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Title: Image3C, a multimodal image-based and label independent integrative method for single-cell analysis
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.  more » « less
Award ID(s):
1923372
NSF-PAR ID:
10287425
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
eLife
Volume:
10
ISSN:
2050-084X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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