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.
Impact statement Image3C analyzes cell populations through image-based clustering and neural network training, which allows single-cell analysis in research organisms devoid of species-specific reagents or pre-existing knowledge on cell phenotypes.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵# These authors share senior authorship
We've added a convolutional neural network that uses Image3C cluster information and Imagestream data for classification analysis of large datasets.