In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model.

Laser-induced choroidal neovascularization (CNV) is a well-established model to mimic the wet form of age-related macular degeneration (AMD). In this protocol, we aim to guide the reader not simply through the technical considerations of generating laser-induced lesions to trigger neovascular processes, but rather focus on the powerful information that can be obtained from multimodal longitudinal in vivo imaging throughout the follow-up period. The laser-induced mouse CNV model was generated by a diode laser administration. Multimodal in vivo imaging techniques were used to monitor CNV induction, progression and regression. First, spectral domain optical coherence tomography (SD-OCT) was performed immediately after the lasering to verify a break of Bruch's membrane. Subsequent in vivo imaging using fluorescein angiography (FA) confirmed successful damage of Bruch's membrane from serial images acquired at the choroidal level. Longitudinal follow-up of CNV proliferation and regression on days 5, 10, and 14 after the lasering was performed using both SD-OCT and FA. Simple and reliable grading of leaky CNV leasions from FA images is presented. Automated segmentation for measurement of total retinal thickness, combined with manual caliber application for measurement of retinal thickness at CNV sites, allow unbiased evaluation of the presence of edema. Finally, histological verification of CNV is performed using isolectin GS-IB4 staining on choroidal flatmounts. The staining is thresholded, and the isolectin-positive area is calculated with ImageJ. This protocol is especially useful in therapeutics studies requiring high-throughput-like screening of CNV pathology as it allows fast, multimodal, and reliable classification of CNV pathology and retinal edema. In addition, high resolution SD-OCT enables the recording of other pathological hallmarks, such as the accumulation of subretinal or intraretinal fluid. However, this method does not provide a possibility to automate CNV volume analysis from SD-OCT images, which has to be performed manually.