Abstract
Transmission electron microscopy (TEM) is a gold standard analytical method for nanoparticle characterization and is playing a valuable role in virus-like particle (VLP) characterization extending to other biological entities such as viral vectors. A dedicated TEM facility is a challenge to both small and medium-sized enterprises (SMEs) and companies operating in low-and-middle income countries (LMICs) due to high start-up and running costs. A low-voltage TEM solution with assisted image acquisition and analysis such as the MiniTEM system, coupled with Vironova Imaging and Analysis Software (VIAS) could provide an affordable and practical alternative. The MiniTEM system has a small footprint and software that enables semi-automated data collection and image analysis workflows using built-in deep learning methods (convolutional neural networks) for automation in analysis, increasing speed of information processing and enabling scaling to larger datasets. In this perspective we outline the potential and challenges in the use of TEM as mainstream analytical tool in manufacturing settings. We highlight the rationale and preliminary findings from our proof-of-concept study aiming to develop a method to assess critical quality attributes (CQAs) of VLPs and facilitate adoption of TEM in manufacturing settings. In our study we explored all the steps, from sample preparation to data collection and analysis using synthetic VLPs as model systems. The applicability of the method in product development was verified at pilot-scale during the technology transfer of dengue VLPs development from a university setting to an LMIC- based vaccine manufacturing company, demonstrating the applicability of this analytical technique to VLP vaccine characterization.