Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints that are learnt from the input data are utilized. Based on the hypothesis that adjacent spectrogram points should not be dispersed in basis spectra, this framework is provided for supervised source separation. To improve the separation performance, group sparse penalties are simultaneously constructed. A multiple-update-rule optimization scheme was used to solve the objective function of the proposed SpaSNMF. Experiments on single-channel source separation reveal that the proposed method provides more robust basis factors and achieves better results than standard NMF and its extensions.