Department of Information Technology, Centre for Image Analysis, Uppsala University, Uppsala, Sweden.
Cytometry A. 2011 Jul;79(7):518-27. doi: 10.1002/cyto.a.21087. Epub 2011 Jun 10.
Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.
特定的单分子检测为基因组学和蛋白质组学开辟了新的可能性，需要自动化的图像分析来进行准确的定量。本工作提出了用于检测和分类使用锁式探针或邻近连接检测到的单分子和单分子相互作用的图像分析方法。我们使用简单、广泛且具有成本效益的宽场显微镜，并通过使用荧光染料组合标记检测事件来增加检测的多重性。本文提出的数学模型可以通过光谱角对双通道图像中的呈点状的信号进行分类，而无需区分低强度和高强度。我们在具有已知信号类别的实验上评估了这些方法，并与基于强度阈值的经典分类算法进行了比较。我们还展示了如何通过测量检测探针的质量和准确性，将这些方法用作评估生化方案的工具。最后，该方法用于原位评估单分子检测事件。
