Aharoni Dana, Dudaie Matan, Barnea Itay, Shaked Natan Tzvi
Tel Aviv University, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv, Israel.
J Biomed Opt. 2025 Jan;30(1):016007. doi: 10.1117/1.JBO.30.1.016007. Epub 2025 Jan 23.
Imaging flow cytometry allows highly informative multi-point cell analysis for biological assays and medical diagnosis. Rapid processing of the imaged cells during flow allows real-time classification and sorting of the cells. Off-axis holography enables imaging flow cytometry without chemical cell staining but requires digital processing to the optical path delay profile for each frame before the cells can be classified, which slows down the overall processing throughput. We present a method for real-time cell classification via label-free quantitative imaging flow cytometry using digital holography, offering a comprehensive representation of cellular structures, without the need for digital processing before automatic cell classification.
We aim to develop an automatic cell classification scheme based directly on the off-axis holographic projections of the cells during flow and test it for stain-free imaging flow cytometry of white blood cells.
After building a dedicated off-axis holographic microscopy system for acquiring white blood cells during flow, we apply deep-learning classification directly in the off-axis hologram space, rather than in the quantitative phase profile space. This way, we simplify computational processes and allow a significant increase in the cell classification throughput. In addition, by utilizing multiple-viewpoint holographic projections of the cells rotated during flow, instead of using a single projection, we obtain better classification results due to the additional cellular information gained.
Our technique demonstrates increasing accuracy with additional viewpoint holographic projections from the optical system, achieving a 7.69% improvement when processing ten interferometric projections compared with a single interferometric projection (regular off-axis hologram). Our technique also outperforms using multiple optical path delay profile projections, requiring off-axis holographic digital preprocessing, by 17.95%, because the holographic projections are analyzed directly without preprocessing and includes the amplitude information as well.
Our cell classification approach has great potential for high-throughput, high-content, label-free imaging flow cytometry for classification of large-scale cellular datasets and real-time cell classification during flow in clinical settings.
成像流式细胞术可为生物学检测和医学诊断提供信息丰富的多点细胞分析。在流动过程中对成像细胞进行快速处理可实现细胞的实时分类和分选。离轴全息术可实现无需化学细胞染色的成像流式细胞术，但在细胞分类之前需要对每一帧的光程延迟分布进行数字处理，这会降低整体处理通量。我们提出了一种通过使用数字全息术的无标记定量成像流式细胞术进行实时细胞分类的方法，无需在自动细胞分类前进行数字处理即可全面呈现细胞结构。
我们旨在直接基于流动过程中细胞的离轴全息投影开发一种自动细胞分类方案，并将其用于白细胞的无染色成像流式细胞术检测。
构建了一个用于在流动过程中采集白细胞的专用离轴全息显微镜系统后，我们直接在离轴全息图空间而非定量相位分布空间中应用深度学习分类。通过这种方式，我们简化了计算过程并显著提高了细胞分类通量。此外，通过利用流动过程中旋转细胞的多视角全息投影，而不是使用单个投影，由于获得了额外的细胞信息，我们得到了更好的分类结果。
我们的技术表明，随着光学系统额外视角全息投影的增加，准确性不断提高，与单个干涉投影（常规离轴全息图）相比，处理十个干涉投影时准确率提高了7.69%。我们的技术也优于使用多个需要离轴全息数字预处理的光程延迟分布投影，提高了17.95%，因为全息投影无需预处理即可直接分析，并且还包含幅度信息。
我们的细胞分类方法在高通量、高内涵、无标记成像流式细胞术方面具有巨大潜力，可用于大规模细胞数据集的分类以及临床环境中流动过程中的实时细胞分类。
