标题： 利用深度迁移学习和压缩感知分离共现细胞事件 显示英文标题

标题： Disentangling coincident cell events using deep transfer learning and compressive sensing

摘要： 准确的单细胞分析对于诊断、免疫监测和细胞治疗至关重要，但同时发生的事件——即多个细胞在传感区域内重叠——会严重损害信号的保真度。 我们提出了一种混合框架，将全卷积神经网络（FCN）与压缩感知（CS）相结合，以解开一维传感器数据中的重叠事件。 该FCN在珠子衍生的数据集上进行训练，能够准确估计同时发生的事件数量，并且无需重新训练即可推广到全血中的免疫磁标记的CD4+和CD14+细胞。 利用此计数，CS模块以高保真度重建各个信号成分，从而精确恢复单细胞特征，包括速度、振幅和水动力直径。 与传统状态机算法相比，表现出更优越的性能——最多可恢复21%更多的事件，并将分类准确性提高到97%以上。 通过类别激活图和参数化高斯模板拟合实现的可解释性确保了透明度和临床可解释性。 该框架已通过磁流式细胞术（MFC）进行了演示，兼容其他生成波形的模态，包括阻抗细胞计数、纳米孔和电阻脉冲传感。 这项工作为下一代非光学单细胞传感平台奠定了基础，这些平台具有自动化、通用化和解决重叠事件的能力，扩展了细胞计数在转化医学和精准诊断中的应用，例如细胞相互作用研究。 显示英文摘要

摘要： Accurate single-cell analysis is critical for diagnostics, immunomonitoring, and cell therapy, but coincident events - where multiple cells overlap in a sensing zone - can severely compromise signal fidelity. We present a hybrid framework combining a fully convolutional neural network (FCN) with compressive sensing (CS) to disentangle such overlapping events in one-dimensional sensor data. The FCN, trained on bead-derived datasets, accurately estimates coincident event counts and generalizes to immunomagnetically labeled CD4+ and CD14+ cells in whole blood without retraining. Using this count, the CS module reconstructs individual signal components with high fidelity, enabling precise recovery of single-cell features, including velocity, amplitude, and hydrodynamic diameter. Benchmarking against conventional state-machine algorithms shows superior performance - recovering up to 21% more events and improving classification accuracy beyond 97%. Explinability via class activation maps and parameterized Gaussian template fitting ensures transparency and clinical interpretability. Demonstrated with magnetic flow cytometry (MFC), the framework is compatible with other waveform-generating modalities, including impedance cytometry, nanopore, and resistive pulse sensing. This work lays the foundation for next-generation non-optical single-cell sensing platforms that are automated, generalizable, and capable of resolving overlapping events, broadening the utility of cytometry in translational medicine and precision diagnostics, e.g. cell-interaction studies.