Robust Analysis of Subcellular Time-lapse Assays

描述（由申请人提供）：我们的目标是开发和商业化活细胞，延时显微镜图像信息学软件，专门用于亚细胞功能的高通量定量，如分析棘和树突中的信号传导。 该软件集成了当前信息学工具中不可用的亚细胞延时分析和建模的新颖而强大的方法，用于增强信号检测和抗噪性，以提高分析通量，准确性，效率和可靠性。 定量延时分析的一般信息学框架包括（1）模型，（2）数据，以及（3）数据与模型的拟合。 我们将创建三个级别的强大动力学分析算法创新，以提高数据质量，模型以及模型拟合的准确性和效率。 我们的具体目标是：1）完成对延时亚细胞分析图像的鲁棒目标检测算法的开发，并定量验证其有效性; 2）完成鲁棒延时信号增强和特征测量算法的开发，并定量验证其有效性;以及3）初步开发分析结果导向的模型拟合和自动校正算法，并定量评估分析质量的改善。 为了建立该方法的可行性，我们选择了基于图像的突触囊泡回收测定作为我们的目标测定。 它是神经元功能的重要指标，其特征在于信号弱且不稳定，对噪声特别敏感，对自动和定量分析有很强的要求，提供了优于替代测定方法（电生理学或电子显微镜）的令人信服的益处，并且有利于学术研究和药物发现筛选。 在第一阶段结束时，我们将产生一个工作原型，用于突触囊泡回收测定的稳健分析。 我们希望通过使用Z因子和S：N显著提高检测质量来验证我们创新的可行性。 这种信息学软件可以在加速将实验室发现转化为药物发现的高通量筛选方面发挥关键作用。

DESCRIPTION (provided by applicant): Our goal is to develop and commercialize live cell, time-lapse microscopy image informatics software specialized for the high throughput quantification of subcellular functions such as the analysis of signaling in spines and dendrites. The software integrates novel and robust methods of subcellular time-lapse analysis and modeling not available in the current informatics tools for the enhancement of signal detection and noise immunity to improve on assay throughput, accuracy, efficiency and reliability. The general informatics framework of a quantitative time-lapse assay consists of (1) the model, (2) the data, and (3) the fitting of data to the model. We will create three levels of robust kinetic analysis algorithm innovations to enhance the data quality, model, as well as model fitting accuracy and efficiency. Our specific aims are 1) Complete the development of the robust object detection algorithm for time-lapse subcellular assay images and quantitatively validate its efficacy; 2) Complete the development of robust timelapse signal enhancement and feature measurement algorithms and quantitatively validate its efficacy; and 3) Initial development of assay outcome-directed model fitting and auto-correction algorithms, and quantitatively assess the improvement in assay quality. To establish the feasibility of the approach, we have selected the image based synaptic vesicle recycling assay as our target assay. It is an important indicator of neuronal function, is characterized by weak and unstable signal and is particularly sensitive to noise, has a strong requirement for automatic and quantitative analysis, provides a compelling benefit over alternative assay approaches (electrophysiology or electron microscopy), and is beneficial for both academic research and drug discovery screening. At the end of the phase I, we will have produced a working prototype for the robust analysis of synaptic vesicle recycling assays. We expect to validate the feasibility of our innovations by significantly improved assay quality using Z-factor and S:N. This informatics software can play a key role in speeding the translation of lab discoveries into high throughput screens for drug discovery.