Mar 31, 2020

Public workspaceData Processing and Preparation of MALDI IMS data

DOI
dx.doi.org/10.17504/protocols.io.bed3ja8n
  • 1Vanderbilt University
  • VU Biomolecular Multimodal Imaging Center / Spraggins Research Group
  • Human BioMolecular Atlas Program (HuBMAP) Method Development Community
Jamie Allen
Icon indicating open access to content
QR code linking to this content
DOI: dx.doi.org/10.17504/protocols.io.bed3ja8n
Protocol Citation: Nathan Heath Patterson, Elizabeth Neumann, Jamie Allen, Danielle Gutierrez, Jeff Spraggins 2020. Data Processing and Preparation of MALDI IMS data. protocols.io https://dx.doi.org/10.17504/protocols.io.bed3ja8n
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 30, 2020
Last Modified: October 18, 2023
Protocol Integer ID: 34971
Keywords: HuBMAP, BIOMIC, Vanderbilt, Data Processing, MALDI IMS
Abstract
Scope:
How to process a mass spectrometry imaging data into a final imzML file.
For each spectrum in the data set, apply total ion current normalization by dividing the intensity values in the spectrum by the sum of the spectrum's intensity values.
Generate a mean spectrum for the dataset by taking the mean of each mass bin of the dataset.
Internally recalibrate the mean spectrum using well-known, spectrally ubiqitious lipid species, retaining the calibration equation.
Extract peak intensity data for every pixel using the provided identified mass list generated here (Lipid Annotation of MALDI IMS Datasets) by inverting the calibration coefficients to get the mass values in the original calibration and pulling intensity values from the Bruker .sqlite file that match these mass values.
Process the extracted peak list into a data table of columns: x, y, m/z 1, m/z 2, m/z 3... where 1, 2, and 3 are placeholders for the identified lipid species.
Read this table into R and create a Cardinal MSImagingExperiment object from the pixel coordinates and peak intensity values.
https://cardinalmsi.org
Use Cardinal's writeImzML() function to write the MSImagingExperiment object to an imzML file.