IonFlow: a galaxy tool for the analysis of ionomics data sets

Abstract Introduction Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools. Objective Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data. Methods IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis. Results and Conclusion The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.

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Elemental analysis of coffee: a comparison of ICP-MS and AAS methods

Czech Journal of Food Sciences ◽

10.17221/399/2013-cjfs ◽

2014 ◽

Vol 32 (No. 4) ◽

pp. 354-359 ◽

Cited By ~ 10

Author(s):

M. Jarošová ◽

D. Milde ◽

M. Kuba

Keyword(s):

Inductively Coupled Plasma ◽

Microwave Digestion ◽

Absorption Spectrometry ◽

Mineral Nutrients ◽

Multivariate Statistical Methods ◽

Multivariate Statistical ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Microwave Digestion System

We determined the mineral nutrients and toxic elements (Ca, Cu, Fe, Mg, Zn, Cd, Cr, Mn, Ni, and Pb) in five types of coffee by atomic absorption spectrometry and inductively coupled plasma mass spectrometry. The decomposition of the samples took place in a microwave digestion system with HNO3 and H2O2 reagents. Partial validation of the method was performed by using the certified reference material (NCS ZC 73014). Univariate and multivariate statistical methods were used to compare both the coffee samples and the techniques used. No significant differences were found between two used methods. Significant differences occurred between the coffee samples but only the application of multivariate statistics helps to distinguish among samples from different locations.

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Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma–Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification

Applied Spectroscopy ◽

10.1177/0003702817734618 ◽

2017 ◽

Vol 72 (2) ◽

pp. 241-250 ◽

Cited By ~ 8

Author(s):

Anna Balbekova ◽

Hans Lohninger ◽

Geralda A.F. van Tilborg ◽

Rick M. Dijkhuizen ◽

Maximilian Bonta ◽

...

Keyword(s):

Mass Spectrometry ◽

Fourier Transform ◽

Inductively Coupled Plasma ◽

Brain Regions ◽

Hyperspectral Data ◽

Data Sets ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Ft Ir

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats’ brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

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ICP–MS Analysis of Multi-Elemental Profile of Greek Wines and Their Classification According to Variety, Area and Year of Production

Separations ◽

10.3390/separations8080119 ◽

2021 ◽

Vol 8 (8) ◽

pp. 119

Author(s):

Konstantina Pasvanka ◽

Marios Kostakis ◽

Maria Tarapoulouzi ◽

Pavlos Nisianakis ◽

Nikolaos S. Thomaidis ◽

...

Keyword(s):

Inductively Coupled Plasma ◽

Mineral Content ◽

Statistical Investigation ◽

Multivariate Statistical ◽

Annual Fluctuation ◽

Inductively Coupled ◽

Elemental Profile ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

And Cluster Analysis

Major, minor and trace elements in wines from Greece were determined by inductively coupled plasma–mass spectrometry (ICP–MS). The concentrations of 44 elements (Na, Mg, P, K, Ca, Cu, Co, Cr, Zn, Sn, Fe, Mn, Li, Be, B, V, Sr, Ba, Al, Ag, Ni, As, Sn, Hg, Pb, Sb, Cd, Ti, Ga, Zr, Nb, Pd, Te, La, Sm, Ho, Tm, Yb, W, Os, Au, Tl, Th, U) in 90 white and red wines from six different regions in Greece for two consecutive vinification years, 2017 and 2018, were determined. Results for the elements aforementioned were evaluated by multivariate statistical methods, such as discriminant analysis and cluster analysis, and the wines were discriminated according to wine variety and geographical origin. Due to the specific choice of the analytes for multivariate statistical investigation, a prediction rate by cross-validation of 98% could be achieved. The aim of this study was not only to reveal specific relationships between the wine samples or between the chemical variables in order to classify the wines from different regions and varieties according to their elemental profile (wine authentication), but also to observe the annual fluctuation in the mineral content of the studied wine samples.

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Rapid Identification of Geographical Origin of Commercial Soybean Marketed in Vietnam by ICP-MS

Journal of Analytical Methods in Chemistry ◽

10.1155/2021/5583860 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Trung Nguyen-Quang ◽

Minh Bui-Quang ◽

Minh Truong-Ngoc

Keyword(s):

Inductively Coupled Plasma ◽

Geographical Origin ◽

Initial Step ◽

Rapid Identification ◽

Composition Analysis ◽

Universal Method ◽

Multivariate Statistical ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry

Inductively coupled plasma mass spectrometry (ICP-MS) analytical method was used to determine the content of 40 elements in 38 soybean samples (Glycine Max) from 4 countries. Multivariate statistical methods, such as principal components analysis (PCA), were performed to analyze the obtained data to establish the provenance of the soybeans. Although soybean is widely marketed in many countries, no universal method is used to discriminate the origin of these cereals. Our study introduced the initial step to the identification of the geographical origin of commercial soybean marketed in Vietnam. The analysis pointed out that there are significant differences in the mean of 33 of the 40 analyzed elements among 4 countries’ soybean samples, namely, 11B, 27Al, 44Ca, 45Sc, 47Ti, 55Mn, 56Fe, 59Co, 60Ni, 63Cu, 66Zn, 69Ga, 75As, 78Se, 85Rb, 88Sr, 89Y, 90Zr, 93Nb, 95Mo, 103Rh, 137Ba, 163Dy, 165Ho, 175Lu, 178Hf, 181Ta, 182W, 185Re, 197Au, 202Hg, 205Tl, and 208Pb. The PCA analysis showed that the soybean samples can be classified correctly according to their original locations. This research can be used as a prerequisite for future studies of using the combination of elemental composition analysis with statistical classification methods for an accurate provenance establishment of soybean, which determined a variation of key markers for the original discrimination of soybean.

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Multielement Analysis of Pakchoi (Brassica rapa L. ssp. chinensis) by ICP-MS and Their Classification according to Different Small Geographical Origins

Journal of Analytical Methods in Chemistry ◽

10.1155/2021/8860852 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Trung Nguyen-Quang ◽

Giang Do-Hoang ◽

Minh Truong-Ngoc

Keyword(s):

Brassica Rapa ◽

Inductively Coupled Plasma ◽

Statistical Interpretation ◽

Universal Method ◽

Multivariate Statistical ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Food Market ◽

First Time

Statistical interpretation of the concentrations of 42 elements, determined using solution-based inductively coupled plasma mass spectrometry (ICP-MS) analysis and multivariate statistical methods, such as principal components analysis (PCA), was used to establish the provenance of pakchoi (Brassica rapa L. ssp. chinensis) from 6 areas in Ha Noi, Vietnam. Although pakchoi is widely cultivated and manufactured, no universal method is used to discriminate the origin of this vegetable. Our study introduced for the first time a method to classify pakchoi in small geographical areas. 42 metallic elements of pakchoi were detected by ICP-MS, which were further analyzed using multivariate statistical analysis to perform clusters based on the geographical locations. Eleven elements, i.e., 28Si; 56Fe; 59Co; 63Cu; 69Ga; 75As; 85Rb; 93Nb; 107Ag; 118Sn, and 137Ba, were identified as discriminators to distinguish pakchoi from those areas. Results from this study presented a new method to discriminant the geographical origins of pakchoi, which could apply to other types of vegetables on the food market.

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Chemometric approach to find relationships between physiological elements and elements causing toxic effects in herb roots by ICP-MS

Scientific Reports ◽

10.1038/s41598-021-00019-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Adam Sajnóg ◽

Elwira Koko ◽

Dariusz Kayzer ◽

Danuta Barałkiewicz

Keyword(s):

Multiple Regression ◽

Inductively Coupled Plasma ◽

Toxic Effects ◽

Elemental Content ◽

Multivariate Statistical ◽

Effect Analysis ◽

Inductively Coupled ◽

Icp Ms ◽

Canonical Variates ◽

Plasma Mass Spectrometry

AbstractIn this paper 13 elements, both physiological and causing toxic effects, were determined by inductively coupled plasma mass spectrometry in roots of 26 species of herbs used in Traditional Chinese Medicine. The herbs were purchased from online shop in two batches 1 year apart to verify the variability of elemental content in time. The multivariate statistical methods—multiple regression, canonical variates and interaction effect analysis—were applied to interpret the data and to show the relationships between elements and two batches of herb roots. The maximum permissible concentration of Cd (0.3 mg kg−1) was exceeded in 7 herb roots which makes 13% of all specimens. The multiple regression analysis revealed the significant relationships between elements: Mg with Sr; V with Pb, As and Ba; Mn with Pb; Fe with As and Ba; Co with Ni and Sr, Cu with Pb, Cd and As; Zn with Pb, Cd, As and Ba. The canonical variates analysis showed that the statistical inference should not be based solely on the type of herb or number of batch because of the underlying interaction effects between those two variables that may be a source of variability of the content of determined elements.

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Interlaboratory comparison of heavy metal testing in animal diagnostic specimens and feed using inductively coupled plasma–mass spectrometry

Journal of Veterinary Diagnostic Investigation ◽

10.1177/1040638720903115 ◽

2020 ◽

Vol 32 (2) ◽

pp. 291-300 ◽

Cited By ~ 2

Author(s):

Steven McGeehan ◽

Timothy Baszler ◽

Cynthia Gaskill ◽

Joseph Johnson ◽

Lori Smith ◽

...

Keyword(s):

Heavy Metals ◽

Inductively Coupled Plasma ◽

Interlaboratory Comparison ◽

Method Comparison ◽

Data Sets ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Reproducible Method ◽

Instrument Comparison

We compared inductively coupled plasma–mass spectrometry (ICP-MS) test results for the analysis of heavy metals (As, Ba, Cd, Hg, Pb, and Se) in pet foods and routine veterinary diagnostic specimens using intralaboratory and interlaboratory comparisons. Four laboratories, 1 principal laboratory and 3 collaborating laboratories, conducted instrument comparison (limit of detection [LOD], limit of quantification [LOQ], and linear dynamic range [LDR] on 24 data sets), in-house method comparison (accuracy and precision on 120 data sets), and interlaboratory comparison (reproducibility on 528 data sets using Horwitz equation analysis). Matrices tested included 2 types of pet food jerky treats (chicken and sweet potato), bovine blood, and bovine liver and kidney. The instrument comparison study confirmed that ICP-MS provided the sensitivity necessary for the analysis of all heavy metals tested at concentrations below the level of concern for routine diagnostic testing. The “in-house” method comparison samples, spiked at low (0.04 µg/g), medium (0.4 µg/g), and high (8.0 µg/g; note: the high validation level spike for mercury was 2 µg/g) concentration levels, indicated that ICP-MS can meet U.S. FDA acceptance criteria for both accuracy (90–105% recovery) and precision (< 6% coefficient of variation). The interlaboratory comparison studies showed that ICP-MS is a reproducible method for the analysis of heavy metals (HorRat value of 0.5–2.0) except for mercury in one laboratory, which used a different sample preparation method (open block rather than microwave digestion). Overall, our study showed that ICP-MS is a reproducible method for the analysis of heavy metals in spite of minor differences in methodology.

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Quantification of hepatic liver iron overload with laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS)

10.1055/s-0038-1677110 ◽

2019 ◽

Author(s):

R Weiskirchen ◽

P Kim ◽

S Weiskirchen ◽

R Uerlings ◽

A Kueppers ◽

...

Keyword(s):

Mass Spectrometry ◽

Laser Ablation ◽

Iron Overload ◽

Inductively Coupled Plasma ◽

Liver Iron ◽

Inductively Coupled ◽

Icp Ms ◽

Liver Iron Overload ◽

Plasma Mass Spectrometry

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Single Particle Inductively Coupled Plasma Mass Spectrometry: Investigating Nonlinear Response Observed in Pulse Counting Mode and Extending the Linear Dynamic Range by Compensating for Dead Time Related Count Losses on a Microsecond Timescale

10.26434/chemrxiv.9911627 ◽

2019 ◽

Author(s):

Ingo Strenge ◽

Carsten Engelhard

Keyword(s):

Mass Spectrometry ◽

Inductively Coupled Plasma ◽

Nonlinear Response ◽

Dynamic Range ◽

Dead Time ◽

Inorganic Nanoparticles ◽

Linear Dynamic Range ◽

Inductively Coupled ◽

Icp Ms ◽

Plasma Mass Spectrometry

The article demonstrates the importance of using a suitable approach to compensate for dead time relate count losses (a certain measurement artefact) whenever short, but potentially strong transient signals are to be analysed using inductively coupled plasma mass spectrometry (ICP-MS). Findings strongly support the theory that inadequate time resolution, and therefore insufficient compensation for these count losses, is one of the main reasons for size underestimation observed when analysing inorganic nanoparticles using ICP-MS, a topic still controversially discussed.

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