Dataset DOI: 10.5061/dryad.dr7sqvbc0

Description of the data and file structure

Files and variables

File: CHST3_floxflox_genomic_sequence.dna

Description: 

This is a Snapgene file of the annotated genomic sequence of a Chst3 flox/flox mouse described in the associated publication 

File: CHST3_floxflox_genomic_sequence_text_version_ODT.odt

Description: 

This is a raw genomic sequence of a Chst3 flox/flox mouse described in the publication

File: Chase_in_pTRE_tight_sequence.dna

Description: 

This is a Snapgene file of annotated genomic sequence of a ChaseR-TRE mouse described in the publication

File: Chase_in_pTRE_tight_sequence_text_format_ODT.odt

Description: 

This is a raw genomic sequence of a ChaseR-TRE mouse described in the publication

File: Terminator_full_sequence.dna

Description: 

This is a Snapgene file of annotated genomic sequence of an OPC-Terminator ^fl/+ mouse described in the publication

File: Terminator_full_sequence_text_format_ODT.odt

Description: 

This is a raw genomic sequence of an OPC-Terminator ^fl/+ mouse described in the publication

File: DangDACSCounting_v2.m

Description: 

DACSCounting.mat is used to annotate all OPCs, proliferating OPCs, and DACS in 2D images. The code is annotated.

File: DangDACSAlign_v2.m

Description: 

DACSAlign.mat is used to display the six nearest OPCs to each DACS, as well as the closest proliferating OPC and its distance to the center of the DACS. The code is annotated. 

File: get_OPC_metadata.py

Description: 

Please place in the same folder with analysis and registration files. For a test set can use use test_xml, test_set_edges.csv, test_set_spots.csv

File: OPC_cell_class.py

Description: 

Please place in the same folder with analysis and registration files

File: parse_MaMuT_Mironova_et_al_2025.py

Description: 

Please use this code for analysis of your MaMuT annotated in vivo imaging datasets. Please note that this code is written specifically for parsing two-photon in vivo time-lapse imaging of the oligodendrocyte lineage, however, it can be potentially modified for other cell types. The code is annotated.

File: preprocess.py

Description: 

Please place in the same folder with analysis and registration files.

File: registration_functions.py

Description: 

Please place in the same folder with analysis and registration files.

File: plot_functions_OPC.py

Description: 

Please place in the same folder with analysis and registration files.

File: register_timepoints_Matn4_YM.py

Description: 

Please use for registration of xyzt hyperstacks to the vasculature channel. Please use registration_test_set.tif as a sample dataset. 

File: Test_set.xml

Description: 

This is a metadata file to test if parse_MaMuT_Mironova_et_al_2025.py and get_OPC_metadata.py pipelines run well. This xml file is generated by a BigDataViewer plugin in Fiji - please consult the MaMuT manual (link below) for more details on how this file can be generated from your data.

File: test_set_edges.csv

Description: 

This is an edges csv output generated from cell tracking in MaMuT. For your own experiments, you can generate this file by opening your MaMuT annotation and in the MaMuT viewer click Track tables - edges - export as csv (UTF-8). Please consult the MaMuT manual (link below) on how to generate your own MaMuT annotation. Please use this file to test if parse_MaMuT_Mironova_et_al_2025.py and get_OPC_metadata.py pipelines run well.

Please disregard NaN values as they don't influence the analysis in this sample dataset.

Variables

• LABEL: Label "d" denotes cells that disappear on the following frame, label "OL" denotes cells that differentiate into an oligodendrocyte in the following frame. 

• TRACK_ID:

• SPOT_SOURCE_ID:

• SPOT_TARGET_ID:

• LINK_COST:

• DIRECTIONAL_CHANGE_RATE:

• SPEED:

• DISPLACEMENT:

• EDGE_TIME:

• EDGE_X_LOCATION:

• EDGE_Y_LOCATION:

• EDGE_Z_LOCATION:

• MANUAL_EDGE_COLOR:

  For information on how these variables are generated, please consult a MaMuT plugin getting started (a link below). 

File: test_set_spots.csv

Description: 

This is an spots csv output generated from cell tracking in MaMuT. For your own experiments, you can generate this file by opening your MaMuT annotation and in the MaMuT viewer click Spot table - export as csv (UTF-8). Please consult the MaMuT manual (link below) on how to generate your own MaMuT annotation. Please use this file to test if parse_MaMuT_Mironova_et_al_2025.py and get_OPC_metadata.py pipelines run well.

Please disregard NaN values as they don't influence the analysis in this sample dataset.

Variables

• LABEL: Label "d" denotes cells that disappear on the following frame, label "OL" denotes cells that differentiate into an oligodendrocyte in the following frame. 

• ID:

• TRACK_ID:

• QUALITY:

• POSITION_X:

• POSITION_Y:

• POSITION_Z:

• POSITION_T:

• FRAME:

• RADIUS:

• VISIBILITY:

• CELL_DIVISION_TIME:

• SOURCE_ID:

• MANUAL_SPOT_COLOR:

• MEAN_INTENSITY_CH1:

• MEDIAN_INTENSITY_CH1:

• MIN_INTENSITY_CH1:

• MAX_INTENSITY_CH1:

• TOTAL_INTENSITY_CH1:

• STD_INTENSITY_CH1:

• CONTRAST_CH1:

• SNR_CH1:

  For additional information on how these variables are generated, please consult a MaMuT plugin getting started (a link below). 

File: registration_test_set.tif

Description: 

This is a sample in hypterstack image of time-lapse two-photon in vivo imaging of oligodendrocyte precursor cells and vasculature that can be use to test if register_timepoints_Matn4_YM.py pipeline runs well. 

File: Raw_values_Mironova_et_al_2025_updated.xlsx

Description: 

Values as reported in figures within the associated publication. Each sheet is labeled with a figure number and if there is more than one dataset per sheet, they are labeled with "A" "B" "C" etc according to how it is organized in the publication. All statistical values and units are reported in figure legends or in the main text of the publication.

File: Raw_absolute_values_Mironova_2025.xlsx

Description: 

Absolute values for the data reported in the publication where normalized data were reported, with sheets organized by figure numbers similar to "Raw_values_Mironova_et_al_2025". 

Code/software

For Python code:

1) Create and activate an environment (conda recommended):

conda create -n opc python=3.10 -y
conda activate opc

2) Install Python dependencies:

pip install numpy matplotlib natsort tifffile scikit-image pandas seaborn# 3) (Registration) Install ITKElastix (easiest path)

pip install itk-elastix

For more tutorials/info, see --> https://github.com/InsightSoftwareConsortium/ITKElastix/tree/master/examples

Please note - Python code was written for Linux.

For .dna files - Snapgene Viewer or any DNA sequence viewer software

For .xlsx files - any software compatible with a .xlsx format

For .mat files - MATLAB (basic MATLAB functions)

For generation of your own MaMuT annotated datasets - MaMuT plugin in Fiji

https://imagej.net/plugins/mamut/getting-started