Year: 2013
Type: review
Abstract: Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
Source: PLoS Computational Biology
Institutions: Cornell University, Houston Methodist Research Institute
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G5520316217, Center for Systematic Modeling of Cancer Development, Cell tracking and analysis for time-lapse microscopy, Neuronal Spines Tracking and Analysis for Time-Lapse, 3D Optical Microscopy, Defining lineage-specific determinants of K-Ras "addiction" in human cancers, G7479764047, High-content Image Analysis and Modeling for Neuron Assay Based Screening, High-content Image Analysis and Modeling for RANigenome-wide Screening, Defining lineage-specific determinants of K-Ras "addiction" in human cancers
OpenAlex ID: W1977757945