A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife

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

Diagnostic accuracy of intraoperative margin assessment techniques in surgery for head and neck squamous cell carcinoma: a meta-analysis

BACKGROUND: Positive margins following head and neck squamous cell carcinoma (HNSCC) surgery lead to significant morbidity and mortality. Existing Intraoperative Margin Assessment (IMA) techniques are not widely used due to limitations in sampling technique, time constraints and resource requirements. We performed a meta-analysis of the diagnostic performance of existing IMA techniques in HNSCC, providing a benchmark against which emerging techniques may be judged. METHODS: The study was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Studies were included if they reported diagnostic metrics of techniques used during HNSCC surgery, compared with permanent histopathology. Screening, manuscript review and data extraction was performed by multiple independent observers. Pooled sensitivity and specificity were estimated using the bivariate random effects model. RESULTS: From an initial 2344 references, 35 studies were included for meta-analysis. Sensitivity (Sens), specificity (Spec), diagnostic odds ratio (DOR) and area under the receiver operating characteristic curve (AUROC) were calculated for each group (n, Sens, Spec, DOR, AUROC): frozen section = 13, 0.798, 0.991, 309.8, 0.976; tumour-targeted fluorescence (TTF) = 5, 0.957, 0.827, 66.4, 0.944; optical techniques = 10, 0.919, 0.855, 58.9, 0.925; touch imprint cytology = 3, 0.925, 0.988, 51.1, 0.919; topical staining = 4, 0.918, 0.759, 16.4, 0.833. CONCLUSIONS: Frozen section and TTF had the best diagnostic performance. Frozen section is limited by sampling error. TTF shows promise but involves administration of a systemic agent. Neither is currently in widespread clinical use. Emerging techniques must demonstrate competitive diagnostic accuracy whilst allowing rapid, reliable, cost-effective results

Faster, more reproducible DESI-MS for biological tissue imaging

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 Abstract ᅟ

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology

Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI

Enhancement of ambient mass spectrometry imaging data by image restoration

Mass spectrometry imaging (MSI) has been a key driver of groundbreaking discoveries in a number of fields since its inception more than 50 years ago. Recently, MSI development trends have shifted towards ambient MSI (AMSI) as the removal of sample-preparation steps and the possibility of analysing biological specimens in their natural state have drawn the attention of multiple groups across the world. Nevertheless, the lack of spatial resolution has been cited as one of the main limitations of AMSI. While significant research effort has presented hardware solutions for improving the resolution, software solutions are often overlooked, although they can usually be applied in a cost-effective manner after image acquisition. In this vein, we present two computational methods that we have developed to directly enhance the image resolution post-acquisition. Robust and quantitative resolution improvement is demonstrated for 12 cases of openly accessible datasets across laboratories around the globe. Using the same universally applicable Fourier imaging model, we discuss the possibility of true super-resolution by software for future studies

De novo lipogenesis alters the phospholipidome of esophageal adenocarcinoma

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 can support objective diagnosis in minutes using a routine frozen tissue section. However, whether mass spectrometry imaging 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 ionisation mass spectrometry imaging (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariable models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (area-under-curve = 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 pre-malignant 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 GPL chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of pre-malignant tissues and unveils mechanisms 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 contribute to esophageal carcinogenesis

Type IIb Supernova SN 2011dh: Spectra and Photometry from the Ultraviolet to the Near-Infrared

We report spectroscopic and photometric observations of the Type IIb SN 2011dh obtained between 4 and 34 days after the estimated date of explosion (May 31.5 UT). The data cover a wide wavelength range from 2,000 Angstroms in the UV to 2.4 microns in the NIR. Optical spectra provide line profiles and velocity measurements of HI, HeI, CaII and FeII that trace the composition and kinematics of the SN. NIR spectra show that helium is present in the atmosphere as early as 11 days after the explosion. A UV spectrum obtained with the STIS reveals that the UV flux for SN 2011dh is low compared to other SN IIb. The HI and HeI velocities in SN 2011dh are separated by about 4,000 km/s at all phases. We estimate that the H-shell of SN 2011dh is about 8 times less massive than the shell of SN 1993J and about 3 times more massive than the shell of SN 2008ax. Light curves (LC) for twelve passbands are presented. The maximum bolometric luminosity of $1.8 \pm 0.2 \times 10^{42}$ erg s$^{-1}$ occurred about 22 days after the explosion. NIR emission provides more than 30% of the total bolometric flux at the beginning of our observations and increases to nearly 50% of the total by day 34. The UV produces 16% of the total flux on day 4, 5% on day 9 and 1% on day 34. We compare the bolometric light curves of SN 2011dh, SN 2008ax and SN 1993J. The LC are very different for the first twelve days after the explosions but all three SN IIb display similar peak luminosities, times of peak, decline rates and colors after maximum. This suggests that the progenitors of these SN IIb may have had similar compositions and masses but they exploded inside hydrogen shells that that have a wide range of masses. The detailed observations presented here will help evaluate theoretical models for this supernova and lead to a better understanding of SN IIb.Comment: 23 pages, 14 figures, 9 tables, accepted by Ap

Mass spectrometry: from imaging to metabolic networks

A deeper understanding of inter-tumorand intra-tumorheterogeneity is a critical factor for the advancement of next generation strategies against cancer. Under the hypothesis that heterogeneous progression of tumorsis mirrored by their metabolic heterogeneity, detection of biochemical mechanisms responsible of the local metabolism becomes crucial.We show that network analysis of co-localized ions from mass spectrometry imaging data provides a detailed chemo-spatial insightinto the metabolic heterogeneity of tumor. Furthermore, module preservation analysis between colorectal cancer patients with and without metastatic recurrence suggests hypotheses on the nature of the different local metabolic pathways