BRIEF DEFINITIVE REPORT |
CORRESPONDENCE Frank J.T. Staal: f.staal{at}erasmusmc.nl
![]() ![]() ![]() ![]() ![]() |
---|
T cells develop from progenitors that migrate from the bone marrow into the thymus (1). Thymocytes are subdivided roughly as being double negative (DN), double positive (DP), or single positive (SP), based on the expression of the CD4 and CD8 coreceptors (1). The DN stage is heterogeneous and can be subdivided into four distinct subsets in mice based on the expression of CD44 and CD25. In human, three distinct DN stages can be recognized: a CD34+CD38CD1a stage that represents the most immature thymic subset and the consecutive CD34+CD38+CD1a and CD34+CD38+CD1a+ stages. Human DN thymocytes mature via an immature single positive (ISP CD4+) and a DP stage into CD4+ or CD8+ SP T cells that express functional T cell receptors (TCR) and that exit the thymus (1).
A hallmark of T cell development is the generation of T cells that express a functional TCR, TCRß or TCR
. During T cell development, the variable domains of TCRA, TCRB, TCRG, and TCRD (located within TCRA) genes are assembled following rearrangement of variable (V), diversity (D), and joining (J) gene segments by a process called V(D)J recombination (2). V(D)J recombination uses the RAG1 and RAG2 enzymes that selectively target recombination signal sequences that flank V, D, and J segments (2).
Studies in T cell acute lymphoblastic leukemias suggest that recombinations of TCR genes are sequential between the different genes (TCRD > TCRG > TCRB > TCRA) as well as within a particular gene (e.g., TCRD: D2-D
3, D
2-J
1, V
-J
1) (2, 3), which is supported by limited data that were obtained from normal human T cell subsets (4). Therefore, the timing and efficiency of rearrangement of various TCR genes must be determined by the accessibility of gene segments to RAG enzymes. Evidence suggests that promoter and enhancer activity that is controlled by transcription factors regulate V(D)J recombination by modulating chromatin structures and rendering gene segments accessible to RAG cleavage (5, 6).
For obvious reasons T cell development mainly is studied in the mouse. Real-time quantitative PCR (RQ-PCR) and DNA microarray techniques allow careful analysis of small cell numbers. In this study we assessed the precise TCR gene configuration and the gene expression profiles of thymic subsets by RQ-PCR and Affymetrix DNA microarrays. By combining these two techniques we aimed for the identification of factors that play a role in regulating human TCR gene recombination.
![]() |
RESULTS AND DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() |
---|
Determination of TCR gene rearrangements by RQ-PCR and GeneScan analysis
TCR gene rearrangement analysis was performed in duplicate and on the two independently purified subsets (average is shown). The primers and TaqMan probes are listed in Table S1 (available at http://www.jem.org/cgi/content/full/jem.20042524/DC1) and do not amplify germline DNA.
TCRD
The D2-D
3 rearrangement was detected first in the earliest CD34+CD38CD1a thymic subset, much earlier during development than in our previous Southern blottingbased report (4). D
2-D
3 rearrangements reached maximum levels in the subsequent CD34+CD38+CD1a stage and declined to low levels in the later subsets (Fig. 1 B). The initial D
2-D
3 wave was followed by D
2-J
1 rearrangements which were detectable at low levels in CD34+CD38CD1a cells, increased in CD34+CD38+CD1a cells, and peaked in CD34+CD38+CD1a+ cells (Fig. 1 C). Complete V
1-J
1 or V
2-J
1 rearrangements were detected first in CD34+ CD38+CD1a cells, increased to peak levels in CD4+ ISP cells, and declined thereafter when the majority of thymocytes differentiated further into the TCR
ß lineage (Fig. 1 D). During TCR
ß T cell lineage development, the TCRD gene is deleted from the TCRA/D gene complex (2, 7) which results in the formation of a T cell receptor excision circle (TREC) that may contain V
-J
1 rearrangements. TRECs do not replicate on cell division; consequently, they are diluted out in proliferating, developing T cells (2). Ki67 staining demonstrated high percentages of proliferating cells within the human ISP and DP CD3 thymic subsets (unpublished data). This proliferation likely accounts for the observed decline in V
-J
1 rearrangements after the ISP stage. The V
1 gene segment seemed to be used preferentially in postnatal TCR
+ thymocytes which is in sharp contrast to the well-described preferential V
2 usage by peripheral TCR
+ T cells (Fig. 1 D; reference 8). We detected very low V
-D
3 levels in the thymic subsets; this represents a minor pathway to initiate TCRD gene rearrangement in postnatal thymus (Fig. 1 E).
|
TCRB
Dß1-Jß1 rearrangements were detected first at low levels in the CD34+CD38+CD1a population and increased thereafter from CD34+CD38+CD1a+ to CD4+ ISP cells (Fig. 2 B). Dß2-Jß2 rearrangements were detected first at low levels in CD34+CD38+CD1a+ cells, one differentiation stage after Dß1-Jß1 rearrangements (Fig. 2 B). The seemingly lower levels of Dß-Jß in DP CD3+ probably are caused by variation within the lower range of detection of our assay and likely do not represent a true decrease.
|
TCRA
Because of large numbers of rearrangable V (
54) and J
(61) gene segments (2), we could not design a multiplex RQ-PCR for reliable quantification of all V
-J
rearrangements. Instead, we aimed for an alternative approach in which we used different indirect measures to study TCRA recombination. TCRA recombination is initiated by the transcription of T-early
(TEA) in order to open the 5' site of the J
cluster, which is followed by TCRD deleting rearrangements, particularly the
REC-
J
rearrangement. These initiating events are followed by multiple, consecutive V
-J
rearrangements (7, 12).
To study initiation of TCRA rearrangement we determined the level of TEA-C transcripts as well as the occurrence of
REC-
J
rearrangements. TEA-C
transcripts started to increase in CD34+CD38+CD1a+ cells and reached peak levels in ISP and DP cells after which they declined again (Fig. 3 B).
REC-
J
rearrangements were detected first in ISP cells and reached peak levels in SP and TCR
ß+ thymocytes (Fig. 3 C). These data show that TCRA rearrangement already has started in the ISP cell population but that there are still cells within the CD3+ DP population that start rearrangement of the (most likely) second TCRA allele. Although TEA-C
transcripts and
REC-
J
rearrangements are good measures for initiation of TCRA rearrangement, they cannot be used for quantification of the actual TCRA rearrangements. TEA-C
is an mRNA product that cannot be extrapolated simply to the actual level of TCRA rearrangements. Quantification of
REC-
J
is complex because it is influenced strongly by ongoing V
-J
rearrangements and the consequently produced TRECs (containing
REC-
J
), whereas the amount of TRECs (and their dilution) is heavily dependent on the fraction of proliferating cells within specific subsets. Therefore, extra accumulation of TRECs may explain the relatively high
REC-
J
levels in nonproliferating SP cells as compared with the preceding proliferating stages.
|
We conclude that TCRA rearrangements are initiated when thymocytes progress from CD34+CD38+CD1a+ toward the ISP stagewhich is much earlier than reported previously (10)and apparently are ongoing until the CD3+ DP stage.
Microarray analysis
A total of 3848 probe sets underwent a significant change between any two successive stages of differentiation. Raw microarray data can be found at http://www.ebi.ac.uk/miamexpress, MIAME accession no. E-MEXP-337, and http://franklin.et.tudelft.nl, including a gene search browser. The expression levels of these probe sets were used to calculate a correlation coefficient between all possible pairs of microarrays and revealed high correlation between biological repeats (Fig. S2, available at http://www.jem.org/cgi/content/full/jem.20042524/DC1). This allowed us to use the average expression values of the two arrays that were performed per subset (obtained from five pooled thymi) for further analysis. Hierarchical clustering of the 3848 probe sets was performed and is described in Fig. S3 and Table S2 (available at http://www.jem.org/cgi/content/full/jem.20042524/DC1).
Of special interest for TCRß-selection and initiation of TCRA rearrangement is that PTCRA (pT) expression increased in the CD34+CD38+CD1a stage, peaked in the CD34+CD38+CD1a+ and ISP stages, after which it declined (Table S2). Mouse microarray data (13) have shown a similar expression pattern of pT
, with a minor peak at DN3 and a larger peak at the DP CD3 stage. Experiments with pT
mutant mice indicate that TCRß-selection in the mouse occurs at DN3 (14), and that TCRA recombination is initiated after TCRß-selection has occurred (14). Here, we show that initiation of TCRA recombination starts in CD34+CD38+CD1a+ cells. Therefore, the analogous pT
expression between mice and men, our TCRB GeneScan, and our TCRA recombination data indicate that human TCRß-selection occurs at the CD34+CD38+CD1a+ stage instead of the previously suggested ISP/DP stage (4, 10, 11).
To identify candidate transcription factors that are involved in regulating TCR recombination, the list of 3848 probe sets was filtered based on Gene Ontology annotation (transcriptional regulation and DNA binding) which yielded a final list of 446 probe sets that encoded a total of 361 genes (Fig. S4, available at http://www.jem.org/cgi/content/full/jem.20042524/DC1). Expression of genes that are associated with T cell commitment/differentiation and/or V(D)J recombination, such as NOTCH1, HES1, GATA3, BCL11B, RAG1, RAG2, and DNTT (TdT), increased strongly in early T cell differentiation.
To determine which genes may have a role in regulating TCR rearrangement, hierarchical cluster analysis was performed (Fig. 4 A). The 446 probe sets were divided into 15 clusters; the prototypic expression patterns are depicted in Fig. 4 B. Based on the TCR rearrangement patterns (as described above) and the prototypic gene expression patterns, we propose that clusters 3, 5, 6, 7, 14, and 15 (Fig. 4 B, asterisk) contain genes that may encode candidate factors for initiation/regulation of TCR rearrangements because the genes that are present in these clusters show higher expression at the moments at which active TCR gene rearrangement occurs. Genes that are present in clusters 3, 5, 6, 7, 14, and 15 are presented in Table I; those of all 15 clusters are shown in Table S3 (available at http://www.jem.org/cgi/content/full/jem.20042524/DC1).
|
|
Genes in cluster 7, 14, and 15, such as SPIB, ICSBP1, TCF4, CREB1, ETS1, and LEF-1, may encode factors that are involved in regulating TCRA rearrangements as well as allelic exclusion of the TCRB locus. These are discussed in the supplemental Results and Discussion (available at http://www.jem.org/cgi/content/full/jem.20042524/DC1).
Novel insights into human T cell development
We confirm that TCR loci rearrange in a highly ordered way (TCRD-TCRG-TCRB-TCRA) and defined sequential rearrangement steps of TCRD, TCRG, TCRB, and initiation of TCRA recombination to specific human thymic subsets. Importantly, our data show that recombination of the TCR genes occurs earlier during human T cell development than previously reported (4, 10). Given that TCRD rearrangement starts at DN1 in mice, followed by TCRG in DN2 and TCRB in DN2, but especially DN3 (1, 19), the human CD34+CD38CD1a, CD34+CD38+CD1a, and CD34+CD38+CD1a+ subsets resemble murine early DN1 (CD44+CD25CD117), late DN1/DN2 (CD117+ DN1; CD44+CD25+), and DN3 (CD44CD25+) stages, respectively. However, the relative frequency of DN1 cells in mice is higher than that of the corresponding human subset (CD34+CD38CD1a). We also demonstrate that TCRß-selection and initiation of TCRA rearrangement already occur at the CD34+CD38+CD1a+ stage of human T cell development, instead of the ISP/DP stage (4, 10, 11), similar to the mouse (i.e., TCRß-selection occurs at DN3 in the mouse; reference 14).
Based on the TCR rearrangement data and the expression profile of key recombination and differentiation genes (e.g., RAG1, RAG2, and PTCRA), we show that human and mouse T cell development are much more similar than assumed previously. In addition, candidate factors for regulation of TCR recombination are identified.
We propose an updated human T cell differentiation model as shown in Fig. S5 (available at http://www.jem.org/cgi/content/full/jem.20042524/DC1). These novel data help to bridge gaps in our understanding of human T cell development, and also should provide insight into the development of T cell acute lymphoblastic leukemias and T-SCID.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() |
---|
RQ-PCR analyses of TCR gene rearrangements.
This essentially was performed as described previously (20). For the various TCR recombinations, cell lines/diagnostic samples were selected as clonal control DNA.
GeneScan analysis for complete and in-frame Vß-Jß gene rearrangements.
This was done by two multiplex PCR reactions as described by the BIOMED-2 Concerted Action (9).
TEA expression by RQ-PCR analyses.
Expression of TEA was defined by calculating the ratio of TEA to the average value of the control genes ABL and GUSB.
Microarray analysis.
Affymetrix microarray analysis essentially was done as described previously (21) and according to Minimum Information About a Microarray Experiment guidelines (www.mged.org/Workgroups/MIAME/miame.html).
Statistical analysis.
Probe intensity background was removed using robust multichip analysis (22). The intensity levels were quantile normalized (23). Array groups (two biologically independent arrays per group) that corresponded to the development stages were compared based on the perfect match probe intensity levels only (22), by performing a per-probe set two-way analysis of variance (with factors "probe" and "stage"). The p-values were adjusted for multiple testing using idák step-down adjustment (24) and all differences with adjusted p-values <0.05 were considered to be significant. For Fig. S2, expression values were log2-transformed and the per-probe set geometric mean was subtracted. Further analysis was performed using Genlab software, running under Matlab (http://www.genlab.tudelft.nl). After per-probe set normalization to zero mean and unit standard deviation (z-score), a hierarchical clustering (complete linkage) was calculated based on Pearson correlation. The number of clusters k was determined by looking for a local minimum of the Davies-Bouldin index calculated for k = 1, ... , 30 (25).
Online supplemental material.
All methods are described in more detail as supplemental Materials and methods online. Additional Results and Discussion, as well as Figs. S1S5 and Tables S1S3, are provided online, as well. Online supplemental material is available at http://www.jem.org/cgi/content/full/jem.20042524/DC1.
![]() |
Acknowledgments |
---|
The authors have no conflicting financial interests.
Submitted: 10 December 2004
Accepted: 19 April 2005
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() |
---|