Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning–augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell–level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

传统的成像流式细胞仪使用荧光标记来识别特定结构，但通过标记过程限制了通量。我们开发了一种无标签技术，可减轻物理染色并通过深度学习增强的数字标签方法提供多路复用读数。我们利用反射显微镜中丰富的结构信息和卓越的灵敏度，并表明在对免疫荧光图像进行训练后，数字标记可以预测准确的亚细胞特征。我们证明了与现有技术相比，预测准确性提高了三倍。除了荧光预测之外，我们还证明了细胞周期的单细胞水平结构表型可以通过数字多路复用图像正确再现，包括高尔基双胞胎、有丝分裂期间的高尔基混浊和 DNA 合成。我们进一步表明，多路复用读数能够跨大细胞群进行准确的多参数单细胞分析。我们的方法可以显着提高成像细胞仪在表型分析、病理学和高内涵筛选应用中的通量。