无需分割细胞，直接从多路成像中学习可解释特征进行细胞类型识别。
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
- 用分组卷积从各成像通道提取可解释嵌入特征
- 在180万神经母细胞瘤细胞上准确识别已知细胞类型
- 适合高维多路成像数据，避免人工特征选择
多路成像（MI）可在亚细胞分辨率下同时可视化多个生物标志物，为细胞异质性和空间组织提供重要洞察。然而现有计算流程依赖需人工调优的细胞分割算法，易因单细胞表征不准确导致下游误差。本文提出一种无分割的深度学习方法，利用分组卷积从各成像通道学习可解释的嵌入特征，实现无需手动特征选择的稳健细胞类型识别。在包含180万神经母细胞瘤患者细胞的成像质谱数据集上验证，该方法能准确识别已知细胞类型，展示其在高维MI数据中的可扩展性与适用性。
原文摘要 · Abstract (English)
Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data.
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