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MAUI (MBI Analysis User Interface)-An image processing pipeline for Multiplexed Mass Based Imaging.

Alex Baranski1, Idan Milo2, Shirley Greenbaum1

  • 1School of Medicine, Department of Pathology, Stanford University, Stanford, California, United States of America.

Plos Computational Biology
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

Mass Based Imaging (MBI) technologies like MIBI-TOF and IMC offer deep cellular insights but produce artifacts. We developed the MBI Analysis User Interface (MAUI) software to streamline artifact removal and accelerate data pre-processing for MBI data.

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Proteomics

Background:

  • Mass Based Imaging (MBI) technologies, including Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC), enable high-plex protein expression analysis in situ.
  • These advanced techniques allow simultaneous measurement of over 40 proteins, providing critical insights into cellular phenotypes and tissue organization.
  • However, imaging artifacts arising from sample preparation, assay procedures, or instrumentation can significantly complicate downstream data analysis.

Purpose of the Study:

  • To introduce the MBI Analysis User Interface (MAUI), a novel software tool designed for the pre-processing of MBI data.
  • To provide a user-friendly graphical interface that simplifies and accelerates the correction of common imaging artifacts.
  • To enhance the efficiency and accuracy of MBI data analysis pipelines.

Main Methods:

  • Development of a series of graphical user interfaces (GUIs) integrated into the MAUI software.
  • Implementation of algorithms for the removal of specific imaging artifacts, including channel crosstalk, noise, and antibody aggregates.
  • Integration of real-time and interactive parameter tuning capabilities for efficient workflow management across multiple images.

Main Results:

  • MAUI effectively streamlines the pre-processing steps required for MBI data, including artifact correction.
  • The software accelerates data processing by enabling interactive and real-time adjustment of parameters.
  • MAUI facilitates the removal of channel crosstalk, noise, and antibody aggregates, improving data quality.

Conclusions:

  • MAUI provides a valuable tool for researchers utilizing MBI technologies, simplifying complex pre-processing tasks.
  • The software enhances the efficiency of MBI data analysis, enabling faster insights into cellular phenotypes and tissue architecture.
  • MAUI democratizes MBI data pre-processing, reducing the reliance on specialized domain expertise for artifact correction.