TOFSIMS-P: A Web-Based Platform for Analysis of Large-Scale TOF-SIMS Data

Yun, So Jeong; Park, Ji Won; Choi, Il Ju; Kang, Byeongsoo; Kim, Hark Kyun; Moon, Dae Won; Lee, Tae Geol; Hwang, Daehee

doi:10.1021/ac2016932

Cited by 12 publications

(15 citation statements)

References 43 publications

(48 reference statements)

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“…This complicates or even makes impossible the precise identification of the original molecule that gave rise to the corresponding secondary ion. For example, it was previously shown that an ion with a mass of 127.04 can arise during ionization of at least five different metabolites and may have a different chemical structure (43). To overcome this issue, we used two complementary approaches.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Gularyan

Гулин

Anufrieva

et al. 2020

Molecular & Cellular Proteomics

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Glioblastoma (GBM) is one of the most aggressive human cancers with a median survival of less than two years. A distinguishing pathological feature of GBM is a high degree of inter- and intratumoral heterogeneity. Intertumoral heterogeneity of GBM has been extensively investigated on genomic, methylomic, transcriptomic, proteomic and metabolomics levels, however only a few studies describe intratumoral heterogeneity because of the lack of methods allowing to analyze GBM samples with high spatial resolution. Here, we applied TOF-SIMS (Time-of-flight secondary ion mass spectrometry) for the analysis of single cells and clinical samples such as paraffin and frozen tumor sections obtained from 57 patients. We developed a technique that allows us to simultaneously detect the distribution of proteins and metabolites in glioma tissue with 800 nm spatial resolution. Our results demonstrate that according to TOF-SIMS data glioma samples can be subdivided into clinically relevant groups and distinguished from the normal brain tissue. In addition, TOF-SIMS was able to elucidate differences between morphologically distinct regions of GBM within the same tumor. By staining GBM sections with gold-conjugated antibodies against Caveolin-1 we could visualize border between zones of necrotic and cellular tumor and subdivide glioma samples into groups characterized by different survival of the patients. Finally, we demonstrated that GBM contains cells that are characterized by high levels of Caveolin-1 protein and cholesterol. This population may partly represent a glioma stem cells. Collectively, our results show that the technique described here allows to analyze glioma tissues with a spatial resolution beyond reach of most of other omics approaches and the obtained data may be used to predict clinical behavior of the tumor.

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Section: Discussionmentioning

confidence: 99%

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Gularyan

Гулин

Anufrieva

et al. 2020

Molecular & Cellular Proteomics

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“…Using some analytical methods (eg stochastic optical reconstruction microscopy and cryo‐electron microscopy), there have been concomitant improvements in data analysis and visualisation methods . Time‐of‐flight secondary ion mass spectrometry is moving from small peak lists to those containing hundreds to thousands of peaks, in turn generating extensive data matrices spanning thousands to millions of potential data points . However, the ToF‐SIMS community still largely deals with informatics issues using relatively simple statistical methods like principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

Madiona

Welch

Russell

et al. 2018

Surface & Interface Analysis

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Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF-SIMS data.Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF-SIMS peak lists using up-binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time-of-flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low-density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF-SIMS data.Artificial neural networks, in the form of self-organising maps, are introduced and provide a non-linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions.This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.

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“…[8] Also, signal enhancements were observed in the high mass range when an argon cluster ion beam, rather than a bismuth ion beam, was used. [9][10][11] In this work, we analyzed Ap 3 A and Ap 4 A molecules spiked in HeLa cell lysates by using the ToF-SIMS technique. We will show that ToF-SIMS can be a useful technique to analyze metabolites such as Ap 3 A and Ap 4 A in cell lysates by using bismuth and argon cluster ion beams.…”

Section: Introductionmentioning

confidence: 99%

ToF‐SIMS analysis of diadenosine triphosphate and didadenosine tetraphosphate using bismuth and argon cluster ion beams

Shon

Cho

Lim

et al. 2014

Surface & Interface Analysis

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In the past few decades, research has shown diadenosine polyphosphates (Ap n A) to be a family of intercellular and intracellular signaling molecules that play a key role in biological and pharmacological activities. ToF-SIMS, recently shown to be a promising technique to analyze metabolites in biological samples due to its high sensitivity and specificity, was employed to analyze mixed diadenosine triphosphate (Ap 3 A) and diadenosine tetraphosphate (Ap 4 A) spiked in HeLa cell lysates by using bismuth and argon cluster ion beams. The molecular ion signals of Ap 3 A and Ap 4 A in the cell lysates were stronger when using an argon cluster ion beam than when using a bismuth cluster ion beam, although the detection limits of both molecules were the same. Using a correlation curve to indicate the molecular signal intensities and concentrations, the amount of Ap 3 A and Ap 4 A molecules in HeLa cell lysates could be quantified in the concentration range of 0.1 mM to 10 mM for Ap 3 A, and 5 mM and 20 mM for Ap 4 A. We believe that ToF-SIMS analysis could be a powerful technique to detect important metabolite molecules such as Ap n A for biomedical applications, without the need for a separation process.

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TOFSIMS-P: A Web-Based Platform for Analysis of Large-Scale TOF-SIMS Data

Cited by 12 publications

References 43 publications

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

ToF‐SIMS analysis of diadenosine triphosphate and didadenosine tetraphosphate using bismuth and argon cluster ion beams

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