Nuclear instance segmentation has played a critical role in pathology image analysis. The main challenges arise from the difficulty in accurately segmenting densely overlapping instances and the high cost of precise mask-level annotations. Existing fully-supervised nuclear instance segmentation methods, such as boundary-based methods, struggle to capture differences between overlapping instances and thus fail in densely distributed blurry regions. They also face challenges transitioning to point supervision, where annotations are simple and effective. Inspired by natural mudslides, we propose a universal method called Mudslide that uses simple representations to characterize differences between different instances and can easily be extended from fully-supervised to point-supervised. Concretely, we introduce a collapse field and leverage it to construct a force map and initial boundary, enabling a distinctive representation for each instance. Each pixel is assigned a collapse force, with distinct directions between adjacent instances. Starting from the initial boundary, Mudslide executes a pixel-by-pixel collapse along various force directions. Pixels that collapse into the same region are considered as one instance, concurrently accounting for both inter-instance distinctions and intra-instance coherence. Experiments on public datasets show superior performance in both fully-supervised and point-supervised tasks. The code is available at https://github.com/CVPR2024/mudslide.