Monosynaptic inputs to ventral tegmental area glutamate and GABA co-transmitting neurons.

A unique population of ventral tegmental area (VTA) neurons co-transmits glutamate and GABA as well as functionally signals rewarding and aversive outcomes. However, the circuit inputs to VTA VGluT2+VGaT+ neurons are unknown, limiting our understanding of the functional capabilities of these neurons. To identify the inputs to VTA VGluT2+VGaT+ neurons, we coupled monosynaptic rabies tracing with intersectional genetic targeting of VTA VGluT2+VGaT+ neurons in mice. We found that VTA VGluT2+VGaT+ neurons received diverse brain-wide inputs. The largest numbers of monosynaptic inputs to VTA VGluT2+VGaT+ neurons were from superior colliculus, lateral hypothalamus, midbrain reticular nucleus, and periaqueductal gray, whereas the densest inputs relative to brain region volume were from dorsal raphe nucleus, lateral habenula, and ventral tegmental area. Based on these and prior data, we hypothesized that lateral hypothalamus and superior colliculus inputs were glutamatergic neurons. Optical activation of glutamatergic lateral hypothalamus neurons robustly activated VTA VGluT2+VGaT+ neurons regardless of stimulation frequency and resulted in flee-like ambulatory behavior. In contrast, optical activation of glutamatergic superior colliculus neurons activated VTA VGluT2+VGaT+ neurons for a brief period of time at high stimulation frequency and resulted in head rotation and arrested ambulatory behavior (freezing). For both pathways, behaviors induced by stimulation were uncorrelated with VTA VGluT2+VGaT+ neuron activity. However, stimulation of glutamatergic lateral hypothalamus neurons, but not glutamatergic superior colliculus neurons, was associated with VTA VGluT2+VGaT+ footshock-induced activity. We interpret these results such that inputs to VTA VGluT2+VGaT+ neurons may integrate diverse signals related to the detection and processing of motivationally-salient outcomes. Further, VTA VGluT2+VGaT+ neurons may signal threat-related outcomes, possibly via input from lateral hypothalamus glutamate neurons, but not threat-induced behavioral kinematics.

A Semi-Automated Workflow for Brain Slice Histology Alignment, Registration, and Cell Quantification (SHARCQ).

Tools for refined cell-specific targeting have significantly contributed to understanding the characteristics and dynamics of distinct cellular populations by brain region. While advanced cell-labeling methods have accelerated the field of neuroscience, specifically in brain mapping, there remains a need to quantify and analyze the data. Here, by modifying a toolkit that localizes electrodes to brain regions (SHARP-Track; Slice Histology Alignment, Registration, and Probe-Track analysis), we introduce a post-imaging analysis tool to map histological images to established mouse brain atlases called SHARCQ (Slice Histology Alignment, Registration, and Cell Quantification). The program requires MATLAB, histological images, and either a manual or automatic cell count of the unprocessed images. SHARCQ simplifies the post-imaging analysis pipeline with a step-by-step GUI. We demonstrate that SHARCQ can be applied for a variety of mouse brain images, regardless of histology technique. In addition, SHARCQ rectifies discrepancies in mouse brain region borders between atlases by allowing the user to select between the Allen Brain Atlas or the digitized and modified Franklin-Paxinos Atlas for quantifying cell counts by region. SHARCQ produces quantitative and qualitative data, including counts of brain-wide region populations and a 3D model of registered cells within the atlas space. In summary, SHARCQ was designed as a neuroscience post-imaging analysis tool for cell-to-brain registration and quantification with a simple, accessible interface. All code is open-source and available for download (https://github.com/wildrootlab/SHARCQ).