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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

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Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
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Dynamic expression profiles from static cytometry data: component fitting and conversion to relative, "same scale"

Jayant Avva1, Michael C Weis, R Michael Sramkoski

  • 1Department of Electrical Engineering and Computer Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.

Plos One
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

This study presents a method to calculate dynamic epitope expression profiles during the cell cycle using cytometry data. This approach enables precise measurement of oscillating epitopes for cell cycle modeling.

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
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Area of Science:

  • Cell Biology
  • Biophysics
  • Computational Biology

Background:

  • Cytometry of proliferating cells reveals time-based features in event frequency.
  • Oscillating epitopes during the cell cycle present a challenge for dynamic expression analysis.

Purpose of the Study:

  • To present a general methodology for calculating dynamic expression profiles of cell cycle-dependent epitopes.
  • To convert epitope expression values to a standardized scale for comparison.

Main Methods:

  • Utilized K562 cell samples labeled for cyclins A2, B1, and phospho-S10-histone H3.
  • Employed Boolean gating and sequential regions on bivariate displays for unambiguous epitope value isolation.
  • Scaled S phase cyclin expressions from indirect assays to calibrate multi-variate direct assay data.

Main Results:

  • Dynamic expression profiles of cyclins A2 and B1 were captured using multi-line linear equations.
  • The method correlates median epitope values with the frequency of events within defined cell cycle regions.

Conclusions:

  • The presented approach offers a generalizable methodology for measuring cell cycle epitope expression.
  • The derived expression profiles serve as essential "state variables" for calibrating mathematical cell cycle models.