Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes.
A computational framework to annotate complex cellular dynamics. A machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states was developed. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.
Uses a computational multiplexed image cytometry analysis toolbox to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. miCAT will be useful in all areas of tissue-based research.
A free, open-source system designed for flexible, high-throughput cell image analysis. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
Allows representation of multidimensional cellular measurements. PhenoPlot is a toolbox that permits visualization of up to 21 variables. It may be useful for determining the morphology of breast cancer cell lines or for understanding and interpreting multidimensional cellular imaging data. To assist users, this tool employs many visual elements such as differently sized, coloured and structured objects. It provides effective and intuitive pictorial representations of cellular phenotypes.
Allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact. The user interface allows the members of the community without any computational knowledge to extract meaningful information from the data. The web interface can be used for querying the data and the results are visualized as plots (e.g. scatter plot, histogram) in real-time. Mineotaur is based on a novel data model allowing the visualization and analysis of extremely large amounts of data.
Allows high-throughput single cell morpho-rheological (MORE) characterization of all major blood cells in continuous flow. MORE software analysis allows individual blood cell mechanics to be studied in a range of human diseases. Label-free, disease-specific MORE blood signatures are a novel resource for generating hypotheses about the underlying molecular mechanisms. In addition, MORE software analysis has the potential to become a standard approach in flow cytometry with many applications in biology, biophysics, biotechnology and medicine.
A software for biological image-based classification, data exploration, and visualization designed for biologists and data scientists. CellProfiler Analyst builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).
A generic workflow for the study of single cell motility in high-throughput time-lapse screening data. MotIW is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.
An HDF5 data format for cell-based assays in high-throughput microscopy, which stores high-dimensional image data along with inter-object relations in graphs. CellH5Browser, an interactive gallery image browser, demonstrates the versatility and performance of the file format on live imaging data of dividing human cells. CellH5 provides opportunities for integrated data analysis by multiple software platforms.
Consists of a high-throughput single-cell imaging and image processing protocol. TIMING conducts large-scale automated video array investigations. It can construct quantitative analyses of cell interaction behaviors at single-cell resolution with visual confirmation. This tool counts the cells in each nanowell, finds their types based on cell-type markers, and provides measurements of size, location, shape, and movements of cells.
Categorizes cells according to their primary phenotypic features. treeHFM can model all relevant events in a cell’s life including cell division. It enables the analysis of event order and cell fate heterogeneity. This program can be used for investigating the differentiation process of blood progenitor cells, and in the analysis of heritable diseases.
An extensible, automated system for high throughput screening and non-perturbing optical single cell analysis. The analytical capabilities of LSDCAS include automatic cell motility analysis, and the detection and analysis of cell events such as normal and abnormal cell division and cell death. LSDCAS also provides automatic assays for cell proliferation and wound healing. The LSDCAS technology is current being developed as a tool for cancer drug discovery.
Allows users to directly compare wild-type and mutant embryos in a dynamic, intuitive manner. With IEV, users can navigate the volumes in synchrony by using the sliders, mouse wheel or crosshair tool (shift key while moving the mouse). The settings panel provides options to toggle viewports on/off, configure the scale bar, zoom in/out, create bookmarks and switch modality. Each viewer has its own drop-down box listing the embryos available to view, an image contrast slider, and the ability to link/unlink individual viewports.
An open-source, cross-platform application. HARP is designed to operate on 3D image data represented as two-dimensional (2D) slices in a variety of formats (tiff, bmp, jpg, etc.). Although it was primarily designed for tomographic images of embryos, it can in practice be used for other imaging modalities, specimen types and fields of study. HARP provides options to automatically crop image volumes to remove any undesirable empty space around the specimens.
Represents a generic novelty detection and deep learning framework which can enables sensitive and accurate cellular phenotype detection. CellCognition Explorer serves for integrated data analysis from raw images to phenotype scores and consists of two programs: (1) the principal CellCognition Explorer permits interactive data visualization tools and the possibility to perform versatile analysis workflows, (2) and the CellCognition Deep Learning Module which is a separate program for graphics processing unit accelerated high-performance computing of deep learning features.
Allows image analysis. TRACMIT permits to automate the chromatin feature extraction of mitotic cells grown on micropatterns. It allows screening of live imaging data sets for novel candidates regulating mitotic processes, such as spindle positioning. The tool can select micropatterns that contain exactly one cell, while incorporating pre-processing, tracking, data filtering and visual validation. It offers a way to monitor and quantify cell division features.
Offers access to a variety of advanced machine learning methods and provides an accurate high-content screen analysis. Advanced Cell Classifier (ACC) contains two features: (1) it is not limited to a specific classifier, (2) can detect even very small phenotype changes, allowing to identify not only the main but also subphenotypes. Its system permits to work with a graphical user interface (GUI) to create training database for classification, an immediate image classification, a plate browser, a report generation, and a numerous classification algorithms.
It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. The customizability of BIOCAT is expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation.
A free and open-source tool that complements the object tracking functionality of the CellProfiler biological image analysis package. CellProfiler Tracer allows multi-parametric morphological data to be visualized on object tracks, providing visualizations that have already been validated within the scientific community for time-lapse experiments, and combining them with simple graph-based measures for highlighting possible tracking artifacts.