struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond.

SUMMARY: Implementing and combining methods from a diverse range of R/Bioconductor packages into 'omics' data analysis workflows represents a significant challenge in terms of standardization, readability and reproducibility. Here, we present an R/Bioconductor package, named struct (Statistics in R using Class-based Templates), which defines a suite of class-based templates that allows users to develop and implement highly standardized and readable statistical analysis workflows. Struct integrates with the STATistics Ontology to ensure consistent reporting and maximizes semantic interoperability. We also present a toolbox, named structToolbox, which includes an extensive set of commonly used data analysis methods that have been implemented using struct. This toolbox can be used to build data-analysis workflows for metabolomics and other omics technologies. AVAILABILITY AND IMPLEMENTATION: struct and structToolbox are implemented in R, and are freely available from Bioconductor (http://bioconductor.org/packages/struct and http://bioconductor.org/packages/structToolbox), including documentation and vignettes. Source code is available and maintained at https://github.com/computational-metabolomics.

Mid-infrared multispectral tissue imaging using a chalcogenide fiber supercontinuum source.

We present, to the best of our knowledge, the first demonstration of mid-infrared supercontinuum (SC) tissue imaging at wavelengths beyond 5 µm using a fiber-coupled SC source spanning 2-7.5 µm. The SC was generated in a tapered large-mode-area chalcogenide photonic crystal fiber in order to obtain broad bandwidth, high average power, and single-mode output for diffraction-limited imaging performance. Tissue imaging was demonstrated in transmission at selected wavelengths between 5.7 (1754 cm-1) and 7.3 µm (1370 cm-1) by point scanning over a sub-millimeter region of colon tissue, and the results were compared to images obtained from a commercial instrument.

An update on the use of Raman spectroscopy in molecular cancer diagnostics: current challenges and further prospects.

INTRODUCTION: Cancer is responsible for an extraordinary burden of disease, affecting 90.5 million people worldwide in 2015. Outcomes for these patients are improved when the disease is diagnosed at an early, or even precancerous, stage. Raman spectroscopy is demonstrating results that show its ability to detect the molecular changes that are diagnostic of precancerous and cancerous tissue. This review highlights the new advances occurring in this domain. Areas covered: PubMed searches were undertaken to identify new research in the utilisation of Raman spectroscopy in cancer diagnostics. The areas in which Raman spectroscopy is showing promise are covered, including improving the accuracy of identifying precancerous changes, using the technology in real time, in vivo modalities, the search for a biomarker to aid potential screening and predicting the response of the cancer to the treatment regimen. Expert commentary: Many of the examples in this review are focused on Barrett's oesophagus and oesophageal adenocarcinoma as this is my area of expertise and perfectly exemplifies where Raman spectroscopy could be utilised in clinical practise. The authors discuss the areas where they believe current knowledge is lacking and how Raman spectroscopy could answer the dilemmas that are still faced in the management of cancer.

Developments in optical imaging for gastrointestinal surgery.

To improve outcomes for patients with cancer, in terms of both survival and a reduction in the morbidity and mortality that results from surgical resection and treatment, there are two main areas that require improvement. Accurate early diagnosis of the cancer, at a stage where curative and, ideally, minimally invasive treatment is achievable, is desired as well as identification of tumor margins, lymphatic and distant disease, enabling complete, but not unnecessarily extensive, resection. Optical imaging is making progress in achieving these aims. This review discusses the principles of optical imaging, focusing on fluorescence and spectroscopy, and the current research that is underway in GI tract carcinomas.

High-resolution FTIR imaging of colon tissues for elucidation of individual cellular and histopathological features.

Novel technologies that could complement current histopathology based cancer diagnostic methods are under examination. In this endeavour mid-infrared spectroscopic imaging is a promising candidate that can provide valuable bio-molecular information from unstained cells and tissues in a rapid and a non-destructive manner. With this imaging technique, the biochemical information obtained from smaller areas of the tissues can be of clinical significance and hence the measured pixel size. Until recently it was difficult to obtain spectral data from pixels below around 5 microns square. High NA objectives have been utilised to reduce the ideal diffraction limit, enabling for the first time elucidation of subcellular features. In this context, the ability of high-resolution imaging, obtained using novel high-magnification optics retro-fitted onto a bench top FTIR imaging system, to characterise histopathological features in colonic tissues has been tested. Formalin fixed paraffin embedded colon tissues from three different pathologies were imaged directly using the conventional and the high-magnification imaging set-ups. To circumvent chemical de-paraffinization protocols, an extended multiplicative signal correction (EMSC) based electronic de-paraffinization was carried out on all the infrared images. Multivariate analysis of the high-magnification infrared imaging data showed a detailed information of the histological features of the colon tissue in comparison to conventional imaging. Furthermore, high-magnification imaging has enabled a label-free characterization of the mucin rich goblet cell features in an unprecedented manner. The current study demonstrates the applicability of high-magnification FTIR imaging to characterise complex tissues on a smaller scale that could be of clinical significance.

Method for identification of spectral targets in discrete frequency infrared spectroscopy for clinical diagnostics.

Spectroscopic imaging with discrete frequency infrared (DF-IR) has the potential to have a major impact on the clinical utility of IR imaging techniques for biochemical detection of disease. This can be achieved in real time using imaging at selected wavenumbers tuned to molecular absorptions of interest enabling tissue and disease-specific contrast to be obtained from the sample. However, selecting the appropriate wavenumbers to measure for DF-IR is critical, since vital diagnostic information could be missed. Here we demonstrate the application of partial least squares discriminant analysis (PLS-DA) and variable importance for projection (VIP) to identify key diagnostic wavenumber targets for the detection of dysplasia in human colon polyp sections. A small dataset, including 41 regions of interest (25 benign, 16 dysplastic; 5175 spectra in total), was selected from Fourier transform infrared (FT-IR) images of human colon polyp sections. PLS-DA was used to differentiate between benign and cancerous human colon polyp sections (sensitivity 95%, specificity 93% cross-validated), and VIP scores were calculated for all wavenumbers. A second PLS-DA model was then calculated using only variables that VIP identified as significant, reducing the number of wavenumbers to ~25% of the full dataset. The resulting cross-validated sensitivity and specificity (93 and 90%, respectively) indicate that the VIP method selects the key diagnostic wavenumbers for this dataset. Finally, a robust subset of variables was identified by selecting wavenumbers that exceeded the minimum VIP score in >95% of our validation iterations. A cross-validated PLS-DA model using only the robustly selected wavenumber targets (~20% of the original wavenumbers) resulted in sensitivity and specificity of 91 and 82%, respectively, indicating that PLS-DA and VIP are suitable approaches for the selection of wavenumber targets in DF-IR.

Evaluation of different tissue de-paraffinization procedures for infrared spectral imaging.

In infrared spectral histopathology, paraffin embedded tissues are often de-paraffinized using chemical agents such as xylene and hexane. These chemicals are known to be toxic and the routine de-waxing procedure is time consuming. A comparative study was carried out to identify alternate de-paraffinization methods by using paraffin oil and electronic de-paraffinization (using a mathematical computer algorithm) and their effectiveness was compared to xylene and hexane. Sixteen adjacent tissue sections obtained from a single block of a normal colon tissue were de-paraffinized using xylene, hexane and paraffin oil (+ hexane wash) at five different time points each for comparison. One section was reserved unprocessed for electronic de-paraffinization based on a modified extended multiplicative signal correction (EMSC). IR imaging was carried out on these tissue sections. Coefficients based on the fit of a pure paraffin model to the IR images were then calculated to estimate the amount of paraffin remaining after processing. Results indicate that on average xylene removes more paraffin in comparison to hexane and paraffin oil although the differences were small. This makes paraffin oil, followed by a hexane wash, an interesting and less toxic alternative method of de-paraffinization. However, none of the chemical methods removed paraffin completely from the tissues at any given time point. Moreover, paraffin was removed more easily from the glandular regions than the connective tissue regions indicating a form of differential paraffin retention based on the histology. In such cases, the use of electronic de-paraffinization to neutralize such variances across different tissue regions might be considered. Moreover it is faster, reduces scatter artefacts by index matching and enables samples to be easily stored for further analysis if required.

Utilising non-consensus pathology measurements to improve the diagnosis of oesophageal cancer using a Raman spectroscopic probe.

The application of semi-supervised methodology to improve the classification performance of a Raman spectroscopic probe for the diagnosis of oesophageal cancer is described. It is well known that gold standard histopathology diagnosis can be highly subjective, particularly for diseases which have several stages, such as cancer. A 'consensus' pathology decision can be obtained to ensure a robust gold standard by obtaining a diagnosis from several experts and samples are then only included in standard classification models if they have been assigned the same pathology by all experts. This can result in a significant number of samples that are excluded from the analysis as no consensus was reached. In this work semi-supervised methodology was used to extend Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to incorporate samples without consensus pathology when discriminating between benign and oesophageal cancer specimens measured using a Raman endoscopic probe ex vivo. We demonstrate that a fully semi-supervised approach improved sensitivity and specificity from 73% and 78% (PCA-LDA) to 78% and 84% (semi-supervised) for discriminating between intestinal metaplasia and dysplasia and from 44% and 66% (PCA-LDA) to 63% and 72% (semi-supervised) when discriminating between intestinal metaplasia and low grade dysplasia.

Discrimination between benign, primary and secondary malignancies in lymph nodes from the head and neck utilising Raman spectroscopy and multivariate analysis.

BACKGROUND: The potential use of Raman spectroscopy (RS) for the detection of malignancy within lymph nodes of the head and neck was evaluated. RS measures the presence of biomolecules by the inelastic scattering of light within cells and tissues. This can be performed in vivo in real-time. METHODS: 103 lymph nodes were collected from 23 patients undergoing surgery for suspicious lymph nodes. Five pathologies, defined by consensus histopathology, were collected including reactive nodes (benign), Hodgkin's and non-Hodgkin's lymphomas, metastases from both squamous cell carcinomas and adenocarcinomas. Raman spectra were measured with 830 nm excitation from numerous positions on each biopsy. Spectral diagnostic models were constructed using principal component analysis followed by linear discriminant analysis (PCA-LDA), and by partial least squares discriminant analysis (PLS-DA) for comparison. Two-group models were constructed to distinguish between reactive and malignant nodes, and three-group models to distinguish between the benign, primary and secondary conditions. RESULTS: Results were validated using a repeated subsampling procedure. Sensitivities and specificities of 90% and 86% were obtained using PCA-LDA, and 89% and 88% using PLS-DA, for the two-group models. Both PCA-LDA and PLS-DA models were also found to be very successful at discriminating between pathologies in the three-group models achieving sensitivities and specificities of over 78% and 89% for PCA-LDA, and over 81% and 89% for PLS-DA for all three pathology groups. CONCLUSION: Raman spectroscopy and chemometric techniques can be successfully utilised in combination for discriminating between different cancerous conditions of lymph nodes from the head and neck.