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Related Concept Videos

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Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry
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rcell2: Microscopy-Based Cytometry in R.

Nicolás A Méndez1,2, German Beldorati1,2, Andreas Constantinou1,2

  • 1Department of Physiology, Molecular and Cellular Biology, School of Exact and Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.

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|April 19, 2023
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Summary
This summary is machine-generated.

This study introduces rcell2, an R package for quantifying cellular features and tracking cells in microscopy images. It enhances previous versions with integrated image processing and advanced cytometry analysis tools.

Keywords:
Cell-IDRfluorescence microscopyimage processing

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Area of Science:

  • Cellular biology
  • Microscopy
  • Bioinformatics

Background:

  • Accurate quantification of cellular features is crucial for understanding cell behavior.
  • Existing methods may lack integrated image processing and advanced analysis capabilities.

Purpose of the Study:

  • To introduce rcell2, an updated R package for comprehensive single-cell analysis from microscopy images.
  • To provide a unified software suite for cell segmentation, feature quantification, and time-course tracking.

Main Methods:

  • Utilizes defocused transmission (bright-field) images for cell segmentation and localization.
  • Analyzes fluorescence images from various microscopy techniques (wide-field, confocal).
  • Employs the rcell2 R package, integrating Cell-ID image processing and R's data analysis tools.

Main Results:

  • Quantifies diverse cellular features including volume, curvature, and fluorescence localization.
  • Enables tracking of individual cells across time-course microscopy experiments.
  • Offers enhanced cytometry data analysis tools compared to previous versions.

Conclusions:

  • rcell2 provides a robust and integrated platform for quantitative single-cell analysis.
  • The package facilitates advanced research in cell biology and related fields.
  • It leverages the power of the R statistical framework for data analysis and visualization.