Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with over 200 clinically available antibody-based tests in modern pathology. However, comprehensive IHC analysis is frequently limited by insufficient tissue quantities in small biopsies. Therefore, virtual multiplex staining emerges as an innovative solution to digitally transform H&E images into multiple IHC representations, yet current methods still face three critical challenges: (1) inadequate semantic guidance for multi-staining, (2) inconsistent distribution of immunochemistry staining, and (3) spatial misalignment across different stain modalities. To overcome these limitations, we present a prompt-guided framework for virtual multiplex IHC staining using only uniplex training data (PGVMS). Our framework introduces three key innovations corresponding to each challenge: First, an adaptive prompt guidance mechanism employing a pathological visual language model dynamically adjusts staining prompts to resolve semantic guidance limitations (Challenge 1). Second, our protein-aware learning strategy (PALS) maintains precise protein expression patterns by direct quantification and constraint of protein distributions (Challenge 2). Third, the prototype-consistent learning strategy (PCLS) establishes cross-image semantic interaction to correct spatial misalignments (Challenge 3).

翻译：免疫组织化学（IHC）染色能够实现对蛋白质表达的精确分子谱分析，在现代病理学中已有超过200种基于抗体的临床可用检测方法。然而，全面的IHC分析常因小活检组织中组织量不足而受到限制。因此，虚拟多重染色作为一种创新解决方案应运而生，旨在将H&E图像数字化转换为多种IHC表征，但现有方法仍面临三个关键挑战：（1）多重染色的语义引导不足；（2）免疫化学染色分布不一致；（3）不同染色模态间的空间错位。为克服这些局限，我们提出了一种仅需单重训练数据的提示引导虚拟多重IHC染色框架（PGVMS）。我们的框架针对每个挑战引入了三项关键创新：首先，采用病理视觉语言模型的自适应提示引导机制，动态调整染色提示以解决语义引导不足问题（挑战1）。其次，我们的蛋白质感知学习策略（PALS）通过对蛋白质分布进行直接量化和约束，保持精确的蛋白质表达模式（挑战2）。第三，原型一致性学习策略（PCLS）建立跨图像语义交互以校正空间错位（挑战3）。