ABSTRACT
Tissue clearing and fluorescent microscopy are powerful tools for unbiased organ-scale protein expression studies. Critical for interpreting expression patterns of large imaged volumes are reliable quantification methods. Here, we present DELiVR a deep learning pipeline that uses virtual reality (VR)-generated training data to train deep neural networks, and quantify c-Fos as marker for neuronal activity in cleared mouse brains and map its expression at cellular resolution. VR annotation significantly accelerated the speed of generating training data compared to conventional 2D slice based annotation. DELiVR detects cells with much higher precision than current threshold-based pipelines, and provides an extensive toolbox for data visualization, inspection and comparison. We applied DELiVR to profile cancer-related mouse brain activity, and discovered a novel activation pattern that distinguishes between weight-stable cancer and cancer-associated weight loss. Thus, DELiVR provides a robust mouse brain analysis pipeline at cellular scale that can be used to study brain activity patterns in health and disease.
The DELiVR software, Fiji plugin and documentation can be found at https://www.DISCOtechnologies.org/DELiVR/.
Highlights
DELiVR detects labelled cells in cleared brains with deep learning
DELiVR is trained by annotating ground-truth data in virtual reality (VR)
DELiVR is launched via a FIJI plugin anywhere from PCs to clusters
Using DELiVR, we found new brain activity patterns in weight-stable vs. cachectic cancer
HighlightsSupplementary Videos can be seen at: https://www.DISCOtechnologies.org/DELiVR/
Competing Interest Statement
The authors have declared no competing interest.
