eLife (Jul 2021)

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

  • Alice Accorsi,
  • Andrew C Box,
  • Robert Peuß,
  • Christopher Wood,
  • Alejandro Sánchez Alvarado,
  • Nicolas Rohner

DOI
https://doi.org/10.7554/eLife.65372
Journal volume & issue
Vol. 10

Abstract

Read online

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 clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-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.

Keywords