“…Automated classification of cells using convolutional neural networks (CNN, machine learning method specialized in image recognition and classification) has become a promising approach for accurate high-throughput cell analysis that is free from observer bias (Blasi et al, 2016;Eulenberg et al, 2017;Kobayashi et al, 2017;Lei et al, 2018;Nassar et al, 2019;Suzuki et al, 2019). To date, CNN-based automated clustering and classification techniques require preexisting knowledge about the organism or cell type of interest (e.g., cell specific morphological traits within an image set) or the availability of cell-specific reagents (e.g., antibodies), or genomic sequence (e.g., single-cell sequencing) (Table 1 shows an overview of the existing methods) (Baron et al, 2019;Blasi et al, 2016;Cheng et al, 2021;Eulenberg et al, 2017;Hennig et al, 2017;Kobayashi et al, 2017;Lei et al, 2018;Nassar et al, 2019). This means that to make effective use of artificial intelligence (AI) approaches for single-cell analysis, one must have information available to train the algorithm or for machine learning (ML) models, which often arises in the form of information gleaned from the use of reagents like antibodies.…”