Artificial intelligence in imaging flow cytometry

Imaging flow cytometry (IFC) is a powerful screening technique that combines the advantages of flow cytometry and optical microscopy (Basiji et al., 2007; Rees et al., 2022). By capturing microscopy images of the specimens as they move along a liquid stream, IFC provides high-throughput collection of morphological and spatial information from thousands or even millions of samples. This makes it a key enabling technology for screening at the single-cell level, which is fundamental for identifying and characterizing pathogenic drivers and biomarkers in a cellular population and for understanding heterogeneity in a biological system. Imaging flow cytometry can be used at different scales, to study bioparticles such as extracellular vesicles (Lannigan and Erdbruegger, 2017; Görgens et al., 2019), bacteria (Power et al., 2021) and cells. It is widely used to study complex tissues, by dissociating the specimen in single cells (Covarrubias et al., 2019). New imaging systems, combined with custom microfluidics are opening to the study of entire organisms, including C. Elegans (Hernando-Rodríguez et al., 2018), Drosophila (Memeo et al., 2021) and zebrafish (Liu et al., 2017) or organoids (Paiè et al., 2016) in three dimensions. Imaging flow cytometry is today a tool for biological, drug discovery and clinical research. It has the potential to transform into a clinical diagnostic method (Doan et al., 2018), but advancements are needed both in automation and in artificial intelligence to handle and analyze the large amount of data retrieved by such high-throughput methods. In this paper we will introduce the typical pipelines for IFC acquisition and processing, and we will focus on the challenges that artificial intelligence should address to facilitate the transformation of IFC from a scientific to a medical diagnostic tool.