6533b86cfe1ef96bd12c8b4f

RESEARCH PRODUCT

Automated analysis of images for molecular quantification in immunohistochemistry.

Esther Castillo-gómezHector CarcellerEero CastrénRamon EguiradoJuan Nacher

subject

EXPRESSION0301 basic medicineComputer scienceBioinformaticsQuantitative proteomicsSEGMENTATIONAutomatic thresholdMATURATIONArticle03 medical and health sciences0302 clinical medicineANTIBODY CONCENTRATIONSegmentationlcsh:Social sciences (General)Software analysis patternlcsh:Science (General)Spatial analysisMultidisciplinarybusiness.industry3112 NeurosciencesPattern recognitionFluorescence intensity030104 developmental biologyImmunohistochemistrylcsh:H1-99Neuroscience researchArtificial intelligencebusiness030217 neurology & neurosurgerylcsh:Q1-390Neuroscience

description

The quantification of the expression of different molecules is a key question in both basic and applied sciences. While protein quantification through molecular techniques leads to the loss of spatial information and resolution, immunohistochemistry is usually associated with time-consuming image analysis and human bias. In addition, the scarce automatic software analysis is often proprietary and expensive and relies on a fixed threshold binarization. Here we describe and share a set of macros ready for automated fluorescence analysis of large batches of fixed tissue samples using FIJI/ImageJ. The quantification of the molecules of interest are based on an automatic threshold analysis of immunofluorescence images to automatically identify the top brightest structures of each image. These macros measure several parameters commonly quantified in basic neuroscience research, such as neuropil density and fluorescence intensity of synaptic puncta, perisomatic innervation and col-localization of different molecules and analysis of the neurochemical phenotype of neuronal subpopulations. In addition, these same macro functions can be easily modified to improve similar analysis of fluorescent probes in human biopsies for diagnostic purposes based on the expression patterns of several molecules. Peer reviewed

10.1016/j.heliyon.2018.e00669https://pubmed.ncbi.nlm.nih.gov/30003163