Image3C, a multimodal image-based and label independent integrative method for single-cell analysis

  1. Alice Accorsi
  2. Andrew C Box
  3. Robert Peuß
  4. Christopher Wood
  5. Alejandro Sánchez Alvarado  Is a corresponding author
  6. Nicolas Rohner
  1. Stowers Institute for Medical Research, United States

Abstract

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

Data availability

All original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1390. Image3C code and description are freely available at the GitHub repository https://github.com/stowersinstitute/LIBPB-1390-Image3C.

Article and author information

Author details

  1. Alice Accorsi

    N/A, Stowers Institute for Medical Research, Kansas City, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Andrew C Box

    N/A, Stowers Institute for Medical Research, Kansas City, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Robert Peuß

    N/A, Stowers Institute for Medical Research, Kansas City, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9716-6650
  4. Christopher Wood

    NA, Stowers Institute for Medical Research, Kansas City, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Alejandro Sánchez Alvarado

    N/A, Stowers Institute for Medical Research, Kansas City, United States
    For correspondence
    asa@stowers.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1966-6959
  6. Nicolas Rohner

    NA, Stowers Institute for Medical Research, Kansas City, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3248-2772

Funding

Howard Hughes Medical Institute

  • Alejandro Sánchez Alvarado

National Science Foundation (1923372)

  • Nicolas Rohner

National Institutes of Health (GM127872,DP2DP2AG071466)

  • Nicolas Rohner

Stowers Institute for Medical Research

  • Andrew C Box
  • Christopher Wood
  • Alejandro Sánchez Alvarado
  • Nicolas Rohner

Deutsche Forschungsgemeinschaft (PE 2807/1-1)

  • Robert Peuß

American Association for Anatomy

  • Alice Accorsi

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: Research and animal care were approved by the Institutional Animal Care and Use Committee (IACUC) of the Stowers Institute for Medical Research. protocol (#2019-080)

Reviewing Editor

  1. Robert P Zinzen, Max Delbrück Centre for Molecular Medicine, Germany

Publication history

  1. Preprint posted: April 9, 2019 (view preprint)
  2. Received: December 2, 2020
  3. Accepted: July 20, 2021
  4. Accepted Manuscript published: July 21, 2021 (version 1)
  5. Version of Record published: August 17, 2021 (version 2)

Copyright

© 2021, Accorsi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alice Accorsi
  2. Andrew C Box
  3. Robert Peuß
  4. Christopher Wood
  5. Alejandro Sánchez Alvarado
  6. Nicolas Rohner
(2021)
Image3C, a multimodal image-based and label independent integrative method for single-cell analysis
eLife 10:e65372.
https://doi.org/10.7554/eLife.65372

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