Imperial College London

DrJamesMcKenzie

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Research Associate
 
 
 
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j.mckenzie

 
 
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380aSir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

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26 results found

Mason SE, Manoli E, Alexander JL, Poynter L, Ford L, Paizs P, Adebesin A, McKenzie JS, Rosini F, Goldin R, Darzi A, Takats Z, Kinross JMet al., 2021, Lipidomic profiling of colorectal lesions for real-time tissue recognition and risk-stratification using rapid evaporative ionisation mass spectrometry., Annals of Surgery, ISSN: 0003-4932

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.

Journal article

Dorado E, Doria ML, Nagelkerke A, McKenzie JS, Maneta-Stavrakaki S, Ion C, Whittaker T, Nicholson J, Stevens MM, Coombes RC, Takats Zet al., 2021, Lipidomic analysis of extracellular vesicles and its potential for the identification of body fluid-based biomarkers for breast cancer diagnosis., Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472

Conference paper

Capece D, D'Andrea D, Begalli F, Goracci L, Tornatore L, Alexander JL, Di Veroli A, Leow S-C, Vaiyapuri TS, Ellis JK, Verzella D, Bennett J, Savino L, Ma Y, McKenzie JS, Doria ML, Mason SE, Chng KR, Keun HC, Frost G, Tergaonkar V, Broniowska K, Stunkel W, Takats Z, Kinross JM, Cruciani G, Franzoso Get al., 2021, Enhanced triacylglycerol catabolism by Carboxylesterase 1 promotes aggressive colorectal carcinoma., Journal of Clinical Investigation, ISSN: 0021-9738

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-κB transcription-factor pathway associates with advanced disease stages and shorter survival in CRC patients. NF-κB 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-κB affects the metabolic adaptations that fuel aggressive disease in CRC patients is unknown. Here, we identified carboxylesterase 1 (CES1) as an essential NF-κB-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 CRC patients. Accordingly, NF-κB drove CES1 expression in CRC consensus molecular subtype (CMS)4, 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 unfavourable prognostic correlation distinguished CES1 from other intracellular triacylglycerol lipases and suggest CES1 could provide a route to treat aggressive CRC.

Journal article

Tzafetas M, Mitra A, Paraskevaidi M, Bodai Z, Kalliala I, Bowden S, Lathouras K, Rosini F, Szasz M, Savage A, Manoli E, Balog J, McKenzie J, Lyons D, Bennett P, MacIntyre D, Ghaem-Maghami S, Takats Z, Kyrgiou Met al., 2020, The intelligent knife (iKnife) and its intraoperative diagnostic advantage for the treatment of cervical disease (vol 117, pg 7338, 2020), PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 117, Pages: 18892-18892, ISSN: 0027-8424

Journal article

Abbassi-Ghadi N, Antonowicz S, McKenzie J, Kumar S, Huang J, Jones E, Strittmatter N, Petts G, Kudo H, court S, Hoare J, Veselkov K, Goldin R, Takats Z, Hanna Get al., 2020, De novo lipogenesis alters the phospholipidome of esophageal adenocarcinoma, Cancer Research, Vol: 80, Pages: 2764-2774, ISSN: 0008-5472

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.

Journal article

Tzafetas M, Mitra A, Paraskevaidi M, Bodai Z, Kalliala I, Bowden S, Lathouras K, Rosini F, Szasz M, Savage A, Balog J, McKenzie J, Lyons D, Bennett P, MacIntyre D, Ghaem-Maghami S, Takats Z, Kyrgiou Met al., 2020, The intelligent-Knife (i-Knife) and its intraoperative diagnostic advantage for the treatment of cervical disease, Proceedings of the National Academy of Sciences of USA, Vol: 117, Pages: 7338-7346, ISSN: 0027-8424

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.

Journal article

Phelps DL, Balog J, Gildea LF, Bodai Z, El-Bahrawy MA, Speller AVM, Rosini F, Kudo H, McKenzie JS, Brown R, Takats Z, Ghaem-Maghami Set al., 2018, The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS), British Journal of Cancer, Vol: 118, Pages: 1349-1358, ISSN: 0007-0920

BackgroundSurvival 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.MethodsAerosol 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.ResultsA 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 < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001).ConclusionsThe REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.

Journal article

Bergholt MS, Serio A, McKenzie JS, Boyd A, Soares RF, Tillner J, Chiappini C, Wu V, Dannhorn A, Takats Z, Williams A, Stevens MMet al., 2017, Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis, ACS Central Science, Vol: 4, Pages: 39-51, ISSN: 2374-7943

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models.

Journal article

Tillner J, Wu V, Jones EA, Pringle SD, Karancsi T, Dannhorn A, Veselkov K, McKenzie JS, Takats Zet al., 2017, Faster, more reproducible DESI-MS for biological tissue imaging, Journal of The American Society for Mass Spectrometry, Vol: 28, Pages: 2090-2098, ISSN: 1044-0305

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.

Journal article

St John ERC, Leff D, takats Z, Darzi Aet al., 2017, Rapid Evaporative Ionisation Mass Spectrometry of Electrosurgical Vapours for the Identification of Breast Pathology: Towards an Intelligent Knife for Breast Cancer Surgery, Breast Cancer Research, Vol: 19, ISSN: 1465-542X

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

Journal article

Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RCet al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539

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.

Journal article

Takats Z, Strittmatter N, McKenzie JS, 2017, Ambient Mass Spectrometry in Cancer Research, APPLICATIONS OF MASS SPECTROMETRY IMAGING TO CANCER, Editors: Drake, McDonnell, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 231-256, ISBN: 978-0-12-805249-5

Book chapter

Doria ML, McKenzie JS, Mroz A, Phelps DL, Speller A, Rosini F, Strittmatter N, Golf O, Veselkov K, Brown R, Ghaem-Maghami S, Takats Zet al., 2016, Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging, Scientific Reports, Vol: 6, ISSN: 2045-2322

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.

Journal article

Veselkov KA, Inglese, Galea D, McKenzie JS, Nicholson JKet al., 2016, Statistical Tools for Molecular Covariance Spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Editors: Lindon, Tranter, Koppenaal, Publisher: Elsevier B.V., Pages: 243-249, ISBN: 978-0-12-803224-4

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.

Book chapter

Alexander J, Gildea L, Balog J, Speller A, McKenzie J, Muirhead L, Scott A, Kontovounisios C, Rasheed S, Teare J, Hoare J, Veselkov K, Goldin R, Tekkis P, Darzi A, Nicholson J, Kinross J, Takats Zet al., 2016, 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, Surgical Endoscopy and Other Interventional Techniques, Vol: 31, Pages: 1361-1370, ISSN: 1432-2218

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 featur

Journal article

Strittmatter N, Lovrics A, Sessler J, McKenzie JS, Bodai Z, Doria ML, Kucsma N, Szakacs G, Takats Zet al., 2016, Shotgun Lipidomic Profiling of the NCI60 Cell Line Panel Using Rapid Evaporative Ionization Mass Spectrometry., Analytical Chemistry, Vol: 88, Pages: 7507-7514, ISSN: 1086-4377

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.

Journal article

Abbassi-Ghadi N, Golf O, Kumar S, Antonowicz S, McKenzie JS, Huang J, Strittmatter N, Kudo H, Jones EA, Veselkov K, Goldin R, Takáts Z, Hanna GBet al., 2016, Imaging of esophageal lymph node metastases by desorption electrospray ionization mass spectrometry, Cancer Research, Vol: 76, Pages: 5647-5656, ISSN: 1538-7445

Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intra-observer variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes 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 co-registered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within lymph nodes. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched immunohistochemistry images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign lymph node 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 per-cent, respectively, when compared to gold-standard immunohistochemistry. DESI-MSI has the potential to be a diagnostic tool for peri-operative identification of LNM and compares favorably with techniques currently used by histopathology experts.

Journal article

St John E, Rossi M, Balog J, McKenzie J, Muirhead L, Speller A, Gildea L, Veselkov K, Shousha S, Ramakrishnan R, Takats Z, Darzi A, Leff Det al., 2016, Real time intraoperative classification of breast tissue with the intelligent knife, European Journal of Surgical Oncology (EJSO), Vol: 42, Pages: S25-S25, ISSN: 0748-7983

Introduction: Re-operation for close/positive margins following breast-conserving surgery occurs frequently (≈20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) determines the structural lipid profile of tissues by the on-line analysis of electrosurgical smoke and uses this information for the rapid identification of dissected tissues. We evaluated its performance regarding real-time classification of heterogeneous breast tissue.Method: 155 patients enrolled in this study comprising method optimization (n=40), construction of a tissue specific ex-vivo database (n=87), and intraoperative analysis (n=28). Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer via a modified surgical hand-piece. Tissue identification results obtained by the multivariate statistical analysis of MS data were validated by histopathology. Intraoperative REIMS data was acquired from resection margins and time-synchronized to operative videos. Ex-vivo classification models were used to predict intraoperative margin status.Results: An ex-vivo classification model using spectral data from healthy breast (n=561) and breast tumours (n=139) provided 92.1% sensitivity, 96.4% specificity and 95.6% overall accuracy. 17,974 spectra from 28 patients were obtained intra-operatively in real-time. The method demonstrated 100% sensitivity and 77.3% specificity regarding intraoperative positive margin detection. For histologically negative margins, misclassification (REIMS false positive) was observed for only 0.5% of total tissue dissection time (69/14,023 spectra).Conclusions: The REIMS method has been optimized for real-time analysis of intraoperative heterogeneous breast tissue, and the results suggest spectral analysis is accurate and rapid for determination of oncological margin status intra-operatively as an “Intelligent Knife”.

Journal article

Alexander JL, Scott A, Mroz A, Perdones-Montero A, Mckenzie J, Rees DN, Speller A, Veselkov K, Kinross JM, Takats Z, Marchesi J, Teare JPet al., 2016, 91 Mass Spectrometry Imaging (MSI) of Microbiome-Metabolome Interactions in Colorectal Cancer, 2016 Digestive Diseases Week, Publisher: Elsevier, Pages: S23-S23, ISSN: 0016-5085

Conference paper

Tillner J, McKenzie JS, Jones EA, Speller AV, Walsh JL, Veselkov KA, Bunch J, Takats Z, Gilmore ISet al., 2016, Investigation of the Impact of Desorption Electrospray Ionization Sprayer Geometry on Its Performance in Imaging of Biological Tissue., Analytical Chemistry, Vol: 88, Pages: 4808-4816, ISSN: 0003-2700

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.

Journal article

Veselkov KA, Inglese P, Galea D, McKenzie JS, Nicholson JKet al., 2016, Statistical tools for molecular covariance spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Pages: 243-249, ISBN: 9780128032244

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.

Book chapter

Veselkov KA, McKenzie JS, Nicholson JK, 2015, Multivariate Data Analysis Methods for NMR-based Metabolic Phenotyping in Pharmaceutical and Clinical Research, NMR in Pharmaceutical Science, Editors: Everett, Harris, Lindon, Wilson, Publisher: John Wiley & Sons, Pages: 323-334, ISBN: 9781118660256

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.

Book chapter

Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mroz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov Tet al., 2015, Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry, GigaScience, Vol: 4, ISSN: 2047-217X

Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS hasunique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatialorganization of biological processes, and has growing potential to be introduced into routine use in both biologyand medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3Dimaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack ofpublicly available benchmark datasets needed to evaluate and compare algorithms.Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired,supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights,an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two typesof 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected frommurine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies culturedin Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectaladenocarcinoma.Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selectedhigh-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

Journal article

McKenzie JS, Donarski JA, Wilson JC, Charlton AJet al., 2011, Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics, Progress in Nuclear Magnetic Resonance Spectroscopy, Vol: 59, Pages: 336-359, ISSN: 0079-6565

Journal article

McKenzie JS, Jurado JM, de Pablos F, 2010, Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks, Food Chemistry, Vol: 123, Pages: 859-864, ISSN: 0308-8146

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. © 2010 Elsevier Ltd.

Journal article

McKenzie JS, Charlton AJ, Donarski JA, MacNicoll AD, Wilson JCet al., 2010, Peak fitting in 2D 1H–13C HSQC NMR spectra for metabolomic studies, Metabolomics, Vol: 6, Pages: 574-582, ISSN: 1573-3882

Journal article

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