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DARTS

DOI

overview

About

DARTS is an integrated tool originally designed for the analysis of Ca2+ microdomains in immune cells (Jurkat T cells, primary murine cells, NK). It is not limited to these data, but can also be used to analyze other intracellular signals in other cell types. Moreover, the global signal can me measured, too. For detailed information, see the Documentation.

It combines the following modules:

  • Postprocessing
    • channel registration
    • background subtraction
    • cell detection and tracking
    • deconvolution
    • bleaching correction
    • ratio computation
  • Shape Normalization
  • Hotspot Detection and Dartboard visualization (based on [2])

Most of these modules can be switched on or off, depending on the individual analysis (see Usage).

Installation

To install DARTS on your computer, a few steps need to be executed. Ideally, you are using a Mac computer with macOS Catalina (10.15) or higher and Intel processor. These settings have been tested extensively.

  • Install Python 3.10.0, following the instructions on the official website (https://www.python.org/downloads/release/python-3100/)
  • Install Anaconda (https://docs.anaconda.com/free/anaconda/install/index.html)
  • Install the latest git version via your terminal (see https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
  • In the terminal window, navigate to the folder where you want to save the required code.
  • Type git clone https://github.com/IPMI-ICNS-UKE/DARTS.git into the terminal window and press enter. The GitHub repository should now be cloned to your local machine. Alternatively, download the .zip file on the github-page.
  • In your file explorer, go to the folder "DARTS/src" and delete the (empty) folder 'TDEntropyDeconvolution'
  • In the terminal window, navigate to the subfolder "src" inside the DARTS folder
  • Type git clone https://github.com/IPMI-ICNS-UKE/TDEntropyDeconvolution.git to clone this module to your 'src' folder
  • In the terminal window, create a conda environment DARTS with a specific python version: conda create --name DARTS python=3.10.0
  • Activate the conda environment: conda activate DARTS
  • Install the necessary packages and their dependencies by executing this command in the terminal:
pip install matplotlib stardist trackpy tomli tensorflow alive-progress openpyxl pystackreg tkcalendar tomlkit simpleitk-simpleelastix

  • Alternatively, install the packages separately (pip install )

  • Install bioformats for python

For more information regarding the installation, see the Documentation

Update DARTS

How to update DARTS:

  1. Navigate to the DARTS folder in the terminal
  2. Activate the conda env: “conda activate DARTS”
  3. git checkout main
  4. git pull origin main

Usage

  1. Make sure that you navigated to the DARTS folder in the terminal. (To skip one folder layer up, use the command 'cd ../')
  2. DARTS is designed for the analysis of dual-channel fluorescence microscopy. Make sure, that the raw data are suitable (see Documentation)
  3. Store raw image files in a source directory. All common microscopy image formats can be opened, e.g. ics- or tif-files.
  4. Define whether it is a local measurement (interested in local hotspots) or just a global measurement (mean ratio over time).
  5. Run python main.py in the terminal/ shell/ powershell or IDE of your choice.
  6. Enter all the information in the GUI (see Documentation for more extensive explanation). Most of the information are crucial for the program to work properly. Then click on start. You can also save the settings to your local machine and access it later.
  7. Depending on the analysis mode (local/global, beads/no beads), you might be asked to provide information regarding the starting point (t=0) of the measurement for each file. For local measurements with beads, the starting point is the time of bead contact, for example. All cases are explained in the Documentation.
  8. Eventually, after providing all the information, DARTS automatically analyzes the data, putting out multiple data (explained in the Documentation).

Main

In this case, we decided to analyze the local hotspots in a measurement, where cells were stimulated with stimulatory antibody-coated beads. We now have to define the bead contacts, which consist of a position and time point as well as the information about the stimulated cell.

  1. Use the slider, to find the time of contact between a bead and a cell of interest. For a precise definition of the exact frame, click onto the sliding bar but outside the actual slider/box.
  2. In the option menu on the right hand side, select "bead contact: x, y, t"
  3. Click on the position in the left half of the image, where the contact between the cell and the bead contact is located at.
  4. Next, select "Choose cell by clicking a point inside". Click on the cell that is stimulated by this bead, preferably in the middle.
  5. Click on "ADD bead contact".
  6. Repeat the steps 1 - 5 for other bead contacts in this file. If you have defined all the bead contacts, go ahead and click on the "Continue"-button.
  7. Now, go ahead with the next files. If you have reached the last file, the script will automatically start with the analysis of all files.

Information: For each file, there might be several bead contacts. In order to save time, the time series will be cropped, so that the frames after the last starting point (e.g. bead contact at 600) + the measurement interval (e.g. 600 frames interval, so 1200 frames cutoff) are deleted as they are not needed.

Bead contacts

There are other cases, such as the hotspot detection without beads or global measurements with/without beads. These cases are explained in the Documentation.

License

This code runs under the Apache 2.0 license.

References and Citing

If DARTS is useful for a project that leads to publication, please acknowledge DARTS by citing it.

[1] Woelk L-M, Kovacevic D, Husseini H, Förster F, Gerlach F, Möckl F, Altfeld M, Guse AH, Diercks B-P and Werner R. DARTS: an open-source Python pipeline for Ca2+ microdomain analysis in live cell imaging data. Front. Immunol. 2024;14:1299435; doi: https://doi.org/10.3389/fimmu.2023.1299435

[2] Diercks BP, Werner R, Schetelig D, Wolf IMA, Guse AH. High-Resolution Calcium Imaging Method for Local Calcium Signaling. Methods Mol Biol. 2019;1929:27-39. doi: https://doi.org/10.1007/978-1-4939-9030-6_3