We consider the informed source separation (ISS) problem where, given the sources and the mixtures, any kind of side-information can be computed during a so-called encoding stage. This side-information is then used to assist source separation, given the mixtures only, at the so-called decoding stage. State of the art ISS approaches do not really consider ISS as a coding problem and rely on some purely source separation-inspired strategies, leading to performances that can at best reach those of oracle estimators. On the other hand, classical source coding strategies are not optimal either, since they do not benefit from the mixture availability. We introduce a general probabilistic framework called coding-based ISS (CISS) that consists in quantizing the sources using some posterior source distribution from those usually used in probabilistic model-based source separation. CISS benefits from both source coding, thanks to the source quantization, and source separation, thanks to the use of the posterior distribution that depends on the mixture. Our experiments show that CISS based on a particular model considerably outperforms for all rates both the conventional ISS approach and the source coding approach based on the same model.