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

Exploring sugar metabolism in bread wheat for improving drought tolerance

Remobilization of stem WSC is well known to contribute to grain yield in wheat. There is, however, extensive genetic variation in the contribution of stem WSC to grain yield under post-anthesis water-deficit. Fructan 1-exohydrolase (1-FEH) is one of the major enzymes contributing to WSC remobilisation and the maintenance of grain yield under water-deficit. 1-FEH has three isoforms (1-FEH w1, w2 and w3) that degrade β - (2-1) fructan linkages thus contributing to fructan remobilization to grain. This thesis investigated the functional role of the three isoforms of the 1-FEH gene in WSC remobilisation under post anthesis water-deficit. Individual performance of the three isoforms was investigated using the corresponding isoform mutation lines derived from the Australian wheat variety Chara. Results from glasshouse experiments showed that the mutation of isoform 1-FEH w3 slowed down WSC remobilisation under post anthesis water-deficit and reduced grain filling and yield. In contrast, mutations of 1-FEH w1 and w2 did not affect WSC remobilisation under water-deficit. This means that 1-FEH w3 plays the leading functional role in WSC remobilisation during grain filling under water-deficit. This differences in remobilisation of WSC components between the mutation lines correlated with the expressional differences of the three isoforms of the 1-FEH gene across the lines. In the 1-FEH w3 mutation line, the expression of the other two isoforms (1-FEH w2 and w1) had the same level as the non-mutated parental cultivar Chara. However, in the 1-FEH w2 and w1 mutation lines, 1-FEH w3 showed significantly higher expression compared to Chara. The results indicated that the functional loss of the isoforms 1-FEH w2 and w1 was made up by the higher expression of the isoform 1-FEH w3 but the functional loss of the 1-FEH w3 isoform was not compensated by the other isoforms. This explains the ability of 1-FEH w2 and w1 mutation lines to maintain the same pattern of WSC remobilisation as the non-mutated parental cultivar. It was also, revealed that the expressional differences of the isforms of the 1-FEH gene across different mutation lines significantly influenced the degradation of WSC and its components under post anthesis water-deficit. Fructan, a fructose-based polymer synthesized from sucrose by fructosyltransferases (FTs), is the main component of wheat stem WSC and is a major source of sugar supply under post anthesis water-deficit when photosynthesis is reduced. Quick degradation of fructan is essential to remobilise sugar to developing grain under water-deficit and this is facilitated by FEHs. The 1-FEH w3 mutation line showed slower degradation and remobilization of fructan compared to the 1-FEH w2 and w1 mutation lines and Chara. This slow degradation made the 1-FEH w3 mutation line partially susceptible to post anthesis water-deficit. Noticeably, differences in WSC component degradation and gene expression of 1-FEH isoforms only became evident under post anthesis water-deficit and not in well-watered plants. This thesis also characterised the 1-FEH gene mutation, by mapping and annotating the mutated region. The F1 seeds, developed by back crossing the 1-FEH w1, w2 and w3 mutation lines with Chara, were genotyped using the Infinium 90K SNP iSelect platform. Putative deletions were identified in the FEH mutation lines encompassing the FEH genomic regions. A total of 15, 20 and 15SNPs were identified within the mutation regions of 1-FEH w1 w2, and w3, respectively. Mapping analysis demonstrated that the mutation affected significantly longer regions than the target gene regions of 1-FEH w1, w3 and w2. From the annotation of the mutation regions, 8 and 6 non-target genes were discovered on chromosomes 6A and 6B, respectively. The annotation of the 1-FEH w2 mutated region was complicated by the presence of an extra three copies of the gene on chromosome 6D. Functional roles of the non-target genes was carried out following computational biology approaches and confirmed that none of the affected non-target genes were expected to have a direct influence on 1-FEH gene function. This study also ratified the association of the distinct role of the 1-FEH w3 gene in sugar remobilisation to the developing wheat grain. Accumulation of oligosaccharides at two seed developmental stages were examined in the 1-FEH w3 mutation line in comparison to Chara under well-watered and water-deficit conditions. This study successfully overcome the challenge of preparing 25 μm seed sections by adopting cryosectioning using egg white which provided compatibility with the mass spectrometric equipment and enabled the production of ions from the oligosaccharides by the laser. Hexose and its polymers were detected separately by the mass spectrometry imaging (MSI) without any enzymatic digestion thus providing information regarding the localisation of sugar accumulation within the tissues of developing seeds. The abundance and localisation pattern of the identified oligosaccharides was influenced by the post anthesis water-deficit treatment. Under water-deficit, the mutation of the 1-FEH w3 reduced the abundance of oligosaccharide accumulation in two stages of seed development (17 DAA and 22 DAA) indicating it pivotal role under post anthesis water-deficit. This is the first study to use MSI to explore sugar accumulation directly within the tissue of developing seeds of wheat. This thesis established the individual role of three isoforms of 1-FEH in remobilising WSC under post anthesis water-deficit and provides unequivocal evidence that 1-FEH w3 is taking the most vital role. This new insight into the distinct role of the 1-FEH gene isoforms under post anthesis water-deficit should assist in providing new gene targets for water-deficit tolerant wheat breeding in the future

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

Peak annotation and data analysis software tools for mass spectrometry imaging

La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z)) sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar, el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm)

Computational methods for the analysis of mass spectrometry imaging data

A powerful enhancement to MS-based detection is the addition of spatial information to the chemical data; an approach called mass spectrometry imaging (MSI). MSI enables two- and three-dimensional overviews of hundreds of molecular species over a wide mass range in complex biological samples. In this work, we present two computational methods and a workflow that address three different aspects of MSI data analysis: correction of mass shifts, unsupervised exploration of the data and importance of preprocessing and chemometrics to extract meaningful information from the data. We introduce a new lock mass-free recalibration procedure that enables to significantly reduce these mass shift effects in MSI data. Our method exploits similarities amongst peaklist pairs and takes advantage of the spatial context in three different ways, to perform mass correction in an iterative manner. As an extension of this work, we also present a Java-based tool, MSICorrect, that implements our recalibration approach and also allows data visualization. In the next part, an unsupervised approach to rank ion intensity maps based on the abundance of their spatial pattern is presented. Our method provides a score to every ion intensity map based on the abundance of spatial pattern present in it and then ranks all the maps using it. To know which masses exhibit similar spatial distribution, our method uses spatial-similarity based grouping to provide lists of masses that exhibit similar distribution patterns. In the last part, we demonstrate the application of a data preprocessing and multivariate analysis pipeline to a real-world biological dataset. We demonstrate this by applying the full pipeline to a high-resolution MSI dataset acquired from the leaf surface of Black cottonwood (Populus trichocarpa). Application of the pipeline helped in highlighting and visualizing the chemical specificity on the leaf surface