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 fluo-rescence-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 quantified gene delivery efficiency in space and time, leveraging human retinal organ-oids, 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.

Deep learning has become an invaluable tool for bioimage analysis but, while open-source cell annotation software such as cellpose are widely used, an equivalent tool for three-dimensional (3D) vascular annotation does not exist. With the vascular system being directly impacted by a broad range of diseases, there is significant medical interest in quantitative analysis for vascular imaging. However, existing deep learning approaches for this task are specialised to particular tissue types or imaging modalities. We present a new deep learning model for segmentation of vasculature that is generalisable across tissues, modalities, scales and pathologies. To create a generalisable model, a 3D convolutional neural network was trained using data from multiple modalities including optical imaging, computational tomography and photoacoustic imaging. Through this varied training set, the model was forced to learn common features of vessels cross-modality and scale. Following this, the general model was fine-tuned to different applications with a minimal amount of manually labelled ground truth data. It was found that the general model could be specialised to segment new datasets, with a high degree of accuracy, using as little as 0.3% of the volume of that dataset for fine-tuning. As such, this model enables users to produce accurate segmentations of 3D vascular networks without the need to label large amounts of training data.