High content screening

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

EBImage is available through the Bioconductor software project (www.bioconductor.org). Strengths Lightweight Suitable for automated, scripted analyses All functions are documented with examples Modular links to R and Bioconductor software, notably imageHTS and cellHTS2 Community support via the Bioconductor mailing list Reproducible (image) analysis using the Sweave report-writing system

Workflow of data-analysis strategies for image-based cell profiling.

1. Image analysis (image correction, image segmentation, feature extraction)
2. Image quality control
3. Preprocessing extracted features
4. Dimensionality reduction
5. Single-cell data aggregation
6. Measuring profile similarity
7. Assay quality assessment
8. Downstream analysis

Examples of publications where the workflow has been used at least partly:

• Image-based multivariate profiling of drug responses from single cells
• Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes
• Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping
• Cell shape and the microenvironment regulate nuclear translocation of NF‐κB in breast epithelial and tumor cells
• Genome-wide RNAi Screening Identifies Protein Modules Required for 40S Subunit Synthesis in Human Cells
• Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
• Systematic morphological profiling of human gene and allele function via Cell Painting

CIDRE is a retrospective illumination correction method for optical microscopy. It is designed to correct collections of images by building a model of the illumination distortion directly from the image data. Larger image collections provide more robust corrections. Details of the method are described in

K. Smith, Y. Li, F. Ficcinini, G. Csucs, A. Bevilacqua, and P. Horvath
CIDRE: An Illumination Correction Method for Optical Microscopy, Nature Methods 12(5), 2015, doi:10.1038/NMETH.3323

The linked webpage presents a collection of ImageJ macros for Intelligent Imaging (Feedback to microscope system for the secondary scan). 

An ImageJ macro able to control some microscopes (Micro-manager or Leica CAM controlled) to acquire high resolution images of only some structures (e.g. isolated cells) or events (e.g. mitosis) within a sample. The scan is sequenced as a primary (low resolution monitoring) scan and a secondary (high resolution, multi-dimensional) scan.