Single-nucleus RNA sequencing (snRNA-seq) provides insights into gene expres-sion in complex tissues but suffers from lower resolution compared to single-cellRNA sequencing (scRNA-seq). To bridge this gap, we propose scWC-GAN, aWasserstein CycleGAN-based model that translates snRNA-seq data into high-resolution scRNA-seq profiles. Our method leverages Earth Mover’s Distance(EMD) for cycle consistency and a latent feature-preserving generator to bettercapture transcriptomic structures. Through extensive evaluation, scWC-GAN out-performs baseline models in FID score and SSIM, demonstrating its ability togenerate biologically meaningful data. While challenges remain in fine-grainedcell-type resolution, our results suggest scWC-GAN as a promising tool for cross-modality single-cell data translation, enhancing downstream analysis in genomics.