Imagerie-Gif Cytometry core facility : measure the fluorescence of your cells with a great statistical power
Our cytometers allow analyzing and/or sorting different cell populations according to their morphology and their fluorescence. Even rare population can be studied thanks to the statistical power of this technology. The facility is hosted within the Institute for Integrative Biology of the Cell (I2BC), which gives it a strong expertise in the study of different biological models: animal cells, plant cells, bacteria, yeasts, organelles… In order to allow users to make the best use of the data obtained with the cytometers, they are also supported in data processing and analysis. Powerful computing stations equipped with dedicated software are made available to them. The Imagerie-Gif facilities can manage private sector projects as it is certified according to ISO 9001:2015/NFX 50-900:2016 standards.
Fluorescent biosensors are molecules which, once introduced into living cells, yield a specific biological signal that can be measured by imaging or cytometry. The sensitivity and high throughput of flow cytometry allow the development of a whole panel of additional analyzes essential to understanding the observed biological process. The differences in physiological states observed in imaging can then be quantified in cytometry. Moreover, cytometry allows multiparametric analyzes offering the possibility of measuring several activities simultaneously cell by cell. It is thus easy to compare the results obtained, to deduce their interdependence or not, which makes it an essential tool for the study of cell signaling.
See also Biosensors Imaging in Light Microscopy section
Automated data analyzes
Cytometry allows analyzing hundred of samples within thousands of events. Data analysis can be made manually with software dedicated to cytometry, but the task can quickly become difficult and time-consuming. The facility aims data analysis, by writing experiment-specific scripts with R coding. You can fit your fluorescence histograms on Gaussian models, your kinetic curves on sigmoid models, you can identifying different population by clustering algorithms according to several parameters…