Profiling Co‐Occurrent Morphological Phenotypes and Their Degree of Expression Severity in Vacuolated Cells by Holo‐Tomographic Flow Cytometry and Fractal Analysis

Cells are complex systems characterized by large phenotype heterogeneity. Conventional single‐cell classification usually separates cells expressing a certain phenotype from the healthy control. However, multiple phenotypes typically coexist within the same cell as a result of complex intracellular interactions, machineries, and external stimuli. Here, we use label‐free optical microscopy to investigate how morphological phenotypes co‐occur within vacuolated cells. Cytoplasmic vacuoles are important hallmarks of several pathologies (e.g., lysosomal storage diseases, viral infections, cancer). We rely on Holo‐tomographic flow cytometry (HTFC) to obtain 3D refractive index tomograms of vacuolated cells in continuous flow. Then, we propose a strategy to reduce the dimensionality of the tomogram using cross‐sectioning and minimum intensity projection (MIP) maps. We extract a set of morphological, refractive index‐based, and fractal parameters demonstrating that the complex heterogeneity of vacuole patterns can be captured and can foster classification based on interpretable features. For training an AI, biologist domain‐experts provided annotation of the different morphological phenotypes expressed and ranked them in terms of expression severity from the tomographic observations. Thus, we introduce a pipeline for morphometric phenotype profiling, in which each cell is associated with a seven‐digit classification code representing the combination of coexisting phenotypes it expresses and their expression severity levels.