid mass spectrometric profiling of cancer cell lines. Spectral reproducibility was assessed for three different cell lines, and the extent of interclass differences and intraclass variance was found to allow the identification of these cell lines based on the REIMS data. Subsequently, the NCI60 cell line panel was subjected to REIMS analysis, and the resulting data set was investigated for its distinction of individual cell lines and different tissue types of origin. Information content of REIMS spectral profiles of cell lines were found to be similar to those obtained from mammalian tissues although pronounced differences in relative lipid intensity were observed. Ultimately, REIMS was shown to detect changes in lipid content of cell lines due to mycoplasma infection. The data show that REIMS is an attractive means to study cell lines involving minimal sample preparation and analysis times in the range of seconds.

In this study, the impact of sprayer design and geometry on performance in desorption electrospray ionization mass spectrometry (DESI-MS) is assessed, as the sprayer is thought to be a major source of variability. Absolute intensity repeatability, spectral composition, and classification accuracy for biological tissues are considered. Marked differences in tissue analysis performance are seen between the commercially available and a lab-built sprayer. These are thought to be associated with the geometry of the solvent capillary and the resulting shape of the primary electrospray. Experiments with a sprayer with a fixed solvent capillary position show that capillary orientation has a crucial impact on tissue complex lipid signal and can lead to an almost complete loss of signal. Absolute intensity repeatability is compared for five lab-built sprayers using pork liver sections. Repeatability ranges from 1 to 224% for individual sprayers and peaks of different spectral abundance. Between sprayers, repeatability is 16%, 9%, 23%, and 34% for high, medium, low, and very low abundance peaks, respectively. To assess the impact of sprayer variability on tissue classification using multivariate statistical tools, nine human colorectal adenocarcinoma sections are analyzed with three lab-built sprayers, and classification accuracy for adenocarcinoma versus the surrounding stroma is assessed. It ranges from 80.7 to 94.5% between the three sprayers and is 86.5% overall. The presented results confirm that the sprayer setup needs to be closely controlled to obtain reliable data, and a new sprayer setup with a fixed solvent capillary geometry should be developed.

Introduction A multi-modal analysis approach using desorption electrospray ionization (DESI-MSI) and RNA-Seq can envision a complete metabolic and genetic information from clinical specimens revealing tumour heterogeneity. Coupled with laser capture microdissection (LCM) provides a compelling opportunity for molecular sub-characterizing of the tumour tissues. The envisioned combination analysis raises special requirements, including short LCM time, to prevent RNA degradation during microdissection at room temperature. The isolated RNA must have sufficient quality and quantity to carry out RNA seq for transcriptomics. Objectives The aim of this study was developing a multi-modal analysis protocol obtaining metabolic clusters to get a more in-depth knowledge for tumour-heterogeneity from Patient-derived Xenografts (PDXs) and clinical specimens. Methods PDXs and primary tissue biopsies from patients with Breast cancer were cryosectioned at ten μm and mounted on PEN membrane glass slides, which are unique slides for LASER Capture Microdissection (LDM). The DESI imaging analysis area was obtained line-by-line using the DEFFI sprayer. The analyzed tissue sections were stained with H&E and annotated by a histopathologist to allow the alignment of optical and MSI images. Next, the areas of interest in the same slide were microdissected by Laser Capture Microdissection for LC-MS and RNA-seq (Leica LDM 7000). RNA was isolated with a commercial kit (Qiagen RNeasy Micro Kit). Finally, standardization of RNA quality control was done by the Agilent 2100 Bioanalyzer System and followed by RNA Sequencing. Results Preliminary results showed that the extracted samples from microdissected sections using Laser Capture Microdissected for LC-MS could be used to validate the metabolites and lipids, which already had been imaged by DESI-MSI. These DESI-MSI and LC-MS results, which obtained from specific areas on the tissue sections can be attributed to identifying metabolically different sub-clones in the adjacent tumour sections. The next identification method for sub-cloning is a transcriptomic approach. For the transcriptomic study, the results of Agilent showed that the RNA quality of samples was sufficiently competent to carry out downstream analysis, including RNA seq. RNA seq can identify specific gene expression of the pathways, which are related to the identified metabolic profiling by DESI-MSI and LC-MS. Conclusion: We found that developing a multi-modal analysis protocol coupled to Laser capture microdissection is a promising approach for the identification of metabolic heterogeneity in the cancerous specimens. Citation Format: Emine Kazanc, Evi Karali, Vincen Wu, Paolo Inglese, James McKenzie, Aurelien Tripp, Nikos Koundouros, Thanasis Tsalikis, Hiromi Kudo, George Poulogiannis, Zoltan Takats. A multimodal analysis in breast cancer: Revealing metabolic heterogeneity using DESI-MS imaging with Laser-microdissection coupled transcriptome approach [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-042.

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

Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.

One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.

Ambient ionization mass spectrometry was developed as a sample preparation-free alternative to traditional MS-based workflows. Desorption electrospray ionization (DESI)-MS methods were demonstrated to allow the direct analysis of a broad range of samples including unaltered biological tissue specimens. In contrast to this advantageous feature, nowadays DESI-MS is almost exclusively used for sample preparation intensive mass spectrometric imaging (MSI) in the area of cancer research. As an alternative to MALDI, DESI-MSI offers matrix deposition-free experiment with improved signal in the lower (

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.

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.

Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were coregistered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within LNs. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched IHC images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign LN tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 89.5%, 100%, 100%, and 97.2%, respectively, when compared with gold-standard IHC. DESI-MSI has the potential to be a diagnostic tool for perioperative identification of LNM and compares favorably with techniques currently used by histopathology experts. Cancer Res; 76(19); 5647–56. ©2016 AACR.

The concentrations of aluminium, barium, calcium, copper, iron, magnesium, manganese, nickel, phosphorus, potassium, sodium, strontium, sulphur and zinc in white, green, black, Oolong and Pu-erh teas have been determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). Samples were microwave-digested and the performance characteristics of the method were verified by analysing a certified reference material. The measured elemental concentrations in tea leaves were used to differentiate the five tea varieties. Non-parametric analysis was applied to highlight significant differences between types, and pattern recognition methods were used to characterise samples. For this aim, linear discriminant analysis (LDA) and probabilistic neural networks (PNN) were used to construct classification models with an overall classification performance of 81% and 97%, respectively.

Objectives Progression through the adenoma-adenocarcinoma sequence has been shown to demonstrate hallmark shifts in the tissue lipidome. The incorporation of certain phospholipid subclasses into cellular membranes has previously been found to correlate with susceptibility to external stressors through alteration of membrane biodynamics. On this basis, it is hypothesized that modulation of cellular lipid modulation may influence radioresistance in rectal cancer. A desorption electrospray ionization mass spectrometry imaging (DESI-MSI) platform was utilized to demonstrate lipidomic shifts reflective of tumor heterogeneity and modulation of cellular lipids as a radioresistance mechanism. Methods 26 specimens of fresh, human rectal cancer tissue were harvested at surgical resection from matched cohorts; 13 of each from those who had received RT, and those who proceeded straight to surgery (STS). DESI-MSI was performed using an Orbitrap Fourier transform mass spectrometer in negative ion mode (mass range 150-2000m/z). Lipid spectra acquired were spatially aligned to subsequent H&E staining of the tissue section in a chemometric toolbox in MatLab, annotating morphologically distinct regions of the tumour microenvironment. Statistical modeling was performed incorporating clinicopathological metadata. ANOVA identified spectral peaks with discriminating power between both RT and STS groups, and between clusters of patients across the range of Mandard tumor regression grades (TRG). Lipid classes and putative structures were determined using an existing MS/MS data reference library (MetLin). Results Significant clustering of lipid species was demonstrated in both RT and STS groups, for both adenocarcinoma and stroma. Leave-One-Out, all-pixels cross-validation (LOOCV) yielded high predictive values (>97.9%) for RT vs. STS adenocarcinoma pixels. Cancers subjected to RT demonstrated significantly increased abundance of phospholipids compared to the STS group, notably phosphatidylglycerols (PG), phosphatidylethanolamines (PE) and phosphatidylserines (PS) on univariate analysis of all spectra, with a defined p/q value of 0.05 and negative log2 fold change of 0.1. Three-class PCA and discriminant analysis demonstrated clustering of samples according to Mandard TRG 1-2, TRG 3, and TRG 4-5. Clear separation of spectra was observed on LOOCV between all three classes. Compounds in the range 240-450m/z were implicated in this shift, in addition to the phospholipids as described. Conclusion This work has demonstrated a putative role for phospholipids and fatty acids in radioresistance, in a human rectal cancer cohort. DESI-MSI provides a unique foundation from which to continue ‘bottom-up' pathway analysis - utilizing both immunohistochemistry and tissue genotyping – which will assist in elucidating the possible mechanisms of response to radiotherapy in rectal cancer. Citation Format: Liam Robert Poynter, Reza Mirnezami, Renata Soares, James McKenzie, Pranav Patel, Nadia Peppa, Alexander Mirnezami, James Kinross, Ara Darzi, Zoltan Takats. Modulation of cellular phospholipids correlates with tumor regression grade and radio resistance in rectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3203.

A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that 1H–13C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the 1H–13C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of 13C and 1H chemical shift correlations from known metabolites.

High-resolution NMR spectroscopy is applied for molecular phenotyping across a range of pharmaceutical and clinical applications such as drug toxicity, disease diagnostics, and personalized healthcare studies. A typical NMR profile of a biological sample contains tens of thousands of signals arising from hundreds of endogenous and exogenous metabolites. The generated data requires advanced computational workflows to translate raw spectroscopic data into pharmacology and clinically useful information. This article outlines various chemoinformatics strategies that maximize disease and pharmacologically relevant molecular information recovery from one-dimensional NMR spectra of biological samples. In broad terms, the outlined strategies involve (i) raw analytical signal preprocessing for improved information recovery, (ii) multivariate statistical explorative and predictive analyses of NMR biological spectra, and (iii) time-course analyses to address a range of pharmaceutically and clinically relevant questions. Keywords: chemoinformatics; NMR spectroscopy; multivariate statistical analysis; signal processing; timecourse analysis

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.