Dataset DOI: 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).