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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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