Whole Reproductive System Non-Negative Matrix Factorization Mass Spectrometry Imaging of an Early-Stage Ovarian Cancer Mouse Model.

High-grade serous carcinoma (HGSC) is the most common and deadliest form of ovarian cancer. Yet it is largely asymptomatic in its initial stages. Studying the origin and early progression of this disease is thus critical in identifying markers for early detection and screening purposes. Tissue-based mass spectrometry imaging (MSI) can be employed as an unbiased way of examining localized metabolic changes between healthy and cancerous tissue directly, at the onset of disease. In this study, we describe MSI results from Dicer-Pten double-knockout (DKO) mice, a mouse model faithfully reproducing the clinical nature of human HGSC. By using non-negative matrix factorization (NMF) for the unsupervised analysis of desorption electrospray ionization (DESI) datasets, tissue regions are segregated based on spectral components in an unbiased manner, with alterations related to HGSC highlighted. Results obtained by combining NMF with DESI-MSI revealed several metabolic species elevated in the tumor tissue and/or surrounding blood-filled cyst including ceramides, sphingomyelins, bilirubin, cholesterol sulfate, and various lysophospholipids. Multiple metabolites identified within the imaging study were also detected at altered levels within serum in a previous metabolomic study of the same mouse model. As an example workflow, features identified in this study were used to build an oPLS-DA model capable of discriminating between DKO mice with early-stage tumors and controls with up to 88% accuracy.

omniSpect: an open MATLAB-based tool for visualization and analysis of matrix-assisted laser desorption/ionization and desorption electrospray ionization mass spectrometry images.

We present omniSpect, an open source web- and MATLAB-based software tool for both desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) that performs computationally intensive functions on a remote server. These functions include converting data from a variety of file formats into a common format easily manipulated in MATLAB, transforming time-series mass spectra into mass spectrometry images based on a probe spatial raster path, and multivariate analysis. OmniSpect provides an extensible suite of tools to meet the computational requirements needed for visualizing open and proprietary format MSI data.

A fast least-squares algorithm for population inference.

BACKGROUND: Population inference is an important problem in genetics used to remove population stratification in genome-wide association studies and to detect migration patterns or shared ancestry. An individual's genotype can be modeled as a probabilistic function of ancestral population memberships, Q, and the allele frequencies in those populations, P. The parameters, P and Q, of this binomial likelihood model can be inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient based approaches such as sequential quadratic programming. This paper proposes a least-squares simplification of the binomial likelihood model motivated by a Euclidean interpretation of the genotype feature space. This results in a faster algorithm that easily incorporates the degree of admixture within the sample of individuals and improves estimates without requiring trial-and-error tuning. RESULTS: We show that the expected value of the least-squares solution across all possible genotype datasets is equal to the true solution when part of the problem has been solved, and that the variance of the solution approaches zero as its size increases. The Least-squares algorithm performs nearly as well as Admixture for these theoretical scenarios. We compare least-squares, Admixture, and FRAPPE for a variety of problem sizes and difficulties. For particularly hard problems with a large number of populations, small number of samples, or greater degree of admixture, least-squares performs better than the other methods. On simulated mixtures of real population allele frequencies from the HapMap project, Admixture estimates sparsely mixed individuals better than Least-squares. The least-squares approach, however, performs within 1.5% of the Admixture error. On individual genotypes from the HapMap project, Admixture and least-squares perform qualitatively similarly and within 1.2% of each other. Significantly, the least-squares approach nearly always converges 1.5- to 6-times faster. CONCLUSIONS: The computational advantage of the least-squares approach along with its good estimation performance warrants further research, especially for very large datasets. As problem sizes increase, the difference in estimation performance between all algorithms decreases. In addition, when prior information is known, the least-squares approach easily incorporates the expected degree of admixture to improve the estimate.

Multivariate hypergeometric similarity measure.

We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets.

Seaweed allelopathy against coral: surface distribution of a seaweed secondary metabolite by imaging mass spectrometry.

Coral reefs are in global decline, with seaweeds increasing as corals decrease. Although seaweeds inhibit coral growth, recruitment, and survivorship, the mechanism of these interactions is poorly understood. Here, we used field experiments to show that contact with four common seaweeds induces bleaching on natural colonies of Porites rus. Controls in contact with inert, plastic mimics of seaweeds did not bleach, suggesting seaweed effects resulted from allelopathy rather than shading, abrasion, or physical contact. Bioassay-guided fractionation of the hydrophobic extract from the red alga Phacelocarpus neurymenioides revealed a previously characterized antibacterial metabolite, neurymenolide A, as the main allelopathic agent. For allelopathy of lipid-soluble metabolites to be effective, the compounds would need to be deployed on algal surfaces where they could transfer to corals on contact. We used desorption electrospray ionization mass spectrometry (DESI-MS) to visualize and quantify neurymenolide A on the surface of P. neurymenioides, and we found the molecule on all surfaces analyzed, with highest concentrations on basal portions of blades.

Win percentage: a novel measure for assessing the suitability of machine classifiers for biological problems.

BACKGROUND: Selecting an appropriate classifier for a particular biological application poses a difficult problem for researchers and practitioners alike. In particular, choosing a classifier depends heavily on the features selected. For high-throughput biomedical datasets, feature selection is often a preprocessing step that gives an unfair advantage to the classifiers built with the same modeling assumptions. In this paper, we seek classifiers that are suitable to a particular problem independent of feature selection. We propose a novel measure, called "win percentage", for assessing the suitability of machine classifiers to a particular problem. We define win percentage as the probability a classifier will perform better than its peers on a finite random sample of feature sets, giving each classifier equal opportunity to find suitable features. RESULTS: First, we illustrate the difficulty in evaluating classifiers after feature selection. We show that several classifiers can each perform statistically significantly better than their peers given the right feature set among the top 0.001% of all feature sets. We illustrate the utility of win percentage using synthetic data, and evaluate six classifiers in analyzing eight microarray datasets representing three diseases: breast cancer, multiple myeloma, and neuroblastoma. After initially using all Gaussian gene-pairs, we show that precise estimates of win percentage (within 1%) can be achieved using a smaller random sample of all feature pairs. We show that for these data no single classifier can be considered the best without knowing the feature set. Instead, win percentage captures the non-zero probability that each classifier will outperform its peers based on an empirical estimate of performance. CONCLUSIONS: Fundamentally, we illustrate that the selection of the most suitable classifier (i.e., one that is more likely to perform better than its peers) not only depends on the dataset and application but also on the thoroughness of feature selection. In particular, win percentage provides a single measurement that could assist users in eliminating or selecting classifiers for their particular application.

caCORRECT2: Improving the accuracy and reliability of microarray data in the presence of artifacts.

BACKGROUND: In previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECT's web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu. RESULTS: We demonstrate that (1) caCORRECT's artifact-aware normalization avoids the undesirable global data warping that happens when any damaged chips are processed without caCORRECT; (2) When used upstream of RMA, PLIER, or MAS5.0, the data imputation of caCORRECT generally improves the accuracy of microarray gene expression in the presence of artifacts more than using Harshlighting or not using any quality control; (3) Biomarkers selected from artifactual microarray data which have undergone the quality control procedures of caCORRECT are more likely to be reliable, as shown by both spike in and PCR validation experiments. Finally, we present a case study of the use of caCORRECT to reliably identify biomarkers for renal cell carcinoma, yielding two diagnostic biomarkers with potential clinical utility, PRKAB1 and NNMT. CONCLUSIONS: caCORRECT is shown to improve the accuracy of gene expression, and the reproducibility of experimental results in clinical application. This study suggests that caCORRECT will be useful to clean up possible artifacts in new as well as archived microarray data.

An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification.

The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance.

Hypergeometric Similarity Measure for Spatial Analysis in Tissue Imaging Mass Spectrometry.

Tissue imaging mass spectrometry (TIMS) is a data-intensive technique for spatial biochemical analysis. TIMS contributes both molecular and spatial information to tissue analysis. We propose and evaluate a similarity measure, based on the hypergeometric distribution, for comparing m/z images from TIMS datasets, with the goal of identifying m/z values with similar spatial distributions. We compare the formulation and properties of the proposed method with those of other similarity measures, and examine the performance of each measure on synthetic and biological data. This study demonstrates that the proposed hypergeometric similarity measure is effective in identifying similar m/z images, and may be a useful addition to current methods in TIMS data analysis.

Combining two-dimensional diffusion-ordered nuclear magnetic resonance spectroscopy, imaging desorption electrospray ionization mass spectrometry, and direct analysis in real-time mass spectrometry for the integral investigation of counterfeit pharmaceuticals.

During the past decade, there has been a marked increase in the number of reported cases involving counterfeit medicines in developing and developed countries. Particularly, artesunate-based antimalarial drugs have been targeted, because of their high demand and cost. Counterfeit antimalarials can cause death and can contribute to the growing problem of drug resistance, particularly in southeast Asia. In this study, the complementarity of two-dimensional diffusion-ordered (1)H nuclear magnetic resonance spectroscopy (2D DOSY (1)H NMR) with direct analysis in real-time mass spectrometry (DART MS) and desorption electrospray ionization mass spectrometry (DESI MS) was assessed for pharmaceutical forensic purposes. Fourteen different artesunate tablets, representative of what can be purchased from informal sources in southeast Asia, were investigated with these techniques. The expected active pharmaceutical ingredient was detected in only five formulations via both nuclear magnetic resonance (NMR) and mass spectrometry (MS) methods. Common organic excipients such as sucrose, lactose, stearate, dextrin, and starch were also detected. The graphical representation of DOSY (1)H NMR results proved very useful for establishing similarities among groups of samples, enabling counterfeit drug "chemotyping". In addition to bulk- and surface-average analyses, spatially resolved information on the surface composition of counterfeit and genuine antimalarial formulations was obtained using DESI MS that was performed in the imaging mode, which enabled one to visualize the homogeneity of both genuine and counterfeit drug samples. Overall, this study suggests that 2D DOSY (1)H NMR, combined with ambient MS, comprises a powerful suite of instrumental analysis methodologies for the integral characterization of counterfeit antimalarials.

Desorption electrospray ionization mass spectrometry reveals surface-mediated antifungal chemical defense of a tropical seaweed.

Organism surfaces represent signaling sites for attraction of allies and defense against enemies. However, our understanding of these signals has been impeded by methodological limitations that have precluded direct fine-scale evaluation of compounds on native surfaces. Here, we asked whether natural products from the red macroalga Callophycus serratus act in surface-mediated defense against pathogenic microbes. Bromophycolides and callophycoic acids from algal extracts inhibited growth of Lindra thalassiae, a marine fungal pathogen, and represent the largest group of algal antifungal chemical defenses reported to date. Desorption electrospray ionization mass spectrometry (DESI-MS) imaging revealed that surface-associated bromophycolides were found exclusively in association with distinct surface patches at concentrations sufficient for fungal inhibition; DESI-MS also indicated the presence of bromophycolides within internal algal tissue. This is among the first examples of natural product imaging on biological surfaces, suggesting the importance of secondary metabolites in localized ecological interactions, and illustrating the potential of DESI-MS in understanding chemically-mediated biological processes.

Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data.

Imaging mass spectrometry is a method for understanding the molecular distribution in a two-dimensional sample. This method is effective for a wide range of molecules, but generates a large amount of data. It is difficult to extract important information from these large datasets manually and automated methods for discovering important spatial and spectral features are needed. Independent component analysis and non-negative matrix factorization are explained and explored as tools for identifying underlying factors in the data. These techniques are compared and contrasted with principle component analysis, the more standard analysis tool. Independent component analysis and non-negative matrix factorization are found to be more effective analysis methods. A mouse cerebellum dataset is used for testing.

Simple quantification of multiplexed quantum dot staining in clinical tissue samples.

In this paper, we present a simple method for the processing and quantification of multiplexed Quantum Dot (QD) labeled images of clinical cancer tissue samples. QDs provide several features which make them ideal for reliable quantification, including long-term signal stability, high signal-to-noise ratios, as well as narrow emission bandwidths. Deconvolution of QD spectra is accomplished in a batch mode in which unmixing parameters are preserved across samples to allow for quantitative and reproducible comparisons. After unmixing the QD images, we segment each one to exclude acellular regions. We use a simple average intensity to quantify the level of QD staining for each image. We illustrate the viability of this approach by testing it on 28 tissue samples using a tissue microarray. We show that using as few as two QD protein targets (MDM-2, and B-actin), the Renal Cell Carcinoma (RCC) samples are distinguishable from adjacent normal tissue samples. A simple linear discriminant results in 100% classification of 25 RCC samples and 3 normal samples. This suggests that multiplexed QDs can be used to properly diagnose RCC from otherwise healthy tissue. We expect to apply this work to larger panels of more robust QD biomarker targets to aid in clinical decision-making for the diagnosis and prognosis of diseases, such as cancer.