Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationships. Typically, the nucleus serves as the primary diagnostic feature, while surrounding cytoplasmic information plays a supportive role. These unique characteristics limit the development of effective foundation models and hinder the transferability of histology-based models for cytopathology. To address this, we propose Cyto-SSL, the first self-supervised pretraining framework for cytological images. It introduces Nuclei-Centered Perturbation, which highlights individual nuclei by perturbing non-nuclear regions. We also design an SR-Transformer module, which complements this by using sparse attention to concentrate on diagnostically relevant scattered cells, while iRPE helps model to capture local spatial relationships and avoids unnecessary attention to irrelevant global structures. Experimental results show that Cyto-SSL enhances performance across diverse cytological datasets and Multiple Instance Learning (MIL) methods. On a WSI-level dataset, it achieved 95.67\% accuracy and outperformed ImageNet-pretrained ResNet-50 by 11.33\%, demonstrating superior feature representation for cytological analysis. Additionally, Cyto-SSL modules are plug-and-play, easily integrated into other pretraining frameworks, yielding a 2.6\% accuracy gain across different SSL methods.