JavaScript is required to consult this page

Genetically-encoded biosensors are heavily used in biology to monitor biological processes in real time within living cells. Many of such biosensors use two different emission wavelengths that change in opposite directions: the biological signal is measured as a change in the ratio of these two emissions. This so-called ratiometric quantification is standard and applies to many situations.
Ratioscope, which runs in the Igor Pro environment, offers two packages:

  • Ratio analysis of fluorescence images: image corrections, display in pseudocolor, measurements, kymograph, analysis of periodic activity, movies...
  • Fusion module: statistics on series of experiments, to compare different experimental conditions; generate editor-ready graphs and plots.

Other software

All software

3D Slicer

3D Slicer is: A software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. A free, open source software available on multiple operating systems: Linux, MacOSX and Windows Extensible, with powerful plug-in capabilities for adding algorithms and applications. Features include: Multi organ: from head to toe. Support for multi-modality imaging including, MRI, CT, US, nuclear medicine, and microscopy. Bidirectional interface for devices. There is no restriction on use, but Slicer is not approved for clinical use and intended for research. Permissions and compliance with applicable rules are the responsibility of the user.

Data analysis and visualisation

3DSpineMFE

A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.

Modelling and simulation

3DSpineS

Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.

Data analysis and visualisationData

Make the most out of EBRAINS

EBRAINS is open and free. Sign up now for complete access to our tools and services.

Ready to get started?Create your account