SATINN: An automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

Abstract

MotivationThe mammalian testis is a complex organ with a hierarchical organization that changes smoothly and stereotypically over time in normal adults. While testis histology is already an invaluable tool for identifying and describing developmental differences in evolution and disease, methods for standardized, digital image analysis of testis are needed to expand the utility of this approach. ResultsWe developed SATINN (Software for Analysis of Testis Images with Neural Networks), a multi-level framework for automated analysis of multiplexed immunofluorescence images from mouse testis. This approach uses a convolutional neural network (CNN) to classify nuclei from seminiferous tubules into 7 distinct cell types with an accuracy of 94.2%. These cell classifications are then used in a second-level tubule CNN, which places seminiferous tubules into one of 7 distinct tubule stages with 90.4% accuracy. We further describe numerous cell- and tubule-level statistics that can be derived from wildtype testis. Finally, we demonstrate how the classifiers and derived statistics can be used to rapidly and precisely describe pathology by applying our methods to image data from two mutant mouse lines. Our results demonstrate the feasibility and potential of using computer-assisted analysis for testis histology, an area poised to evolve rapidly on the back of emerging, spatially-resolved genomic and proteomic technologies. Availability and implementationScripts to apply the methods described here are available from http://github.com/conradlab/SATINN.