SParQ

SParQ (Streamlined Particle Quantification) is a plug-in for Fiji/ImageJ, a widely used scientific image processing software. The main features and benefits of SParQ are:

1. Automated quantification of vesicular or punctate structures in microscopy images, such as endosomes, autophagosomes, and lysosomes, commonly studied in protein transport and degradation research.

2. Streamlines the image analysis process by automating tasks typically done manually and individually for each image, such as adjusting and thresholding.

3. It allows the operator to evaluate and control all phases of the quantification process, ensuring the accuracy and reliability of the results.

4. Integrates seamlessly into the Fiji/ImageJ software, making it easily accessible to researchers familiar with this platform.

5. Enables the analysis of hundreds of structures under various conditions, significantly reducing the time and effort required for detailed analyses in protein homeostasis studies.

Topic

Imaging;Cell biology;Proteins

Detail

  • Operation: Quantification;Single particle alignment and classification;Phasing

  • Software interface: Command-line user interface

  • Language: Kotlin

  • License: GNU General Public License v2.0

  • Cost: Free of charge with restrictions

  • Version name: v0.3.0

  • Credit: American Heart Association, NIH.

  • Input: -

  • Output: -

  • Contact: Andreas Jenny andreas.jenny@einsteinmed.org

  • Collection: -

  • Maturity: -

Publications

  • Streamlined particle quantification (SParQ) plug-in is an automated fluorescent vesicle quantification plug-in for particle quantification in Fiji/ImageJ.
  • Mesquita A, et al. Streamlined particle quantification (SParQ) plug-in is an automated fluorescent vesicle quantification plug-in for particle quantification in Fiji/ImageJ. Streamlined particle quantification (SParQ) plug-in is an automated fluorescent vesicle quantification plug-in for particle quantification in Fiji/ImageJ. 2020; 16:1711-1717. doi: 10.1080/15548627.2019.1695400
  • https://doi.org/10.1080/15548627.2019.1695400
  • PMID: 31752589
  • PMC: PMC8386606

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