Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

/pdf/fiji-an-open-source-platform-for-biological-image-analysis-1v529iu3cx.pdf

Fiji: an open-source platform for biological-image analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

/pdf/ilastik-interactive-machine-learning-for-bio-image-analysis-57nb1zt342.pdf

ilastik: interactive machine learning for (bio)image analysis.

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein
 interaction data from model organisms and humans
 (http://www.thebiogrid.org). BioGRID currently holds 347 966
 interactions (170 162 genetic, 177 804 protein) curated from both
 high-throughput data sets and individual focused studies, as derived
 from over 23 000 publications in the primary literature. Complete
 coverage of the entire literature is maintained for budding yeast
 (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe)
 and thale cress (Arabidopsis thaliana), and efforts to expand curation
 across multiple metazoan species are underway. The BioGRID houses 48
 831 human protein interactions that have been curated from 10 247
 publications. Current curation drives are focused on particular areas
 of biology to enable insights into conserved networks and pathways that
 are relevant to human health. The BioGRID 3.0 web interface contains
 new search and display features that enable rapid queries across
 multiple data types and sources. An automated Interaction Management
 System (IMS) is used to prioritize, coordinate and track curation
 across international sites and projects. BioGRID provides interaction
 data to several model organism databases, resources such as Entrez-Gene
 and other interaction meta-databases. The entire BioGRID 3.0 data
 collection may be downloaded in multiple file formats, including PSI MI
 XML. Source code for BioGRID 3.0 is freely available without any
 restrictions.

/pdf/the-biogrid-interaction-database-2011-update-4t23jtqdsl.pdf

The BioGRID Interaction Database: 2011 update

Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.

https://rupress.org/jcb/article-pdf/189/5/777/1346115/jcb_201004104.pdf

Metadata matters: access to image data in the real world

Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

/pdf/phenotypic-profiling-of-the-human-genome-by-time-lapse-2771sjuqmi.pdf

Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes

Quantitative microscopy relies on imaging of large cell numbers but is often hampered by time-consuming manual selection of specific cells. The 'Micropilot' software automatically detects cells of interest and launches complex imaging experiments including three-dimensional multicolor time-lapse or fluorescence recovery after photobleaching in live cells. In three independent experimental setups this allowed us to statistically analyze biological processes in detail and is thus a powerful tool for systems biology.

/pdf/micropilot-automation-of-fluorescence-microscopy-based-2qq7oo250d.pdf

Micropilot: automation of fluorescence microscopy–based imaging for systems biology

Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.

/pdf/automatic-analysis-of-dividing-cells-in-live-cell-movies-to-5go2pjw3mt.pdf

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

Automated screening platforms allow biologists to acquire large amounts of image data with high information content. However, reliable automatic methods for analyzing this data are often not available. Here, we present an approach for detailed cell cycle analysis based on live cell fluorescence microscopy image sequences. Our approach comprises segmentation and tracking of dividing cell nuclei, and classifies cells into seven cell cycle phases as well as five abnormal morphological phenotypes. Moreover, we automatically quantify cell cycle phase durations and perform a statistical analysis to determine temporal phenotypes. Our approach was successfully applied to images from gene knockdown experiments and experiments treated with small molecule drugs.

/pdf/automatic-analysis-of-live-cell-image-sequences-to-determine-z9gssxeqfk.pdf

Annelie Wünsche

Papers

Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes

Micropilot: automation of fluorescence microscopy–based imaging for systems biology

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

Automatic Analysis of Live Cell Image Sequences to determine Temporal Mitotic Phenotypes.