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Informatics challenges of high-throughput microscopy

机译：高通量显微镜的信息学挑战

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In this article, we discussed the emerging informatics issues of high-throughput screening (HTS) using automated fluorescence microscopy technology, otherwise known as high-content screening (HCS) in the pharmaceutical industry. Optimal methods of scoring biomarkers and identifying candidate hits have been actively studied in academia and industry, with the exception of data modeling topics. To find candidate hits, we need to score the images associated with different compound interventions. In the application example of RNAi genome-wide screening, we aim to find the candidate effectors or genes which correspond to the images acquired using the three channels. Scoring the effectors is equivalent to scoring the images based on the number of phenotypes existing in those images. Our ultimate objective of studying HTS is to model the relationship between gene networks and cellular phenotypes, investigate cellular communication via protein interaction, and study the disease mechanism beyond the prediction based on the molecular structure of the compound. Finally, computational image analysis has become a powerful tool in cellular and molecular biology studies. Signal processing and modeling for high-throughput image screening is an emerging filed that requires novel algorithms for dynamical system analysis, image processing, and statistical modeling. We hope that this article will motivate the signal processing communities to address challenging data modeling and other informatics issues of HTS.
机译：在本文中，我们讨论了使用自动荧光显微镜技术进行的高通量筛选（HTS）的新兴信息学问题，在制药行业中也称为高内涵筛选（HCS）。除数据建模主题外，学术界和工业界都在积极研究对生物标志物评分和识别候选命中的最佳方法。为了找到候选命中，我们需要对与不同复合干预措施相关的图像进行评分。在RNAi全基因组筛选的应用示例中，我们旨在找到与使用三个通道获取的图像相对应的候选效应子或基因。对效应子评分等同于根据那些图像中存在的表型数量对图像评分。我们研究HTS的最终目的是建模基因网络与细胞表型之间的关系，通过蛋白质相互作用研究细胞之间的通讯，并研究基于化合物分子结构无法预测的疾病机理。最后，计算图像分析已成为细胞和分子生物学研究中的强大工具。用于高通量图像筛选的信号处理和建模是一个新兴领域，需要用于动态系统分析，图像处理和统计建模的新颖算法。我们希望本文能够激励信号处理社区解决HTS的挑战性数据建模和其他信息学问题。

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