High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.

Infectious titer determination of lentiviral vectors using a temporal immunological real-time imaging approach.

The analysis of the infectious titer of the lentiviral vector samples obtained during upstream and downstream processing is of major importance, however, also the most challenging method to be performed. Currently established methods like flow cytometry or qPCR lack the capability of enabling high throughput sample processing while they require a lot of manual handling. To address this limitation, we developed an immunological real-time imaging method to quantify the infectious titer of anti-CD19 CAR lentiviral vectors with a temporal readout using the Incucyte® S3 live-cell analysis system. The infective titers determined with the Incucyte® approach when compared with the flow cytometry-based assay had a lower standard deviation between replicates and a broader linear range. A major advantage of the method is the ability to obtain titer results in real-time, enabling an optimal readout time. The presented protocol significantly decreased labor and increased throughput. The ability of the assay to process high numbers of lentiviral samples in a high throughput manner was proven by performing a virus stability study, demonstrating the effects of temperature, salt, and shear stress on LV infectivity.