Seeing ghosts In fluorescence-activated cell sorting, characteristic target features are labeled with a specific fluorophore, and cells displaying different fluorophores are sorted. Ota et al. describe a technique called ghost cytometry that allows cell sorting based on the morphology of the cytoplasm, labeled with a single-color fluorophore. The motion of cells relative to a patterned optical structure provides spatial information that is compressed into temporal signals, which are sequentially measured by a single-pixel detector. Images can be reconstructed from this spatial and temporal information, but this is computationally costly. Instead, using machine learning, cells are classified directly from the compressed signals, without reconstructing an image. The method was able to separate morphologically similar cell types in an ultrahigh-speed fluorescence imaging–activated cell sorter. Science, this issue p. 1246 Morphology-based cell classification and sorting is achieved at high accuracy and throughput without obtaining images. Ghost imaging is a technique used to produce an object’s image without using a spatially resolving detector. Here we develop a technique we term “ghost cytometry,” an image-free ultrafast fluorescence “imaging” cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.

中文翻译：

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幽灵细胞术

看到鬼影 在荧光激活的细胞分选中，特征性目标特征用特定的荧光团标记，然后对显示不同荧光团的细胞进行分选。太田等人。描述了一种称为鬼细胞计数的技术，该技术允许根据细胞质的形态进行细胞分选，并用单色荧光团标记。细胞相对于图案化光学结构的运动提供了空间信息，该信息被压缩成时间信号，由单像素检测器依次测量。可以从这些空间和时间信息重建图像，但这在计算上是昂贵的。相反，使用机器学习，可以直接从压缩信号中对细胞进行分类，而无需重建图像。该方法能够在超高速荧光成像激活细胞分选仪中分离形态相似的细胞类型。科学，这个问题 p。1246 基于形态学的细胞分类和分选以高精度和高吞吐量实现，无需获取图像。重影成像是一种用于在不使用空间分辨检测器的情况下生成物体图像的技术。在这里，我们开发了一种称为“幻影细胞术”的技术，这是一种基于单像素检测器的无图像超快荧光“成像”细胞术。从细胞相对于静态随机图案光学结构的运动中获得的空间信息被压缩转换为信号，依次到达单像素检测器。时间波形与随机模式强度分布的组合使用使我们能够以计算方式重建细胞形态。更重要的是，我们表明，在没有图像重建的情况下直接在压缩波形上应用机器学习方法可以实现高效的无图像形态学流式细胞术。尽管仪器紧凑且价格低廉，但无图像重影细胞术在没有特定生物标志物的情况下基于细胞形态实现了准确和高通量的细胞分类和选择性分选，这两者都难以使用传统流式细胞仪来实现。我们表明，在没有图像重建的情况下直接在压缩波形上应用机器学习方法可以实现高效的无图像形态学流式细胞术。尽管仪器紧凑且价格低廉，但无图像重影细胞术在没有特定生物标志物的情况下基于细胞形态实现了准确和高通量的细胞分类和选择性分选，这两者都难以使用传统流式细胞仪来实现。我们表明，在没有图像重建的情况下直接在压缩波形上应用机器学习方法可以实现高效的无图像形态学流式细胞术。尽管仪器紧凑且价格低廉，但无图像重影细胞术在没有特定生物标志物的情况下基于细胞形态实现了准确和高通量的细胞分类和选择性分选，这两者都难以使用传统流式细胞仪来实现。