IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008887.html
   My bibliography  Save this article

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

Author

Listed:
  • Alex Baranski
  • Idan Milo
  • Shirley Greenbaum
  • John-Paul Oliveria
  • Dunja Mrdjen
  • Michael Angelo
  • Leeat Keren

Abstract

Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.Author summary: High-dimensional Imaging technologies allow to simultaneously measure the expression levels of dozens of proteins in biological tissue, providing insight into single-cell phenotypes and organiza-tion in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MAUI, a series of graphical user interfaces that facilitate this data pre-processing, including the removal of chan-nel crosstalk, noise and antibody aggregates. MAUI accelerates and automates these steps, such that reproducible, high-quality data will be the input for subsequent stages of analysis.

Suggested Citation

  • Alex Baranski & Idan Milo & Shirley Greenbaum & John-Paul Oliveria & Dunja Mrdjen & Michael Angelo & Leeat Keren, 2021. "MAUI (MBI Analysis User Interface)—An image processing pipeline for Multiplexed Mass Based Imaging," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-16, April.
    • Handle: RePEc:plo:pcbi00:1008887
    DOI: 10.1371/journal.pcbi.1008887
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008887
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008887&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1008887?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yael Amitay & Yuval Bussi & Ben Feinstein & Shai Bagon & Idan Milo & Leeat Keren, 2023. "CellSighter: a neural network to classify cells in highly multiplexed images," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Peng Lu & Karolyn A. Oetjen & Diane E. Bender & Marianna B. Ruzinova & Daniel A. C. Fisher & Kevin G. Shim & Russell K. Pachynski & W. Nathaniel Brennen & Stephen T. Oh & Daniel C. Link & Daniel L. J., 2023. "IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Muhammad Shaban & Yunhao Bai & Huaying Qiu & Shulin Mao & Jason Yeung & Yao Yu Yeo & Vignesh Shanmugam & Han Chen & Bokai Zhu & Jason L. Weirather & Garry P. Nolan & Margaret A. Shipp & Scott J. Rodig, 2024. "MAPS: pathologist-level cell type annotation from tissue images through machine learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Thomas Hu & Mayar Allam & Shuangyi Cai & Walter Henderson & Brian Yueh & Aybuke Garipcan & Anton V. Ievlev & Maryam Afkarian & Semir Beyaz & Ahmet F. Coskun, 2023. "Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    5. Candace C. Liu & Noah F. Greenwald & Alex Kong & Erin F. McCaffrey & Ke Xuan Leow & Dunja Mrdjen & Bryan J. Cannon & Josef Lorenz Rumberger & Sricharan Reddy Varra & Michael Angelo, 2023. "Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Yunhao Bai & Bokai Zhu & John-Paul Oliveria & Bryan J. Cannon & Dorien Feyaerts & Marc Bosse & Kausalia Vijayaragavan & Noah F. Greenwald & Darci Phillips & Christian M. Schürch & Samuel M. Naik & Edw, 2023. "Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1008887. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.