Recombinant adeno-associated virus (rAAV) has become the most widely used vector in the gene therapy field with hundreds of clinical trials ongoing and already several products on the market. AAV's physicochemical stability, and the various natural and engineered serotypes allow for targeting a broad range of cell types and tissue by diverse routes of administration. Progressing from early clinical studies to eventual market approval, many critical quality attributes have to be defined and reproducibly quantified, such as AAV stability, purity, aggregates, empty/full particles ratio, and rAAV genome titration. Droplet digital PCR (ddPCR) is becoming the tool of choice to perform absolute quantification of rAAV genomes. In the present study, we have identified critical parameters that could impact AAV titration and characterization accuracy, such as Poisson distribution confidence interval, primers/probe position, and potential aggregates. Our work presents how ddPCR can help to better characterize AAV vectors on the single particle level and highlights challenges that we are facing today in terms of AAV titration.
Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things; however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices; (2) managing problems that arise during implementation; and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.
Training in robust research practices is becoming increasingly common. However, many course participants may encounter challenges in implementation of what they learned after returning to their research groups. In this piece, we summarize insights and "lessons learned" from a group of former course participants. We offer practical tips on implementation and cultural change that may be useful for researchers at any career stage. In addition, we provide a list of considerations for course instructors to help them support course attendees after training is over.
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