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H-EM:一种用于低分辨率图像细胞术的同时细胞直径和强度定量的算法。

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry.

机构信息

Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain.

Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, MA, United States of America.

出版信息

PLoS One. 2019 Sep 12;14(9):e0222265. doi: 10.1371/journal.pone.0222265. eCollection 2019.

Abstract

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called "cell astronomy" systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

摘要

荧光细胞术是指使用荧光标记的抗体定量测定细胞的物理特性和表面生物标志物。通常首选的技术是流式细胞术,它通过逐个流过细胞来进行快速的单细胞分析,以及显微镜术,它消除了流动通道的复杂性,提供了较少吞吐量的多细胞分析。低放大倍率基于图像的细胞仪,也称为“细胞天文学”系统,有望同时实现仪器简单性和高通量。在这个放大倍率下,单个细胞映射到图像中的少数几个像素。虽然非常有吸引力,但到目前为止,这个想法还没有被证明能够产生细胞标记的定量结果,主要是由于这些图像中存在较差的信噪比和部分体积效应。在这项工作中,我们展示了一种细胞天文学系统,当与定制开发的算法结合使用时,能够可靠地定量细胞强度和直径。我们使用经过校准的 MESF 珠和荧光染色的白细胞展示了该系统,在两种情况下都能很好地识别群体。所提出的系统的主要贡献在于开发了一种新算法 H-EM,该算法能够在非常低的放大倍率(2x)下实现簇间分离。与手动分析相比,该算法提供的亮度估计比 DAOSTORM 更准确,同时拟合细胞位置、亮度、直径和背景水平。该算法首先执行 Fisher 判别分析来检测亮点。从每个点开始,通过异构混合模型(H-EM)初始化期望最大化算法,该算法以亚像素精度恢复细胞荧光和直径,同时区分背景噪声。最后,应用递归分裂过程来识别细胞簇中的单个细胞。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/6742454/f3240add626e/pone.0222265.g001.jpg

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