DOI: 10.5061/dryad.2bvq83c4n
title: Monosynaptic inputs to dopamine neurons in the ventral tegmental area and substantia nigra compacta and serotonin neurons in the dorsal raphe and median raphe

description: Monosynaptic inputs to serotonin neurons in the dorsal raphe (DR) and
 median raphe (MnR) were labeled in mice using the modified rabies virus
 SADdG-GFP (envA). The anatomical location and corresponding brain regions
 of labeled input neurons were systematically recorded and stored using
 MATLAB. This dataset includes the extracted input mapping data, along with
 the custom MATLAB codes used for data extraction, processing, and analysis
 of the retrograde tracing experiments.
description: Virus for Cre-dependent expression of receptor
 (AAV5-FLEX-TVA-mCherry) and rabies G protein (AAV8-FLEX-RG) were injected
 into the dorsal raphe (DR) or median raphe (MnR) of serotonin transporter
 (Sert)-Cre mice. 2 weeks later, pseudotyped modified rabies virus
 (SADdG-GFP(envA)) was injected at the same location. The brain was fixed
 with phosphate-buffered saline (PBS) and paraformaldehyde (PFA) serial
 perfusion, and cut into 100 μm coronal sections. Every 3 sections of the
 obtained brain sections were counter-stained with NeuroTrace Fluorescent
 Nissl Stains (Molecular Probes) and imaged with Zeiss AxioImager Z2 using
 a 10x lens. GFP-labeled neurons were manually registered onto brain areas
 based on Paxinos and Franklin’s atlas.
description: # Monosynaptic inputs to dopamine neurons in the ventral tegmental area
 and substantia nigra compacta, and serotonin neurons in the dorsal raphe
 and median raphe Dataset DOI:
 [10.5061/dryad.2bvq83c4n](https://doi.org/10.5061/dryad.2bvq83c4n) ##
 Description of the data and file structure Each zip file (e.g., DRf1.zip)
 contains all original TIF images and extracted data MAT files for each
 animal. “DRf*” is tracing from serotonin neurons in the dorsal raphe (DR),
 and “MRf*” is tracing from serotonin neurons in the median raphe (MnR).
 The original starter neurons and their monosynaptic inputs are labeled
 with green (GFP), and the Nissl counterstaining is labeled with red. The
 images are taken with an x10 objective lens, and one pixel in the MAT
 files corresponds to 2.6871 x 10^-3^ mm. ### Files and variables Data in
 zip folders #### Each .mat file contains one structure “analy”. analy.Rat:
 animal name analy.Section: brain section number analy.Bregma: the
 estimated anterior-posterior position to Bregma analy.neuron: x-y location
 of labeled neurons analy.ref1: x-y location of midline, 1mm deep from
 Bregma/lambda crossing point analy.ref2: x-y location of midline, 4mm deep
 from Bregma/lambda crossing point analy.area: structure for a contour of a
 brain area analy.area.xy: x-y locations of a contour of a brain area
 analyl.area.ID: a brain area number in the list (see below)
 analy.param.AreaList: a list of brain area abbreviation
 analy.param.FullNames: a list of brain area full name Other saved
 parameters are not related to further analyses. ## Code/software Code for
 data acquisition **inputome5** This is a generic code for data extraction
 in brain anatomy, used for retrograde tracing studies. It is used to
 manually draw contours of brain areas and manually label location of
 neurons using TIF images, and save both information as a simple matrix for
 further analyses. After labeling, it shows location of labelled neurons
 and contour of brain areas, overlaid on each original TIF image. This code
 may also update information to an existing dataset, such as adding new
 neurons, deleting existing neurons or modifying brain area, and save new
 data. “inputome5” is generic and can be used to convert any image data to
 matlab matrix data of x-y positions of user’s targets, such as labeling
 and counting cells in manually registered target locations. To run this
 code, first prepare a matlab file to list brain areas with abbreviation in
 “AreaList” and full name in “FullName” as a cell column. An example file
 for the current data “**Anatomy5**” is attached. 1\. Open “inputome5” in
 matlab. 2\. Choose a TIF file. Now TIF image is seen in matlab. 3\. Load
 MAT data by clicking “LoadData”. Now extracted neurons and brain areas are
 overlaid. Checking the checkbox for “Image”, ”Neurons”, “Neurons2”,
 “Areas”, and “Notes” to visualize selected information. Skip this step for
 a new analysis. 4\. Modify data by clicking “Neuron”, “Neurons2”, “Area”,
 “Notes”, “DeleteN”, “DeleteN2”, “DeleteA”, or “DeleteNotes”. Brightness
 and contrast are changed by “BRTN” and “CONT” for each color, although
 adjusting image brightness and contrast in other software before saving
 TIF images may be easier than changing those here every time. May also
 change animal name at “Rat”, section number at “Section”, hemisphere at
 “Hemisphere”, and bregma at “Bregma”. May replace reference points by
 “Ref1” and “Ref2”. 5\. Click “Save” to save new data. 6\. Click
 “Initialize” to move to the next image. Analysis codes **extract_neuron**
 This code outputs information of each input neuron in a single image. 1\.
 Load “analy” from .mat data file (see above). 2\. Run
 “extract_neuron(analy)” to output information. x: original x
 (medio-lateral) location of input neurons in the image y: original y
 (dorso-ventral) location of input neurons in the image bregma:
 anterior-posterior location of the section, referenced to Bregma section:
 section number of the section in each animal x01: x (medio-lateral)
 location of the reference point, 1mm deep from Bregma/lambda crossing
 point on the midline y01: y (dorso-ventral) location of the reference
 point, 1mm deep from Bregma/lambda crossing point on the midline x02: x
 (medio-lateral) location of the reference point, 4mm deep from
 Bregma/lambda crossing point on the midline y02: y (dorso-ventral)
 location of the reference point, 4mm deep from Bregma/lambda crossing
 point on the midline area: the brain area of each input neuron doub: a
 flag for duplicated registration to multiple brain areas **plot_neuron3**
 This code plots input neurons overlaid on selected tif images. 1\. In the
 code, choose animal folders at lines 24-26 and lines 32-34, and section
 numbers for each animal at line 15. Choose plot_area = 1 at line 18 to
 plot brain areas. 2\. Run the code to compare input distribution.
 **filter_neuron_data** This code plots a density plot of input neurons
 overlaid on brain areas. 1\. Load data “analy” (see above). 2\. Run
 “filter_neuron_data(analy)”. **extract_neuron_all** This code plots a 3D
 plot of all input neurons and inputs in selected brain areas. 1\. Choose
 brain areas to plot in the code at line 54. 2\. Run the code. 2\. Choose
 *.mat data file. 3\. This will output 3D plot of all inputs in all files
 in the same folder. **summary_inputome3** This code plots summary graphs
 of neuron number and neuron density in each brain area in multiple animals
 (mean +- standard error) and outputs the exact numbers. 1\. In the code,
 choose animal folders for *.mat data files at lines 4-8 and data folder
 for “Anatomy5.mat” at line 11. 2\. Run the code “[N,A] =
 summary_inputome3” to get a summary figure (replicate Figure 3 in *Neuron*
 2012 by Watabe-Uchida et al.) and two matrices of exact neuron numbers “N”
 and densities “A” for each brain area (row) and each animal (column).