Subcellular protein distribution software tools | Laser scanning microscopy image analysis

Assists in analysing cell shape and motion. 3D cell shape and migration allows users to study the correlation between these shape and motion parameters and subcellular fluorescence localization. It is based on a 3D Gaussian partial-derivative kernel surface filtering algorithm. This method is combined with a self-adjusting high intensity threshold.

Provides a method to determinate automatically and unbiasedly distributions of protein across cellular compartments. PatternUnmixer is built on a machine-learning approach that calculates the amount of fluorescent signal in different subcellular compartments without the need of hand tuning and by only requiring the acquisition of separate training images of markers for each compartment. This software suits for high-throughput microscopy and works well on real images obtained form mixed patterns.

Allows quantification of clathrin-coated pit dynamics from fluorescence time-lapse data. cmeAnalysis provides functionalities including: (1) sensitive detection, (2) tracking (based on u-track), (3) master/slave detection for multi-channel data, (4) intensity-based classification of coated structures, and (5) lifetime analysis. It also contains a graphical user interface (GUI) for inspection of analysis results from individual movies.

Performs 2D immunofluorescence images to pinpoint and number synaptic protein puncta. SynPAnal provides an application that intends to partially automate processing tasks of files generated by confocal laser scanning microscopy. The application focuses on the quantification of puncta attributes but can also be applied for basic fluorescent intensity measuring as well as for basic morphometric analysis of neurons.

Computational methods were developed to automatically analyze the images created by the University of California, San Francisco (UCSF) yeast GFP fusion localization project. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.

A cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. We evaluate S-PSorter on a collection of 1,636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that S-PSorter achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method.

An automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization images based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments.

Allows analysis of a range of parameters measured in 3D cell culture based on 2D images. PCaAnalyser is an automated image-analysis based software developed as an ImageJ plugin. The software enables high-throughput analysis of images acquired from cells grown in a 3D matrix. It is able to reproducibly analyze immuno-staining of different markers known to be involved in cancer progression including CXCR4, α6 and β1 integrin subunits.

Provides tools for learning generative models of cell organization directly from images, storing and retrieving those models in XML files and synthesizing cell images (or other representations) from one or more models.

Aims to analyze the dynamics of macromolecular assemblies with high spatial and temporal resolution. QFSM allows users to study spatial and temporal relations between the formation, turnover, and mechanical outputs of the filament network. It assists in identifying and tracking speckles and utilizes their location, appearance, and disappearance to derive network flows and assembly/disassembly maps.