Bioimage Informatics for Systems Pharmacology

Education Chapter 17: Bioimage Informatics for Systems Pharmacology Fuhai Li, Zheng Yin, Guangxu Jin, Hong Zhao, Stephen T. C. Wong* NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Weil Medical College of Cornell University, Houston, Texas, United States of America Abstract: Recent advances in auto- 1. Introduction es on extracting and analyzing quantita- mated high-resolution fluorescence tive phenotypic data automatically from The old adage that a picture is worth a microscopy and robotic handling large amounts of cell images with ap- thousand words certainly applies to the have made the systematic and cost proaches in image analysis, computation effective study of diverse morpho- identification of phenotypic variations in vision and machine learning [3,4]. Appli- logical changes within a large pop- biomedical studies. Bright field microsco- cations of HCA for screening drugs and ulation of cells possible under a py, by detecting light transmitted through targets are referred to as High Content variety of perturbations, e.g., drugs, thin and transparent specimens, has been Screening (HCS), which focuses on iden- compounds, metal catalysts, RNA widely used to investigate cell size, shape, tifying compounds or genes that cause interference (RNAi). Cell population- and movement. The recent development desired phenotypic changes [5–7]. The of fluorescent proteins, e.g., green fluores- based studies deviate from conven- image data contain rich information cent protein and its derivatives [1], tional microscopy studies on a few content for understanding biological pro- cells, and could provide stronger enabled the investigation of the phenotyp- cesses and drug effects, indicate diverse statistical power for drawing exper- ic changes of subcellular protein struc- and heterogeneous behaviors of individual imental observations and conclu- tures, e.g., chromosomes and microtu- cells, and provide stronger statistical power sions. However, it is challenging to bules, revolutionizing optical imaging in in drawing experimental observations and manually extract and quantify phe- biomedical studies. Fluorescent proteins conclusions, compared to conventional notypic changes from the large are bound to specific proteins that are microscopy studies on a few cells. Howev- amounts of complex image data uniformly located in relevant cellular generated. Thus, bioimage informat- structures, e.g., chromosomes, and emit er, extracting and mining the phenotypic ics approaches are needed to rapidly longer wavelength light, e.g., green light, changes from the large scale, complex and objectively quantify and analyze after exposure to shorter wavelength light, image data is daunting. It is not feasible to the image data. This paper provides e.g., blue light. Thus, the spatial morphol- manually analyze these data. Hence, bio- an overview of the bioimage infor- ogy and temporal dynamic activities of image informatics approaches were need- matics challenges and approaches in subcellular protein structures can be ed to automatically and objectively ana- image-based studies for drug and lyze large scale image data, extract and target discovery. The concepts and imaged with a fluorescence microscope - an optical microscope that can specifically quantify the phenotypic changes to profile capabilities of image-based screen- the effects of drugs and targets. ing are first illustrated by a few detect emitted fluorescence of a specific Bioimage informatics in image-based practical examples investigating dif- wavelength [2]. In current image-based ferent kinds of phenotypic changes studies, five-dimensional (5D) image data studies usually consists of multiple analysis caused by drugs, compounds, or of thousands of cells (cell populations) can modules [3,8,9], as shown in Figure 1. RNAi. The bioimage analysis ap- be acquired: spatial (3D), time lapse (1D), Each of the analysis tasks is challenging, proaches, including object detection, and multiple fluorescent probes (1D). and different approaches are often re- segmentation, and tracking, are then With advances to automated high- quired for the analysis of different types of described. Subsequently, the quanti- resolution microscopy, fluorescent label- images. To facilitate image-based screen- tative features, phenotype identifica- ing, and robotic handling, image-based ing studies, a number of bioimage infor- tion, and multidimensional profile studies have become popular in drug and matics software packages have been de- analysis for profiling the effects of target discovery. These image-based stud- veloped and are publicly available [9]. drugsandtargetsaresummarized. ies are often referred to as the High This chapter provides an overview of the Moreover, a number of publicly Content Analysis (HCA) [3], which focus- bioimage informatics approaches in im- available software packages for bioimage informatics are listed for further reference. It is expected that Citation: Li F, Yin Z, Jin G, Zhao H, Wong STC (2013) Chapter 17: Bioimage Informatics for Systems this review will help readers, includ- Pharmacology. PLoS Comput Biol 9(4): e1003043. doi:10.1371/journal.pcbi.1003043 ing those without bioimage infor- Editors: Fran Lewitter, Whitehead Institute, United States of America and Maricel Kann, University of matics expertise, understand the Marylan Baltimore d, United County States , of America capabilities, approaches, and tools Published April 25, 2013 of bioimage informatics and apply Copyright: ß 2013 Li et al. This is an open-access article distributed under the terms of the Creative them to advance their own studies. Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The research is supported by NIH R01 LM008696, NIH R01 CA121225, NIH R01 LM009161, NIH R01 This article is part of the ‘‘Transla- A G028928 , NIH U54CA 1491 69 and CPRIT RP110532 . The funders had no role in the preparation of the manuscript. tional Bioinformatics’’ collection for Competing Interests: The authors have declared that no competing interests exist. PLOS Computational Biology. * E-mail: [email protected] PLOS Computational Biology | www.ploscompbiol.org 1 April 2013 | Volume 9 | Issue 4 | e1003043 What to Learn in This Chapter research. For example, the effects of hundreds of compounds were profiled for N What automated approaches are necessary for analysis of phenotypic changes, phenotypic changes using multicolor cell especially for drug and target discovery? images in [10–12]. Hundreds of quantitative features were extracted to indicate the N What quantitative features and machine learning approaches are commonly phenotypic changes caused by these com- used for quantifying phenotypic changes? pounds, and then computational ap- N What resources are available for bioimage informatics studies? proaches were proposed to identify the effective compounds, categorize them, age-based studies for drug and target analysis, and finally, a brief summary is characterize their dose-dependent re- discovery to help readers, including those provided in Section 7. sponse, and suggest novel targets and without bioimage informatics expertise, mechanisms for these compounds [10– understand the capabilities, approaches, 2. Example Image-based 12]. Moreover, phenotypic heterogeneity and tools of bioimage informatics and Studies for Drug and Target was investigated by using a subpopulation based approach to characterize drug apply them to advance their own studies. Discovery The remainder of this chapter is organized effects in [13], and distinguish cell popu- as follows. Section 2 introduces a number There are a variety of image-based lations with distinct drug sensitivities in of practical screening applications for studies for discovery of drugs, targets, and [14]. Also in [15,16], the phenotypic discovery of potential drugs and targets. mechanisms of biological processes. A good changes of proteins inside individual Section 3 describes the challenges and starting point for learning about bioimage Drosophila Kc167 cells treated with RNAi approaches for quantitative image analy- informatics approaches is to study practical libraries were investigated by using high sis, e.g., object detection, segmentation, image-based studies, and a number of resolution fluorescent microscopy, and and tracking. Section 4 introduces tech- examples are summarized below. bioimage informatics analysis was applied niques for quantification of segmented to quantify these images to identify genes objectives, including feature extraction, regulating the phenotypic changes of phenotype classification, and clustering. 2.1 Multicolor Cell Imaging-based interest. Figure 2 shows an image of Section 5 reviews a number of prevalent Studies for Drug and Target Drosophila Kc167 cells, which were treated approaches for profiling drug effects based Discovery with RNAi and stained to visualize the on the quantitative phenotypic data. Fixed cell images with multiple fluores- nuclear DNA (red), F-actin (green), and a- Section 6 lists major, publicly available cent markers have been widely used for tubulin (blue). Freely available software software packages of bioimage informatics drug and target screening in scientific packages, such as CellProfiler [17], Fiji Figure 1. The flowchart of bioimage informatics for drug and target discovery. doi:10.1371/journal.pcbi.1003043.g001 PLOS

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