Following nanoparticle uptake by cells using high-throughput microscopy and the deep-learning based cell identification algorithm Cellpose

How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.

Automated summary

The uptake of nanoparticles by human cells is a crucial question for various applications in medicine and safety. Microscopy-based methods, particularly deep-learning algorithms like Cellpose, offer advantages in accurately identifying and analyzing cells. This study successfully applied Cellpose to fluorescence microscopy images of HeLa cells and developed a workflow for high-throughput analysis of thousands of cells without compromising accuracy. The workflow was then used to study the relationship between cell size and nanoparticle uptake using images of cells exposed to fluorescently-labeled polystyrene nanoparticles.