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Related Concept Videos

Flow Cytometry01:23

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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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Updated: Dec 2, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Agile workflow for interactive analysis of mass cytometry data.

Julia Casado1, Oskari Lehtonen1, Ville Rantanen1

  • 1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Bioinformatics (Oxford, England)
|November 2, 2020
PubMed
Summary
This summary is machine-generated.

Cyto is a new open-source tool for analyzing large single-cell cytometry datasets. It automates analysis and provides interactive visualization to uncover cell populations in blood and ovarian cancer samples.

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Area of Science:

  • Single-cell biology
  • Computational biology
  • Immunology

Background:

  • Single-cell proteomics technologies like mass cytometry generate large datasets.
  • Analyzing these complex datasets requires specialized, interactive tools for knowledge extraction.
  • Understanding cell-to-cell variation is crucial in biological research.

Purpose of the Study:

  • To present Cyto, a comprehensive and interactive method for streamlining large-scale cytometry data analysis.
  • To automate the application of state-of-the-art single-cell analysis techniques with interactive visualization.
  • To demonstrate the utility of Cyto in analyzing real-world biological samples.

Main Methods:

  • Cyto is an open-source, workflow-based solution.
  • It integrates automated single-cell analysis methods with interactive visualization.
  • The method was applied to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples.

Main Results:

  • Cyto reliably captures immune cell subpopulations from peripheral blood.
  • It identifies unique immune and cancer cell subpopulations in HGSOC tumor and ascites samples.
  • The interactive nature of Cyto facilitates the translation of complex data into biological insights.

Conclusions:

  • Cyto provides an efficient and effective platform for the analysis of large-scale cytometry data.
  • The tool enhances the characterization of cellular heterogeneity in various biological contexts.
  • Cyto is a valuable resource for researchers working with single-cell proteomics data.