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Detection of cell specific cluster determinant expression by reverse transcriptase polymerase chain reaction
M James-Yarish1, W G Bradley, P J Emmanuel
1Department of Pediatrics, All Children's Hospital, University of South Florida, St. Petersburg 33701.
Journal of Immunological Methods
|February 28, 1994
Summary
A new reverse transcriptase polymerase chain reaction (RT-PCR) method offers sensitive gene expression analysis for cell surface markers. This technique detected B cell markers in a patient previously thought to lack them, improving diagnostic capabilities.
Area of Science:
- Molecular Biology
- Immunology
- Biochemistry
Background:
- Flow cytometry is commonly used for detecting cell surface markers.
- Gene expression analysis of cell determinants is crucial in immunology.
- Existing methods may lack sensitivity for certain cell populations.
Purpose of the Study:
- To develop a novel, sensitive, and reproducible method for analyzing gene expression of cell surface determinants.
- To compare the new method with traditional flow cytometry.
- To demonstrate the utility of the new method in a clinical case.
Main Methods:
- Utilized reverse transcriptase polymerase chain reaction (RT-PCR) for gene expression analysis.
- Quantified PCR products using radiolabeled nucleotides and ion exchange filter chromatography.
- Applied the method to human cell lines, peripheral blood lymphocytes, bone marrow, and lymph node cells.
Main Results:
- The RT-PCR method demonstrated high sensitivity and reproducibility for analyzing CD gene expression.
- Analysis of a B cell deficient patient revealed CD19, CD20, and CD23 expression via RT-PCR, which was not detected by flow cytometry.
- Results from RT-PCR and flow cytometry were largely similar, but RT-PCR offered enhanced detection capabilities.
Conclusions:
- RT-PCR provides a sensitive alternative for detecting cell surface marker gene expression.
- This method can identify cell populations missed by conventional techniques, aiding in diagnosing immune deficiencies.
- The described methodology enhances the understanding of gene expression in various human cell types.