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MassBase: A large-scaled depository of mass spectrometry datasets for metabolome analysis.

ABSTRACT: Depository of low-molecular-weight compounds or metabolites detected in various organisms in a non-targeted manner is indispensable for metabolomics research. Due to the diverse chemical compounds, various mass spectrometry (MS) setups with state-of-the-art technologies have been used. Over the past two decades, we have analyzed various biological samples by using gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, or capillary electrophoresis-mass spectrometry, and archived the datasets in the depository MassBase (http://webs2.kazusa.or.jp/massbase/). As the format of MS datasets depends on the MS setup used, we converted each raw binary dataset of the mass chromatogram to text file format, and thereafter, information of the chromatograph peak was extracted in the text file from the converted file. In total, the depository comprises 46,493 datasets, of which 38,750 belong to the plant species and 7,743 are authentic or mixed chemicals as well as other sources (microorganisms, animals, and foods), as on August 1, 2020. All files in the depository can be downloaded in bulk from the website. Mass chromatograms of 90 plant species obtained by LC-Fourier transform ion cyclotron resonance MS or Orbitrap MS, which detect the ionized molecules with high accuracy allowing speculation of chemical compositions, were converted to text files by the software PowerGet, and the chemical annotation of each peak was added. The processed datasets were deposited in the annotation database KomicMarket2 (http://webs2.kazusa.or.jp/km2/). The archives provide fundamental resources for comparative metabolomics and functional genomics, which may result in deeper understanding of living organisms.

SUBMITTER: Ara T

PROVIDER: S-EPMC8215474 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

Dataset Information

MassBase: A large-scaled depository of mass spectrometry datasets for metabolome analysis.

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