Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology.

细胞表型异质性是许多生物过程的重要标志，了解其起源仍然是一个巨大的挑战。这种异质性通常反映了染色质结构的变化，受到病毒感染和癌症等因素的影响，这些因素极大地重塑了细胞景观。为了解决识别不同细胞状态的挑战，我们开发了细胞核人工智能（AINU），这是一种深度学习方法，可以在纳米级分辨率下识别特定的核特征。 AINU 可以根据超分辨率显微镜图像中核心组蛋白 H3、RNA 聚合酶 II 或 DNA 的空间排列来区分不同的细胞状态。只需少量图像作为训练数据，AINU 在适当的再训练后就能正确识别人类体细胞、人类诱导的多能干细胞、转染 1 型单纯疱疹病毒的极早期感染细胞，甚至癌细胞。最后，使用人工智能解释方法，我们发现核仁中的 RNA 聚合酶 II 定位有助于区分人类诱导的多能干细胞与其体细胞。总体而言，AINU 与核结构超分辨率显微镜相结合，为精确检测细胞异质性提供了强大的工具，在推进再生医学、病毒学和癌症生物学的诊断和治疗方面具有巨大的潜力。