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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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Related Experiment Video

Updated: Jun 18, 2026

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry
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Published on: June 10, 2025

Analysis of High-Throughput Flow Cytometry Data Using plateCore.

Errol Strain1, Florian Hahne, Ryan R Brinkman

  • 1FDA-Center for Food Safety and Nutrition, HFS-013 5100 Paint Branch Parkway, College Park, MD 20740, USA.

Advances in Bioinformatics
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

A new R/Bioconductor package, plateCore, automates high-throughput flow cytometry (FCM) data analysis. This tool ensures reproducible results comparable to manual gating, simplifying complex screening experiments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunology

Background:

  • R/Bioconductor packages like flowCore and flowViz offer open platforms for flow cytometry (FCM) analysis.
  • High-throughput FCM screening generates large datasets requiring efficient analysis tools.

Purpose of the Study:

  • To develop plateCore, an R/Bioconductor package extending existing FCM tools.
  • To enable automated negative control-based gating for plate-based FCM screening data.
  • To simplify processing and analysis of high-throughput FCM screening experiments.

Main Methods:

  • Developed the plateCore R/Bioconductor package.
  • Applied plateCore to analyze a high-throughput FCM screening dataset of human cell surface markers.
  • Compared plateCore automated analysis with manual analysis using FlowJo software.

Main Results:

  • Automated gating using plateCore demonstrated good agreement with manual gating by a cytometry expert.
  • Analysis using plateCore showed high reproducibility for FCM screening data.
  • plateCore facilitates easier processing and analysis of plate-based FCM datasets.

Conclusions:

  • plateCore enhances existing R/Bioconductor FCM packages for automated analysis.
  • The package provides a reproducible and efficient method for high-throughput FCM screening.
  • plateCore is a valuable tool for analyzing large-scale cell surface marker data.