A Semi-Automated Imaging Flow Cytometry Workflow for High-Throughput Quantification of Compound Internalization with
Kirill Elfimov1, Ludmila Gotfrid1, Alina Nokhova2
1State Research Center of Virology and Biotechnology 'Vector', Koltsovo 630559, Russia.
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For many therapeutic agents to be effective against intracellular targets, they must first be able to penetrate the cell membrane. Current methodologies for assessing internalization, such as confocal microscopy and conventional flow cytometry, are limited by low throughput or an inability to provide precise spatial information on signal localization. Here, we present a comprehensive, semi-automated analytical pipeline for investigating compound internalization based on imaging flow cytometry, which is designed to address these limitations. Our workflow details the procedure from sample preparation and data acquisition on an Amnis FlowSight cytometer to analysis using IDEAS 6.2 software with a custom-designed template. Key features of our approach include the automated discrimination of signal between the plasma membrane and cytoplasmic compartments, the calculation of an internalization coefficient, and the introduction of a novel parameter-signal distribution entropy-to quantify the uniformity of the compound distribution within cells. For the statistical analysis, we developed FluoSta v1.0, a software tool that automates descriptive statistics and analysis of variance (ANOVA with Tukey's post hoc test) and facilitates data visualization. The pipeline's utility was demonstrated in a series of model experiments, including a comparative assessment of the internalization efficiency of PS- versus PS/LNA-modified compounds in MT-4 cell cultures.
