Deep learning based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra.
Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilized DLAP to analyse subtypes of tyrosine hydroxylase (TH)-positive dopaminergic midbrain neurons in mouse and human brain sections. These neurons modulate complex behaviour, and are differentially affected in Parkinsons and other diseases. DLAP allows the analysis of large cell numbers, and facilitates the identification of small cellular subpopulations. Specifically, we identified a small subpopulation of TH-positive neurons ([~]5%), mainly located in the very lateral Substantia nigra (SN), that was immunofluorescence-negative for the plasmalemma dopamine transporter (DAT), with [~]40% smaller cell bodies. These neurons were negative for aldehyde dehydrogenase 1A1, with a lower co-expression rate for dopamine-D2-autoreceptors, but a [~]7-fold higher likelihood of calbindin-d28k co-expression ([~]70%). Our results have important implications, as DAT is crucial for dopamine-signalling, and is commonly used as a marker for dopaminergic SN neurons.