3D Quantification of Viral Transduction Efficiency in Living Human Retinal Organoids

TL;DR

This paper introduces a new method to measure how well viruses deliver genes into living human retinal tissues using 3D imaging and machine learning.

Contribution

The novel contribution is a spatiotemporal quantification method for viral transduction in living retinal organoids using confocal imaging and deep learning.

Findings

A pipeline combining live imaging and deep learning enables 3D quantification of transduction efficiency in retinal organoids.

The method preserves spatial and temporal information, allowing time-dependent studies of gene delivery.

This approach can be extended to evaluate drug delivery in various biological systems.

Abstract

The development of therapeutics builds on testing their efficiency in vitro. To optimize gene therapies, for example, fluorescent reporters expressed by treated cells are typically utilized as readouts. Traditionally, their global fluorescence signal has been used as an estimate of transduction efficiency. However, analysis in individual cells within a living 3D tissue remains a challenge. Readout on a single‐cell level can be realized via fluorescence‐based flow cytometry at the cost of tissue dissociation and loss of spatial information. Complementary, spatial information is accessible via immunofluorescence of fixed samples. Both approaches impede time‐dependent studies on the delivery of the vector to the cells. Here, quantitative 3D characterization of viral transduction efficiencies in living retinal organoids is introduced. The approach combines quantification of gene delivery efficiency in space and time, leveraging human retinal organoids, engineered adeno‐associated virus (AAV) vectors, confocal live imaging, and deep learning‐based image segmentation. The integration of these tools in an organoid imaging and analysis pipeline allows quantitative testing of future treatments and other gene delivery methods. It has the potential to guide the development of therapies in biomedical applications.

Optimization of viral vectors and their testing in human tissue is crucial to gene therapy development. Conventional technologies to assess virus transduction either lack spatial or temporal information. Here, spatiotemporal mapping of virus transduction in living retinal tissue is introduced, levering fluorescence imaging and machine learning‐based segmentation. Beyond viral vectors, drug delivery in a plethora of systems can be quantified.

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