scholarly journals “NoTaMe”: Workflow for Non-Targeted LC-MS Metabolic Profiling

2020 ◽  
Author(s):  
Marietta Kokla ◽  
Anton Klåvus ◽  
Stefania Noerman ◽  
Ville M. Koistinen ◽  
Marjo Tuomainen ◽  
...  
Keyword(s):  
Metabolic Profiling ◽  
Mass Spectrometry Analysis ◽  
Quality Data ◽  
Metabolomics Data ◽  
High Quality Data ◽  
Spectrometry Analysis ◽  
The Third ◽  
Nutritional Metabolomics ◽  
Study Designs ◽  
Metabolomics Analysis

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

scholarly journals “Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 135 ◽  
Author(s):  
Anton Klåvus ◽  
Marietta Kokla ◽  
Stefania Noerman ◽  
Ville M. Koistinen ◽  
Marjo Tuomainen ◽  
...  
Keyword(s):  
Metabolic Profiling ◽  
Mass Spectrometry Analysis ◽  
Quality Data ◽  
Liquid Chromatography Mass Spectrometry ◽  
Metabolomics Data ◽  
High Quality Data ◽  
Spectrometry Analysis ◽  
The Third ◽  
Nutritional Metabolomics ◽  
Study Designs

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

scholarly journals Current practice in plankton metabarcoding: optimization and error management

2019 ◽  
Vol 41 (5) ◽  
pp. 571-582 ◽  
Author(s):  
Luciana F Santoferrara
Keyword(s):  
High Throughput Sequencing ◽  
Current Practice ◽  
Research Question ◽  
Quality Data ◽  
Error Management ◽  
False Negatives ◽  
High Quality Data ◽  
Alpha And Beta Diversity ◽  
Downstream Analysis ◽  
Study Designs

Abstract High-throughput sequencing of a targeted genetic marker is being widely used to analyze biodiversity across taxa and environments. Amid a multitude of exciting findings, scientists have also identified and addressed technical and biological limitations. Improved study designs and alternative sampling, lab and bioinformatic procedures have progressively enhanced data quality, but some problems persist. This article provides a framework to recognize and bypass the main types of errors that can affect metabarcoding data: false negatives, false positives, artifactual variants, disproportions and incomplete or incorrect taxonomic identifications. It is crucial to discern potential error impacts on different ecological parameters (e.g. taxon distribution, community structure, alpha and beta-diversity), as error management implies compromises and is thus directed by the research question. Synthesis of multiple plankton metabarcoding evaluations (mock sample sequencing or microscope comparisons) shows that high-quality data for qualitative and some semiquantitative goals can be achieved by implementing three checkpoints: first, rigorous protocol optimization; second, error minimization; and third, downstream analysis that considers potentially remaining biases. Conclusions inform us about the reliability of metabarcoding for plankton studies and, because plankton provides unique chances to compare genotypes and phenotypes, the robustness of this method in general.

Multi-platform mass spectrometry analysis of the CSF and plasma metabolomes of rigorously matched amyotrophic lateral sclerosis, Parkinson's disease and control subjects

2016 ◽  
Vol 12 (4) ◽  
pp. 1287-1298 ◽  
Author(s):  
Anna Wuolikainen ◽  
Pär Jonsson ◽  
Maria Ahnlund ◽  
Henrik Antti ◽  
Stefan L. Marklund ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Amyotrophic Lateral Sclerosis ◽  
Study Design ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Control Subjects ◽  
And Control ◽  
Lateral Sclerosis ◽  
Metabolomics Analysis

Schematic view of the study design and the mass spectrometry platforms used for metabolomics analysis.

scholarly journals Proteomics in Cancer Biomarkers Discovery: Challenges and Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Reem M. Sallam
Keyword(s):  
Relevant Literature ◽  
Quality Data ◽  
Evidence Based ◽  
Optimal Conditions ◽  
Critical Factors ◽  
Research Groups ◽  
High Quality Data ◽  
Good Science ◽  
Cancer Pathogenesis ◽  
Study Designs

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.

scholarly journals Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides

2021 ◽  
Author(s):  
Suzi Hong ◽  
Gajender Aleti ◽  
Jordan N. Kohn ◽  
Emily A. Troyer ◽  
Kelly Weldon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Depressive Symptomatology ◽  
Aromatic Amino Acids ◽  
Oral Microbiome ◽  
Mass Spectrometry Analysis ◽  
Metabolomics Data ◽  
16S Sequencing ◽  
Spectrometry Analysis ◽  
Oral Microbes ◽  
Host Microbe Interactions

Abstract BackgroundDepression and obesity, both of which are highly prevalent and inflammation underlies, often co-occur. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how microbiome alterations contribute to underlying pathologic processes remains unclear. Metabolomic investigations to uncover microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. MethodsUsing 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we determined key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity. ResultsGram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was highly predictive of depressive symptomatology-obesity co-occurrences than obesity and depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among host phenotypes. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin. ConclusionsTogether, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

scholarly journals Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides

2021 ◽  
Author(s):  
Gajender Aleti ◽  
Jordan N Kohn ◽  
Emily A Troyer ◽  
Kelly Weldon ◽  
Shi Huang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Depressive Symptomatology ◽  
Aromatic Amino Acids ◽  
Oral Microbiome ◽  
Mass Spectrometry Analysis ◽  
Metabolomics Data ◽  
16S Sequencing ◽  
Spectrometry Analysis ◽  
Oral Microbes ◽  
Host Microbe Interactions

Depression and obesity, both of which are highly prevalent and inflammation underlies, often co-occur. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how microbiome alterations contribute to underlying pathologic processes remains unclear. Metabolomic investigations to uncover microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we determined key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity. Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was highly predictive of depressive symptomatology-obesity co-occurrences than obesity and depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among host phenotypes. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin. Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

scholarly journals MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 492
Author(s):  
Luca Nicolotti ◽  
Jeremy Hack ◽  
Markus Herderich ◽  
Natoiya Lloyd
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Programming Languages ◽  
Detailed Comparison ◽  
Mass Spectrometry Data ◽  
Mass Spectrometry Analysis ◽  
Molecular Feature ◽  
Metabolomics Data ◽  
Spectrometry Analysis ◽  
Mass Spectrometry Technology

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

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