Summary of study ST001857

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 PR001171. The data can be accessed directly via it's Project DOI: 10.21228/M8BM4Q 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.

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Study IDST001857
Study TitlePeak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks (Prostate tissue)
Study SummaryThe human prostate tissue sample was analyzed by 2D MALDI FT ICR MSI. For detailed information we refer to the msiPL manuscript by Abdelmoula et al.
Institute
Brigham and Women’s Hospital
Last NameAbdelmoula
First NameWalid
Address60 Fenwood RD, Boston, MA
Emailwahassan@bwh.harvard.edu
Phone8572149765
Submit Date2021-06-20
Raw Data AvailableYes
Raw Data File Type(s)h5
Analysis Type DetailMALDI
Release Date2021-07-18
Release Version1
Walid Abdelmoula Walid Abdelmoula
https://dx.doi.org/10.21228/M8BM4Q
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:


Project:

Project ID:PR001171
Project DOI:doi: 10.21228/M8BM4Q
Project Title:msiPL: Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks
Project Type:Deep Learning for 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 2020.08.13.250142). The below is taken from the abstract: Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma.
Institute:Brigham and Women's Hospital
Department:Neurosurgery
Laboratory:Surgical Molecular Imaging Laboratory
Last Name:Abdelmoula
First Name:Walid
Address:60 Fenwood RD, Boston, Massachusetts, 02115, USA
Email:wahassan@bwh.harvard.edu
Phone:617-525-7374

Subject:

Subject ID:SU001934
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Tissue Type
SA174242P_1900Human Prostate
SA174243Dataset_S4PDX GBM - mouse brain tumor section
SA174244Dataset_S2PDX GBM - mouse brain tumor section
SA174245Dataset_S1PDX GBM - mouse brain tumor section
SA174246Dataset_S3PDX GBM - mouse brain tumor section
Showing results 1 to 5 of 5

Collection:

Collection ID:CO001927
Collection Summary:As stated in the msiPL paper: "Briefly, 12 µm thickness prostate sample diagnosed with a Gleason score of (3+4)=7 were mounted on a microscopy glass slide and coated with CHCA (5 mg/mL in 70/30 methanol/water with 0.1% trifluoroacetic acid V/V) using an automated sprayer (TM-Sprayer, HTX Imaging, Carrboro, NC). The analysis of the samples was performed on a 9.4 Tesla SolariX XR FT ICR mass spectrometer (Bruker Daltonics, Billerica, MA) using the MALDI source in positive ion mode in the mass range between 250-1000 m/z, with a spatial resolution of 120 µm."
Sample Type:Prostate

Treatment:

Treatment ID:TR001946
Treatment Summary:N/A

Sample Preparation:

Sampleprep ID:SP001940
Sampleprep Summary:As stated in the msiPL paper: For the Prostate tissue: "Briefly, 12 µm thickness prostate sample diagnosed with a Gleason score of (3+4)=7 were mounted on a microscopy glass slide and coated with CHCA (5 mg/mL in 70/30 methanol/water with 0.1% trifluoroacetic acid V/V) using an automated sprayer (TM-Sprayer, HTX Imaging, Carrboro, NC). The analysis of the samples was performed on a 9.4 Tesla SolariX XR FT ICR mass spectrometer (Bruker Daltonics, Billerica, MA) using the MALDI source in positive ion mode in the mass range between 250-1000 m/z, with a spatial resolution of 120 µm." For the PDX GBM mouse brain dataset: "The intracranial tumor belonging to a PDX model of GBM12 (PDX National Resource, Mayo Clinic), was analyzed by MALDI FT ICR MSI using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA), using continuous accumulation of selected ions in the mass range between 380-620 m/z. The indium tin oxide (ITO)-coated slide with 12 µm thickness tissue sections , was coated with DHB (160 mg/mL in a 70/30 v/v solution of methanol/0.2% TFA), according to Randall et al2. The 3D MSI dataset was collected from 4 tissue sections with an inter-slice distance of 160 µm. Internal online calibration was performed using heme m/z 616.1776 during data acquisition."

Combined analysis:

Analysis ID AN003010
Analysis type MS
Chromatography type Other
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:CH002231
Instrument Name:none
Column Name:none
Chromatography Type:Other

MS:

MS ID:MS002799
Analysis ID:AN003010
Instrument Name:Bruker Solarix FT-ICR-MS
Instrument Type:FT-ICR
MS Type:MALDI
MS Comments:Bruker software
Ion Mode:POSITIVE
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