Hyperspectral unmixing aims at separating a mixed pixel into a set of pure spectral signatures and their corresponding fractional abundances. Investigating prior spatial and spectral information to regularize the unmixing problem can effectively improve the estimation performance. However, handcrafting a powerful regularizer is a non-trivial task and complex regularizers introduce extra difficulties in solving the optimization problem. In this paper, we present a flexible spectral unmixing method using plug-and-play priors. This method benefits from the alternating direction method of multipliers (ADMM) to decompose the optimization problem into iterative subproblems and incorporates the image denoisers as prior models in a subproblem. In this form, we can plug in various image denoising operations to bypass handcrafting regularizers. We demonstrate the superiority of the proposed unmixing method comparing with other state-of-the-art methods both on synthetic data and real airborne data.