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Anti-TRBC1 Antibody-Based Flow Cytometric Detection of T-Cell Clonality: Standardization of Sample Preparation and Diagnostic Implementation.
- Source :
-
Cancers [Cancers (Basel)] 2021 Aug 30; Vol. 13 (17). Date of Electronic Publication: 2021 Aug 30. - Publication Year :
- 2021
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Abstract
- A single antibody (anti-TRBC1; JOVI-1 antibody clone) against one of the two mutually exclusive T-cell receptor β-chain constant domains was identified as a potentially useful flow-cytometry (FCM) marker to assess Tαβ-cell clonality. We optimized the TRBC1-FCM approach for detecting clonal Tαβ-cells and validated the method in 211 normal, reactive and pathological samples. TRBC1 labeling significantly improved in the presence of CD3. Purified TRBC1 <superscript>+</superscript> and TRBC1 <superscript>-</superscript> monoclonal and polyclonal Tαβ-cells rearranged TRBJ1 in 44/47 (94%) and TRBJ1+TRBJ2 in 48 of 48 (100%) populations, respectively, which confirmed the high specificity of this assay. Additionally, TRBC1 <superscript>+</superscript> /TRBC1 <superscript>-</superscript> ratios within different Tαβ-cell subsets are provided as reference for polyclonal cells, among which a bimodal pattern of TRBC1-expression profile was found for all TCRVβ families, whereas highly-variable TRBC1 <superscript>+</superscript> /TRBC1 <superscript>-</superscript> ratios were observed in more mature vs. naïve Tαβ-cell subsets (vs. total T-cells). In 112/117 (96%) samples containing clonal Tαβ-cells in which the approach was validated, monotypic expression of TRBC1 was confirmed. Dilutional experiments showed a level of detection for detecting clonal Tαβ-cells of ≤10 <superscript>-4</superscript> in seven out of eight pathological samples. These results support implementation of the optimized TRBC1-FCM approach as a fast, specific and accurate method for assessing T-cell clonality in diagnostic-FCM panels, and for minimal (residual) disease detection in mature Tαβ <superscript>+</superscript> leukemia/lymphoma patients.
Details
- Language :
- English
- ISSN :
- 2072-6694
- Volume :
- 13
- Issue :
- 17
- Database :
- MEDLINE
- Journal :
- Cancers
- Publication Type :
- Academic Journal
- Accession number :
- 34503189
- Full Text :
- https://doi.org/10.3390/cancers13174379