iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.

METHODS

To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.

RESULTS

In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.

CONCLUSIONS

We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

K. M, Karthikkeyan G, Prasad TSK, Modi PK, Datta TK, Ramesha K, Manimaran A, Jeyakumar S. Deep Metabolomic Profiling Reveals Alterations in Fatty Acid Synthesis and Ketone Body Degradations in Spermatozoa and Seminal Plasma of Astheno-Oligozoospermic Bulls

Male fertility is extremely important in dairy animals because semen from a single bull is used to inseminate several thousand females. Asthenozoospermia (reduced sperm motility) and oligozoospermia (reduced sperm concentration) are the two important reasons cited for idiopathic infertility in crossbred bulls; however, the etiology remains elusive. In this study, using a non-targeted liquid chromatography with tandem mass spectrometry-based approach, we carried out a deep metabolomic analysis of spermatozoa and seminal plasma derived from normozoospermic and astheno-oligozoospermic bulls. Using bioinformatics tools, alterations in metabolites and metabolic pathways between normozoospermia and astheno-oligozoospermia were elucidated. A total of 299 and 167 metabolites in spermatozoa and 183 and 147 metabolites in seminal plasma were detected in astheno-oligozoospermic and normozoospermic bulls, respectively. Among the mapped metabolites, 75 sperm metabolites were common to both the groups, whereas 166 and 50 sperm metabolites were unique to astheno-oligozoospermic and normozoospermic bulls, respectively. Similarly, 86 metabolites were common to both the groups, whereas 45 and 37 seminal plasma metabolites were unique to astheno-oligozoospermic and normozoospermic bulls, respectively. Among the differentially expressed metabolites, 62 sperm metabolites and 56 seminal plasma metabolites were significantly dysregulated in astheno-oligozoospermic bulls. In spermatozoa, selenocysteine, deoxyuridine triphosphate, and nitroprusside showed significant enrichment in astheno-oligozoospermic bulls. In seminal plasma, malonic acid, 5-diphosphoinositol pentakisphosphate, D-cysteine, and nicotinamide adenine dinucleotide phosphate were significantly upregulated, whereas tetradecanoyl-CoA was significantly downregulated in the astheno-oligozoospermia. Spermatozoa from astheno-oligozoospermic bulls showed alterations in the metabolism of fatty acid and fatty acid elongation in mitochondria pathways, whereas seminal plasma from astheno-oligozoospermic bulls showed alterations in synthesis and degradation of ketone bodies, pyruvate metabolism, and inositol phosphate metabolism pathways. The present study revealed vital information related to semen metabolomic differences between astheno-oligozoospermic and normospermic crossbred breeding bulls. It is inferred that fatty acid synthesis and ketone body degradations are altered in the spermatozoa and seminal plasma of astheno-oligozoospermic crossbred bulls. These results open up new avenues for further research, and current findings can be applied for the modulation of identified pathways to restore sperm motility and concentration in astheno-oligozoospermic bulls.

Deciphering metabolomic alterations in seminal plasma of crossbred (Bos taurus X Bos indicus) bulls through comparative deep metabolomic analysis

The incidence of sub-fertility is higher in crossbred bulls compared to zebu bulls. In the present study, we analysed the metabolomic profile of seminal plasma from crossbred and zebu bulls and uncovered differentially expressed metabolites between these two breeds. Using a high-throughput LC-MS/MS-based approach, we identified 990 and 1,002 metabolites in crossbred and zebu bull seminal plasma respectively. After excluding the exogenous metabolites, we found that 50 and 68 putative metabolites were unique to crossbred and zebu bull seminal plasma, respectively, whilst 87 metabolites were common to both. After data normalisation, 63 metabolites were found to be dysregulated between crossbred and zebu bull seminal plasma. Observed pathways included Linoleic acid metabolism (observed metabolite was phosphatidylcholine) in crossbred bull seminal plasma whereas inositol phosphate metabolism (observed metabolites were phosphatidylinositol-3,4,5-trisphosphate/inositol 1,3,4,5,6-pentakisphosphate/myo-inositol hexakisphosphate) was observed in zebu bull seminal plasma. Abundance of Tetradecanoyl-CoA was significantly higher, whilst abundance of Taurine was significantly lower in crossbred bull seminal plasma. In conclusion, the present study established the seminal plasma metabolomic profile in crossbred and zebu bulls and suggest that increased lipid peroxidation coupled with low concentrations of antioxidants in seminal plasma might be associated with high incidence of sub-fertility in crossbred bulls.

A Practical Guide to Metabolomics Software Development

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

An approach for feature selection with data modelling in LC-MS metabolomics

The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation via Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Abiotic and biotic stresses are the main reasons of substantial crop yield losses worldwide. Research devoted to reveal mechanisms of plant reactions during their interactions with the environment are conducted on the level of genome, transcriptome, proteome, and metabolome. Data obtained during these studies would permit to define biochemical and physiological mechanisms of plant resistance or susceptibility to affecting factors/stresses. Metabolomics based on mass spectrometric techniques is an important part of research conducted in the direction of breeding new varieties of crop plants tolerant to the affecting stresses and possessing good agronomical features. Studies of this kind are carried out on model, crop and resurrection plants. Metabolites profiling yields large sets of data and due to this fact numerous advanced statistical and bioinformatic methods permitting to obtain qualitative and quantitative evaluation of the results have been developed. Moreover, advanced integration of metabolomics data with these obtained on other omics levels: genome, transcriptome and proteome should be carried out. Such a holistic approach would bring us closer to understanding biochemical and physiological processes of the cell and whole plant interacting with the environment and further apply these observations in successful breeding of stress tolerant or resistant crop plants.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2 .

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.