The traditional frame-based cameras that rely on exposure windows for imaging experience motion blur in high-speed scenarios. Frame-based deblurring methods lack reliable motion cues to restore sharp images under extreme blur conditions. The spike camera is a novel neuromorphic visual sensor that outputs spike streams with ultra-high temporal resolution. It can supplement the temporal information lost in traditional cameras and guide motion deblurring. However, in real-world scenarios, aligning discrete RGB images and continuous spike streams along both temporal and spatial axes is challenging due to the complexity of calibrating their coordinates, device displacements in vibrations, and time deviations. Misalignment of pixels leads to severe degradation of deblurring. We introduce the first framework for spike-guided motion deblurring without knowing the spatiotemporal alignment between spikes and images. To address the problem, we first propose a novel three-stage network containing a basic deblurring net, a carefully designed bi-directional deformable aligning module, and a flow-based multi-scale fusion net. Experimental results demonstrate that our approach can effectively guide the image deblurring with unknown alignment, surpassing the performance of other methods. Public project page: https://github.com/Leozhangjiyuan/UaSDN.