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. [...

Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

We have developed an algorithm called Q5 for probabilistic classification of healthy vs. disease whole serum samples using mass spectrometry. The algorithm employs Principal Components Analysis (PCA) followed by Linear Discriminant Analysis (LDA) on whole spectrum Surface-Enhanced Laser Desorption/Ionization Time of Flight (SELDI-TOF) Mass Spectrometry (MS) data, and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a novel probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is non-iterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques, and can provide clues as to the molecular identities of differentially-expressed proteins and peptides

A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. <it>peaks</it>) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics.</p> <p>Results</p> <p>We propose a method for feature selection and extraction grounded on the theory of multi-scale spaces for high resolution spectra derived from analysis of serum. Then we use support vector machines for classification. In particular we use a database containing 216 samples spectra divided in 115 cancer and 91 control samples. The overall accuracy averaged over a large cross validation study is 98.18. The area under the ROC curve of the best selected model is 0.9962.</p> <p>Conclusion</p> <p>We improved previous known results on the problem on the same data, with the advantage that the proposed method has an unsupervised feature selection phase. All the developed code, as MATLAB scripts, can be downloaded from <url>http://medeaserver.isa.cnr.it/dacierno/spectracode.htm</url></p

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions

Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.Comment: Supporting website: https://single-cell.net/guideline

MALDI-ToF mass spectrometry biomarker profiling via multivariate data analysis application in the biopharmaceutical bioprocessing industry

PhD ThesisMatrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) is a technique by which protein profiles can be rapidly produced from biological samples. Proteomic profiling and biomarker identification using MALDI-ToF MS have been utilised widely in microbiology for bacteria identification and in clinical proteomics for disease-related biomarker discovery. To date, the benefits of MALDI-ToF MS have not been realised in the area of mammalian cell culture during bioprocessing. This thesis explores the approach of ‘intact-cell’ MALDI-ToF MS (ICM-MS) combined with projection to latent structures – discriminant analysis (PLS-DA), to discriminate between mammalian cell lines during bioprocessing. Specifically, the industrial collaborator, Lonza Biologics is interested in adopting this approach to discriminate between IgG monoclonal antibody producing Chinese hamster ovaries (CHO) cell lines based on their productivities and identify protein biomarkers which are associated with the cell line productivities. After classifying cell lines into two categories (high/low producers; Hs/Ls), it is hypothesised that Hs and Ls CHO cells exhibit different metabolic profiles and hence differences in phenotypic expression patterns will be observed. The protein expression patterns correlate to the productivities of the cell lines, and introduce between-class variability. The chemometric method of PLS-DA can use this variability to classify the cell lines as Hs or Ls. A number of differentially expressed proteins were matched and identified as biomarkers after a SwissProt/TrEMBL protein database search. The identified proteins revealed that proteins involved in biological processes such as protein biosynthesis, protein folding, glycolysis and cytoskeleton architecture were upregulated in Hs. This study demonstrates that ICM-MS combined with PLS-DA and a protein database search can be a rapid and valuable tool for biomarker discovery in the bioprocessing industry. It may help in providing clues to potential cell genetic engineering targets as well as a tool in process development in the bioprocessing industry. With the completion of the sequencing of the CHO genome, this study provides a foundation for rapid biomarker profiling of CHO cell lines in culture during recombinant protein manufacturing.Lonza Biologics