C-COUNT: a convolutional neural network-based tool for automated scoring of erythroid colonies

Abstract

Despite advances in flow cytometry and single-cell transcriptomics, colony-formation assays (CFAs) remain an essential component in the evaluation of erythroid and hematopoietic progenitors. These assays provide functional information on progenitor differentiation and proliferative potential, making them a mainstay of hematology research and clinical diagnosis. However, the utility of CFAs is limited by the time-consuming and error-prone manual counting of colonies, which is also prone to bias and inconsistency. Here we present "C-COUNT," a convolutional neural network-based tool that scores the standard colony-forming-unit-erythroid (CFU-e) assay by reliably identifying CFU-e colonies from images collected by automated microscopy and outputs both their number and size. We tested the performance of C-COUNT against three experienced scientists and find that it is equivalent or better in reliably identifying CFU-e colonies on plates that also contain myeloid colonies and other cell aggregates. We further evaluated its performance in the response of CFU-e progenitors to increasing erythropoietin concentrations and to a spectrum of genotoxic agents. We provide the C-COUNT code, a Docker image, a trained model, and training data set to facilitate its download, usage, and model refinement in other laboratories. The C-COUNT tool transforms the traditional CFU-e CFA into a rigorous and efficient assay with potential applications in high-throughput screens for novel erythropoietic factors and therapeutic agents.