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Updated: May 24, 2026

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Published on: February 19, 2020

Dynamic epitope expression from static cytometry data: principles and reproducibility.

James W Jacobberger¹, Jayant Avva, Sree N Sreenath

¹Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, United States of America. jwj@case.edu

|February 21, 2012

Summary

This summary is machine-generated.

This study introduces a new method using multiparametric flow cytometry to precisely quantify multiple cell cycle proteins and their modifications. This enables accurate cell cycle modeling and understanding of protein dynamics.

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Area of Science:

Cell Biology
Biophysics
Systems Biology

Background:

Accurate quantification of cell cycle protein dynamics is crucial for understanding cell cycle regulation.
Current methods lack the precision and scope for comprehensive analysis of multiple proteins and modifications.
Mathematical modeling of the cell cycle requires precise, quantitative data for validation and enhancement.

Purpose of the Study:

To develop an open-ended methodology for precise quantification of multiple cell cycle proteins and their modifications.
To provide accurate expression profiles for cell cycle regulators and their post-translational modifications.
To generate data suitable for calibrating and validating mathematical models of the cell cycle.

Main Methods:

Multiparametric flow cytometry was employed on Molt4 cells.
Measurements included cyclins A2 and B1, phospho-S10-histone H3, DNA content, and cell size.
N-dimensional data analysis using sequential, unidirectional regions to generate expression profiles.

Main Results:

The methodology demonstrated high reproducibility across triplicate experiments.
Precise mapping of sequential cyclin A2 and B1 degradation and histone H3 dephosphorylation was achieved.
A two-phase expression rate during interphase for each cyclin was robustly identified.

Conclusions:

Precise, correlated expression profiles of cell cycle proteins and modifications can be generated, limited only by antibody availability.
These profiles are valuable for creating large information libraries.
The data generated can be used for calibration and validation of mathematical cell cycle models.