Recent advancements in spatial transcriptomics (ST) have revolutionized our ability to simultaneously profile gene expression, spatial location, and tissue morphology, enabling the precise mapping of cell types and signaling pathways within their native tissue context. However, the high cost of sequencing remains a significant barrier to its widespread adoption. Although existing methods often leverage histopathological images to predict transcriptomic profiles and identify cellular heterogeneity, few approaches directly estimate cell-type abundance from these images. To address this gap, we propose HPCell, a deep learning framework for inferring cell-type abundance directly from H&E-stained histology images. HPCell comprises three key modules: a pathology foundation module, a hypergraph module, and a Transformer module. It begins by dividing whole-slide images (WSIs) into patches, which are processed by the pathology foundation module using a teacher-student framework to extract robust morphological features. These features are used to construct a hypergraph, where each patch (node) connects to its spatial neighbors to model complex many-to-many relationships. The Transformer module applies attention to the hypergraph features to capture long-range dependencies. Finally, features from all modules are integrated to estimate cell-type abundance. Extensive experiments show that HPCell consistently outperforms state-of-the-art methods across multiple spatial transcriptomics datasets, offering a scalable and cost-effective approach for investigating tissue structure and cellular interactions.