Summary of Study ST002045

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001292. The data can be accessed directly via it's Project DOI: 10.21228/M8Q70T This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Show all samples  
Download mwTab file (text)   |  Download mwTab file(JSON)   |  Download data files (Contains raw data)
Study IDST002045
Study TitlemassNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation
Study SummaryThe patient-derived xenograft (PDX) mouse brain tumor model of glioblastoma (GBM) samples were analyzed by 2D MALDI FT ICR MSI.
Institute
Brigham and Women's Hospital
DepartmentDepartment of Neurosurgery
LaboratoryNathalie Y.R. Agar
Last NameAbdelmoula
First NameWalid
Address60 Fenwood RD, Boston, MA
Emailwahassan@bwh.harvard.edu
Phone8572149765
Submit Date2021-12-06
Raw Data AvailableYes
Raw Data File Type(s)h5
Analysis Type DetailMALDI-MS
Release Date2022-01-04
Release Version1
Walid Abdelmoula Walid Abdelmoula
https://dx.doi.org/10.21228/M8Q70T
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001292
Project DOI:doi: 10.21228/M8Q70T
Project Title:massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation
Project Type:integrated processing and classification of MSI data analysis
Project Summary:The attached MSI datasets of GBM and prostate cancer tissues were analyzed in the manuscript by Abdelmoula et al. (bioRxiv 2021.05.06.442938). The below is taken from the abstract: Motivation: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high dimensionality, and spectral non-linearity. Preprocessing, including peak picking, has been used to reduce raw data complexity, however peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. Results: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity, and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model’s performance was assessed using cross-validation, and the results demonstrate higher accuracy and a 174-fold gain in speed compared to the established classical machine learning method, support vector machine.
Institute:Brigham and Women's Hospital
Department:Department of Neurosurgery
Laboratory:Nathalie Y.R. Agar
Last Name:Abdelmoula
First Name:Walid
Address:60 Fenwood RD, Boston, MA
Email:wahassan@bwh.harvard.edu
Phone:8572149765

Subject:

Subject ID:SU002127
Subject Type:Mammal
Subject Species:Mus musculus
Taxonomy ID:10090

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id local_sample_id sample_id
SA1923291PDX GBM - mouse brain tumor sectionDataset1
SA1923302PDX GBM - mouse brain tumor sectionDataset2
SA1923313PDX GBM - mouse brain tumor sectionDataset3
SA1923324PDX GBM - mouse brain tumor sectionDataset4
SA1923335PDX GBM - mouse brain tumor sectionDataset5
SA1923346PDX GBM - mouse brain tumor sectionDataset6
SA1923357PDX GBM - mouse brain tumor sectionDataset7
SA1923368PDX GBM - mouse brain tumor sectionDataset8
Showing results 1 to 8 of 8

Collection:

Collection ID:CO002120
Collection Summary:As stated in the massNetpaper: Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis.
Sample Type:PDX GBM - mouse brain tumor section

Treatment:

Treatment ID:TR002139
Treatment Summary:N/A

Sample Preparation:

Sampleprep ID:SP002133
Sampleprep Summary:As stated in the massNet paper Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis.

Combined analysis:

Analysis ID AN003329
Analysis type MS
Chromatography type None (Direct infusion)
Chromatography system none
Column none
MS Type MALDI
MS instrument type FT-ICR
MS instrument name Bruker Solarix FT-ICR-MS
Ion Mode POSITIVE
Units Da

Chromatography:

Chromatography ID:CH002466
Instrument Name:none
Column Name:none
Chromatography Type:None (Direct infusion)

MS:

MS ID:MS003099
Analysis ID:AN003329
Instrument Name:Bruker Solarix FT-ICR-MS
Instrument Type:FT-ICR
MS Type:MALDI
MS Comments:Bruker software
Ion Mode:POSITIVE
  logo