Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

View ORCID ProfileEric Czech, View ORCID ProfileBulent Arman Aksoy, View ORCID ProfilePinar Aksoy, View ORCID ProfileJeff Hammerbacher
doi: https://doi.org/10.1101/460980
Eric Czech
1Microbiology and Immunology Department at Medical University of South Carolina
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eric Czech
  • For correspondence: eric@hammerlab.org
Bulent Arman Aksoy
1Microbiology and Immunology Department at Medical University of South Carolina
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Bulent Arman Aksoy
Pinar Aksoy
1Microbiology and Immunology Department at Medical University of South Carolina
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pinar Aksoy
Jeff Hammerbacher
1Microbiology and Immunology Department at Medical University of South Carolina
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jeff Hammerbacher
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Background Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.

Results Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.

Conclusion Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.

Footnotes

  • Email addresses: EC: eric{at}hammerlab.org, BAA: arman{at}hammerlab.org, PA: pinar{at}hammerlab.org, JH: hammer{at}hammerlab.org

  • This revision contains information on CellProfiler integration.

  • Abbreviations

    CODEX
    Co-detection by indexing
    GUI
    Graphical User Interface
    PHA
    Phalloidin-Fluor 594
    CP
    CellProfiler
    DEI
    DNA Exchange Imaging
    TIFF
    Tagged ImageFile Format
    FCS
    Flow Cytometry Standard
    CLI
    Command Line Interface
    I/O
    Input / Output
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
    Back to top
    PreviousNext
    Posted July 18, 2019.
    Download PDF
    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging
    Eric Czech, Bulent Arman Aksoy, Pinar Aksoy, Jeff Hammerbacher
    bioRxiv 460980; doi: https://doi.org/10.1101/460980
    Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
    Citation Tools
    Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging
    Eric Czech, Bulent Arman Aksoy, Pinar Aksoy, Jeff Hammerbacher
    bioRxiv 460980; doi: https://doi.org/10.1101/460980

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Bioinformatics
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (4682)
    • Biochemistry (10357)
    • Bioengineering (7670)
    • Bioinformatics (26330)
    • Biophysics (13523)
    • Cancer Biology (10683)
    • Cell Biology (15438)
    • Clinical Trials (138)
    • Developmental Biology (8497)
    • Ecology (12820)
    • Epidemiology (2067)
    • Evolutionary Biology (16851)
    • Genetics (11399)
    • Genomics (15478)
    • Immunology (10616)
    • Microbiology (25207)
    • Molecular Biology (10220)
    • Neuroscience (54463)
    • Paleontology (401)
    • Pathology (1668)
    • Pharmacology and Toxicology (2897)
    • Physiology (4342)
    • Plant Biology (9243)
    • Scientific Communication and Education (1586)
    • Synthetic Biology (2557)
    • Systems Biology (6780)
    • Zoology (1466)