Department Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.
IEEE Trans Med Imaging. 2012 Sep;31(9):1786-808. doi: 10.1109/TMI.2012.2203142. Epub 2012 Jun 6.
The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function of the tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.
病毒颗粒进入细胞的过程对感染起着决定性的作用。在这项工作中，我们通过延时荧光显微镜研究了病毒颗粒与细胞膜的融合。为了能够基于单个颗粒随时间的强度自动识别融合，我们开发了一种分层概率方法。该方法将单个颗粒的作用分解为三个抽象：随时间的强度、潜在的时间强度模型以及高级别行为。每个抽象对应一个层，这些层通过随机混合系统和隐马尔可夫模型表示。我们使用最大似然策略来有效地组合这两种表示。为了计算抽象的估计值，我们使用混合粒子滤波器和维特比算法。基于合成图像序列，我们将该方法的性能作为图像噪声的函数进行了特征描述。我们还将性能作为跟踪误差的函数进行了特征描述。我们还成功地将该方法应用于显示与宿主细胞接触的假型 HIV-1 颗粒的真实图像序列，并将实验结果与通过手动分析获得的真实结果进行了比较。
