该研究文章介绍了 AAnet，这是一种用于单细胞转录组学数据的神经网络，旨在通过原型分析 (AA) 来解决肿瘤内的异质性问题。文章指出，传统的聚类和轨迹推断方法难以表征细胞状态的连续性，而AAnet通过学习数据的非线性几何，将细胞状态映射到单纯形潜在空间中，从而识别出代表极端细胞状态的原型 (ATs)。研究人员使用三阴性乳腺癌 (TNBC) 模型和小鼠转移灶数据，发现了五种主要的ATs，例如增殖性和缺氧性ATs，并结合空间转录组学揭示了这些ATs在肿瘤微环境中具有独特的空间组织。最后，通过功能验证，研究证实了缺氧AT对糖酵解的依赖性，特别关注了GLUT3基因，并将其发现与人类乳腺癌样本的ATs进行了比较，突显了AAnet在理解肿瘤细胞状态和指导治疗策略方面的潜力。References: Venkat A, Youlten S E, San Juan B P, et al. AAnet resolves a continuum of spatially-localized cell states to unveil intratumoral heterogeneity[J]. Cancer Discovery, 2025.
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