- Open Access
Relative quantification of TCR Vbeta-chain families by real time PCR for identification of clonal T-cell populations
© Ochsenreither et al; licensee BioMed Central Ltd. 2008
- Received: 18 February 2008
- Accepted: 01 July 2008
- Published: 01 July 2008
Quantification of T-cell receptor (TCR) chain families can be utilized for detection of clonal T-cell populations. Besides southern blotting and antibody-based approaches, quantitative real time PCR (qRT PCR) has been more widely applied in this context during the last years. Here, the heterogeneity of sequences within single families is the most challenging problem for exact quantification.
Vβ-families were quantified using a universal reverse primer and family-specific forward primers with TaqMan technology on a light cycler instrument. Relative concentrations were calculated considering slopes and crossing points of each PCR reaction. Total expression of α/β TCR was assessed by quantification of the constant α-chain as a further control.
The method was tested by serial dilutions of clonal T-cells in mononuclear cells from healthy volunteers. Calculated percentages were in good correspondence with qRT PCR results demonstrating high reliability. Duplicates showed excellent technical reproducibility. We analyzed blood samples of 20 healthy volunteers for determination of mean and standard deviation for each family. The method was applied both to tissue and blood samples from patients with carcinomas and hematological disorders.
We introduce a versatile method for the relative quantification of Vβ-families by real time PCR. The experimental strategy described allows the identification of alterations in the Vβ-family repertoire.
- JURKAT Cell
- Fluorescence Activate Cell Sorter
- Prolymphocytic Leukemia
- Universal Reverse Primer
- Standard Dilution Curve
T-lymphocytes are specialized mediators of the adaptive immune system, selectively destroying cells altered by viral infection or malignant transformation [1, 2]. T-cell mediated immune responses are characterized by activation and subsequent clonal expansion of antigen-specific cells. Recognition of Major Histocompatibility Complex class I (MHC-I) bound peptide is mediated by the dimeric transmembrane T-cell receptor (TCR) composed of an α- and a β-chain in the majority of cases. The high diversity of these chains is generated by stepwise recombinations of a multitude of variable (V), in case of the β-chain diversity (D), and joining (J) gene sequences with a corresponding constant (C) chain during thymic T-cell evolution . V-genes are grouped in families consisting of genes with at least 50% sequence homology .
T lymphocyte repertoire alterations can be evaluated by TCR-diversity restriction analyses of the sequence or the length of the high variable part of the α- or β-chains (e. g. spectrotype, SSCP ('single strand conformation polymorphism'), DGGE ('denaturing gradient gel electrophoresis'), heteroduplex analysis [5–11]) or by TCR V-family quantification [12–16]. Because of the lower described number of Vβ-families compared to Vα-families, the higher variability of the β-chain, and the fact that each T-cell clone can express two different α-chains but only one β-chain, the Vβ-chain has been largely preferred for this type of analysis [12, 13, 17, 18].
Quantification of Vβ-families have been investigated by means of different approaches as southern blotting or, more recently, Fluorescence Activated Cell Sorter (FACS) and quantitative real time reverse transcribed PCR (qRT PCR) [5, 12–14]. In contrast to other approaches, PCR-based methods could detect a higher number of different families, had higher selectivity, and were applicable to different specimens. A cumbersome limitation for qRT PCR assessments was the suboptimal establishment of standard dilution curves for exact quantification due to the high heterogeneity of sequences within each family.
Here we introduce a versatile and rapid method for qRT PCR relative quantification of Vβ-family expressions based on slope and crossing point of the respective PCR reaction overwhelming therefore the problem of establishing standard dilution curves.
Peripheral blood samples were drawn form patients and healthy volunteers. Tissue samples were collected from patients with colorectal cancer who underwent therapeutical resection. Both patients and controls had given informed consent for the use of their specimens before sampling. Human T acute lymphoblastic leukemia (ALL) cell lines JURKAT, MOLT-16, and CCRF-CEM were purchased from German Collection of Microorganisms and Cell Cultures (DSMZ) and cultivated under recommended conditions.
RNA extraction and cDNA synthesis
Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) or fresh tissue using TRIzol® (Invitrogen, Carlsbad, California, USA) or RNeasy® Mini Kit (Qiagen, Hilden, Nordrhein-Westfalen, Germany) according to manufacturers' instructions. RNA was quantified using a NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, Delaware, USA), and integrity was checked electrophoretically. Reverse transcription was performed with Omniscript Reverse Transcriptase® (Qiagen) as described previously . Samples were stored at -20°C.
Relative quantification of Vβ-families
Primers/probe for quantification of Vβ-families (modified from , TaqMan).
Sequence (5' – 3')
CAC TCT GAA CTA AAC CTG A
TCA ACC ATG CAA GCC TGA CC
CGC TTC TCC CTG ATT CTG GAG TCC
TTC CCA TCA GCC GCC CAA ACC TA
CTG AGA TGA ATG TGA GCA CCT TG
CTG AGC TGA ATG TGA ACG CCT TG
AGA TCC AGC GCA CAG AGC G
AGA TCC AGC GCA CAS AGC A
GCC AAG TCG CTT CTC ACC TG
TGA AGA TCC AGC CCT CAG AAC CC
TCT CAC CTA AAT CTC CAG ACA AAG
CCA CGG AGT CAG GGG ACA CA
TGC CAG GCC CTC ACA TAC CTC TCA
GAG AAT TTC CTC CTC ACT CTG G
GAC CTC CCC CTC ACT CTG G
CTC AGG CTG CTG TCG GCT G
CTC AGG CTG GAG TTG GCT G
AGG GTA CAA AGT CTC TCG AAA AG
CAG GCA CAG GCT AAA TTC TCC
GAA CTG GAG GAT TCT GGA GT
GAA GGG TAC AGC GTC TCT CGG
TTT CTG CTG AAT TTC CCA AAG AGG
TCT CAA TGC CCC AAG AAC GCA C
AGG TGC CCC AGA ATC TCT CAG
TCA AAG GAG TAG ACT CCA CTC TC
AGA TCC GGT CCA CAA AGC TG
ATT CTG AAC TGA ACA TGA GCT CCT
ATC CAG GAG GCC GAA CAC TTC
FAM-ATG GCT CAA ACA CAG CGA CCT CGG-TAMRA
GGT GTG GGA GAT CTC TGC TTC
assuming a constant s for all families.
Quantification of TCR constant α-chain
Primers/probes sets for quantification of PBGD and Cα-chain (FRET).
TGC AGG CTA CCA TCC ATG TCC CTG C
AGC TGC CGT GCA ACA TCC AGG ATG T
CGT GGA ATG TTA CGA GCA GCA GTG ATG CCT ACC-Fl
LC-TGT GGG TCA TCC TCA GGG CCA TCT TC-ph
ACA CCT TCT TCC CCA G
TCC AGT TGG TGG CAT T
GTG ATT GGG TTC CGA ATC CTC C-Fl
LC-CTG AAA GTG GCC GGG TTT AAT CT-ph
Slopes of the PCR reactions for all Vβ-families were determined by dilution series of a positive control as described (data not shown). The average slope was 3.966 with a standard deviation of 0.61. This value was used for the calculation of family percentages. For assessment of mean value and standard deviation of each family in peripheral blood, PBMCs of 20 healthy volunteers were analyzed (Figure 1B).
Because total amount of TCR α/β expression could be different among various types of specimens, we quantified the constant α-chain as a control of sample quality. HAC expression was 50-fold higher in PBMCs than in tissue samples (data not shown).
In this study, we introduced a new approach for estimating the distribution of Vβ-family expression in a population of T-cells. To determine the effective concentration of a transcript, standard dilutions of the specific fragment are generally utilized. In case of Vβ-family analysis, not a single target but a big amount of different close related transcripts has to be quantified because of the high variability of the Complement Determining Region 3 (CDR3) both in length and sequence. Therefore, establishment of a PCR in which a dilution series of a single amplicon (standard curve) is used for normalization, besides increase in costs, can not lead to exact results even using probes annealing to the constant part of the Vβ-chain. As PCR reactions lead to an exponential increase of produced fragments, and as a result crossing point values have to be interpreted logarithmically concerning the effective concentration of the target, it is possible to calculate percentages without normalization and independently from the sample concentration when crossing points and slope are taken into consideration. For these reasons, in order to by-pass CDR3 diversity, we established a system in which relative quantification is based on the comparison of crossing points, rather than performing a family-by-family quantification using single sequence plasmids for normalization.
There are two drawbacks on the approach: First, because the system is ,normalized' by the sum of its c'-values, results from runs with more than three lacking crossing points (because of suboptimal sample quality or technical problems) will not be informative. Second, the high standard deviation of the slopes limits the correctness of the mathematical procedure. Exemplary calculations showed that this effect was of no practical relevance as long as the concentration difference between compared samples, reflected by the difference between the sums of all c' (c' total ), did not exceed a factor of 100 (data not shown). Nevertheless, this effect may play a role comparing blood with tissue as HAC/PBGD ratio in tissue is lower than in PBMCs. If the difference would be much higher, mathematical corrections of the percentage of a single predominant family should be performed considering Δc' total and s j .
Compared to techniques characterizing the CDR3 region (e.g. spectrotyping, SSCP, DGGE), the method described is less time-consuming and useful for high-throughput screening analyses. As for spectrotyping, conclusive proof of clonality can only be achieved by sequencing. Approaches like TC landscape combine CDR3 length analysis with Vβ-family quantification by FACS quantification in order to improve sensitivity . Due to higher number of detectable families and higher sensitivity, the use of a high-throughput PCR-based quantification algorithm could make a combined approach even more effective .
We present a novel and versatile approach for high-throughput Vβ family quantification. Our approach is suitable for samples from blood or bone marrow as well as from tissue. Because of high reproducibility, comparative analyses of samples in different time points or from different compartments are possible.
We thank Susanne Wojtke for excellent technical assistance.
- Andersen MH, Schrama D, Thor Straten P, Becker JC: Cytotoxic T Cells. J Invest Dermatol. 2006, 126 (1): 32-41. 10.1038/sj.jid.5700001.View ArticlePubMedGoogle Scholar
- Boon T, Coulie PG, Van den Eynde BJ, van der Bruggen P: Human T Cell Response Against Melanoma. Annu Rev Immunol. 2006, 24: 175-208. 10.1146/annurev.immunol.24.021605.090733.View ArticlePubMedGoogle Scholar
- Davis MM, Bjorkman PJ: T cell antigen receptor genes and T-cell recognition. Nature. 1988, 334 (6181): 395-402. 10.1038/334395a0.View ArticlePubMedGoogle Scholar
- Arden B, Clark SP, Kabelitz D, Mak TW: Human T-cell receptor variable gene segment families. Immunogenetics. 1995, 42 (6): 455-500.PubMedGoogle Scholar
- Pilch H, Höhn H, Freitag K, Neukirch C, Necker A, Haddad P, Tanner B, Knapstein PG, Maeurer MJ: Improved assessment of T-cell receptor (TCR) VB repertoire in clinical specimens: combination of TCR-CDR3 spectratyping with flow cytometry-based TCR VB frequency analysis. Clin Diagn Lab Immunol. 2002, 9 (2): 257-266. 10.1128/CDLI.9.2.257-266.2002.PubMed CentralPubMedGoogle Scholar
- Yao XS, Diao Y, Sun WB, Luo JM, Qin M, Tang XY: Analysis of the CDR3 length repertoire and the diversity of TCR alpha chain in human peripheral blood T lymphocytes. Cell Mol Immunol. 2007, 4 (3): 215-220.PubMedGoogle Scholar
- Long SA, Khalili J, Ashe J, Berenson R, Ferrand C, Bonyhadi M: Standardized analysis for the quantification of Vbeta CDR3 T-cell receptor diversity. J Immunol Methods. 2006, 317 (1-2): 100-113. 10.1016/j.jim.2006.09.015.View ArticlePubMedGoogle Scholar
- Collette A, Cazenave PA, Pied S, Six A: New methods and software tools for high throughput CDR3 spectratyping. Application to T lymphocyte repertoire modifications during experimental malaria. J Immunol Methods. 2003, 278 (1-2): 105-116. 10.1016/S0022-1759(03)00225-4.View ArticlePubMedGoogle Scholar
- Chino Y, Murata H, Goto D, Matsumoto I, Tsutsumi A, Sakamoto T, Ohtsuka M, Sekisawa K, Ito S, Sumida T: T cell receptor BV gene repertoire of lymphocytes in bronchoalveolar lavage fluid of polymyositis/dermatomyositis patients with interstitial pneumonitis. Int J Mol Med. 2006, 17: 101-109.PubMedGoogle Scholar
- thor Straten P, Barfoed A, Seremet T, Saeterdal I, Zeuthen J, Guldberg P: Detection and characterization of alpha-beta-T-cell clonality by denaturing gradient gel electrophoresis (DGGE). Biotechniques. 1998, 25 (2): 244-250.PubMedGoogle Scholar
- van Dongen JJ, Langerak AW, Brüggemann M, Evans PA, Hummel M, Lavender FL, Delabesse E, Davi F, Schuuring E, García-Sanz R, van Krieken JH, Droese J, González D, Bastard C, White HE, Spaargaren M, González M, Parreira A, Smith JL, Morgan GJ, Kneba M, Macintyre EA: Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia. 2003, 17 (12): 2257-2317. 10.1038/sj.leu.2403202.View ArticlePubMedGoogle Scholar
- Willhauk M, Möhler T, Scheibenbogen C, Pawlita M, Brossart P, Schmier JW, Keilholz U: T-cell receptor beta variable region diversity in melanoma metastases after interleukin 2-based immunotherapy . Clin Cancer Res. 1996, 2 (4): 767-772.Google Scholar
- Brewer JL, Ericson SG: An improved methodology to detect human T cell receptor beta variable family gene expression patterns. J Immunol Methods. 2005, 302 (1-2): 54-67. 10.1016/j.jim.2005.04.014.View ArticlePubMedGoogle Scholar
- Lim A, Baron V, Ferradini L, Bonneville M, Kourilsky P, Pannetier C: Combination of MHC-peptide multimer-based T cell sorting with the Immunoscope permits sensitive ex vivo quantitation and follow-up of human CD8+ T cell immune responses. J Immunol Methods. 2002, 261 (1-2): 177-194. 10.1016/S0022-1759(02)00004-2.View ArticlePubMedGoogle Scholar
- Sun W, Popat U, Hutton G, Zang YC, Krance R, Carrum G, Land GA, Heslop H, Brenner M, Zhang JZ: Characteristics of T-cell receptor repertoire and myelin-reactive T cells reconstituted from autologous haematopoietic stemm-cell grafts in multiple sclerosis. Brain. 2004, 127 (Pt 5): 996-1000. 10.1093/brain/awh117.View ArticlePubMedGoogle Scholar
- Degauque N, Schadendorf D, Brouard S, Guillet M, Sébille F, Höhn H, Pallier A, Ruiz C, Dupont A, Chapin S, Hofmann U, Maeurer M, Soulillou JP: Blood T-cell Vbeta transcriptome in melanoma patients. Int J Cancer. 2004, 110 (5): 721-729. 10.1002/ijc.20149.View ArticlePubMedGoogle Scholar
- Gagne K, Brouard S, Giral M, Sebille F, Moreau A, Guillet M, Bignon JD, Imbert BM, Cuturi MC, Soulillou JP: Highly altered V beta repertoire of T cells Infiltrating long-term rejected kidney allografts. J Immunol. 2000, 164 (3): 1553-1563.View ArticlePubMedGoogle Scholar
- Thor Straten P, Schrama D, Andersen MH, Becker JC: T-cell clonotypes in cancer. J Transl Med. 2004, 2 (1): 11-10.1186/1479-5876-2-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Keilholz U, Goldin-Lang P, Bechrakis NE, Max N, Letsch A, Schmittel A, Scheibenbogen C, Heufelder K, Eggermont A, Thiel E: Quantitative detection of circulating tumor cells in cutaneous and ocular melanoma and quality assessment by real-time reverse transcriptase-polymerase chain reaction. Clin Cancer Res. 2004, 10 (5): 1605-1612. 10.1158/1078-0432.CCR-0610-3.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.