AbstractCurrent diagnostic approaches that characterize T-cell deficiency by analyzing diversity of T-cell receptor sequences effectuate limited informational gain about the actual restrictiveness. For deeper insight into T-cell receptor repertoires we developed next-generation-sequencing-spectratyping, which employs high coverage Roche/454 sequencing of T-cell receptor (β)-chain amplicons. For automated analysis of high-throughput-sequencing data, we developed a freely available software, the TCR profiler. Gene usage, length, encoded amino acid sequence and sequence diversity of the complementarity determining region 3 were determined and comprehensively integrated into a novel complexity score. Repertoires of CD8+ T cells from children with idiopathic or hepatitis-induced very severe aplastic anemia (n=7), children two months after bone marrow transplantation (n=7) and healthy controls (children n=5, adults n=5) were analyzed. Complexity scores clearly distinguished between healthy and diseased, and even between different immune deficiency states. The repertoire of aplastic anemia patients was dominated by public (i.e. present in more than one person) T-cell receptor clonotypes, whereas only 0.2% or 1.9% were public in normal children and adults, respectively. The CDR3 sequence ASSGVGFSGANVLT was highly prevalent in 3 cases of hepatitis-induced anemia (15–32% of all sequences), but was only low expressed in idiopathic aplastic anemia (2–5%, n=4) or healthy controls (<1%). Fifteen high frequent sequences were present exclusively in aplastic anemia patients. Next-generation-sequencing-spectratyping allows in-depth analysis of T-cell receptor repertoires and their restriction in clinical samples. A dominating clonotype was identified in hepatitis-induced anemia that may be associated with disease pathogenesis and several aplastic-anemia-associated, putatively autoreactive clonotypes were sequenced.
Very severe aplastic anemia (vSAA, <200 granulocytes/μl blood) is an acquired bone marrow failure syndrome resulting in pancytopenia.1 The etiology is idiopathic in the majority of cases,1 but a subset of patients develops SAA a few months after an episode of acute hepatitis.2,3 Pathological autoimmune responses are causative for the bone marrow failure in both types of SAA. Oligoclonally expanded populations of cytotoxic T cells diminish the patient’s own hematopoietic CD34 stem cells.1 The T-cell receptor (TCR) of the autoreactive T cells recognizes self-antigens presented by CD34 cells. The antigenic determinant is still unknown and may differ between patients. In hepatitis-associated SAA CD34 cells may present secondary targets of an immune response initially targeting the liver.4
A normal adult polyclonal T-cell compartment comprises an estimated number of 2.5 × 10 different T-cell clones each expressing a unique T-cell receptor.5 Children harbor higher absolute numbers of T cells and express a more diverse TCR repertoire than adults. During aging both the number of T cells as well as the diversity decreases.6,7 In 95% of T cells the TCR is a heterodimer composed of an and a protein chain that contain a constant and a variable region. Within the variable region the hypervariable, complementarity-determining region 3 (CDR3) is involved in antigen recognition. Its diversity is generated by somatic recombination of variable (V), joining (J) and in case of the β-chain also diversity (D) genes, termed V(D)J recombination. Random trimming and addition of non-template nucleotides at the junction sites (N-diversity mechanisms) greatly increases diversity further.8,9 Theoretically, a repertoire of approximately 10 different TCRs could be generated in humans.
Allelic exclusion leads to the expression of only one TCR β-chain protein in mature T cells.10 Therefore, TCR β-chains provide a molecular fingerprint of T-cell clones. 48 different functional TRBV, 13 TRBJ and 2 TRBD genes and additionally 1 TRBJ and 18 TRBV pseudogenes can potentially be rearranged. Commonly employed TCR repertoire determination by flow cytometric analysis11 is thus strongly restricted by the availability and the specificity of anti-TRB antibodies and no TCR sequence information can be gained. An immunoscope technique, CDR3 spectratyping that analyzes CDR3 length polymorphisms, is currently more widely used.8,12–16 This technique is limited by the fact that it requires numerous PCR reactions employing primers targeting all possible V and J genes for the amplification of individual β-chains. Delayed data availability, inability to detect diversity of TRB-chain sequences of equal length and the need to clone PCR products to obtain sequence information are further drawbacks.
More comprehensive approaches that accurately assess TCR diversity in patient samples could present powerful diagnostic tools. Important applications are monitoring of immune reconstitution, disease monitoring, control of therapeutic responses and the identification of pathogenic T-cell clonotypes in autoimmune disease.
Here, we developed next-generation-sequencing-spectratyping (NGS-S) that employs high coverage, massive parallel Roche/454-sequencing of TRB-chain amplicons as a fast and accurate method to determine T-cell diversity in clinical samples. For automated analysis of the generated NGS-S data we developed a novel software that allowed us to determine, for each TRB chain analyzed, the exact length and sequence of the CDR3, to identify the rearranged TRBV, TRBD and TRBJ genes, to analyze non-template nucleotides added at the junction sites and to exclude non-functional transcripts. Employing this technique we investigated the impairment of the T-cell compartment in pediatric patients with idiopathic and hepatitis induced vSAA and analyzed the expansion of specific potentially autoreactive T-cell clonotypes in these patients.
Design and Methods
Patients and controls
T-cell diversity of 7 children diagnosed with vSAA as previously described17,18 and enrolled in the multicenter BFM-SAA 94 therapy study was prospectively analyzed (Table 1). The TCR repertoire of 7 age-matched children who underwent haploidentical bone marrow transplantation was investigated 2–4 months after transplantation. The conditioning regimen has been described previously.19 The control group consisted of 5 healthy adult donors and 5 healthy age-matched pediatric donors. The local ethics committee approved the research and all participants or their parents gave written informed consent.
Mononuclear cells were derived from bone marrow (all pediatric patients and controls) or peripheral blood (all healthy adult donors) by Ficoll (Biochrom AG, Berlin, Germany) density centrifugation. CD8 T lymphocytes were selected by using magnetic beads (Dynabeads, Dynal Biotech ASA, Oslo, Norway). Total RNA of 10 cells was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and complementary DNA was synthesized by standard procedures.
Spectratyping was performed as described previously.20 A brief description is provided in the Online Supplementary Design and Methods section.
Amplicon library preparation and next-generation-sequencing of TCR β-chains
Each amplicon library was generated by a single PCR. cDNA was amplified using the ACCUPrime Supermix I (Invitrogen, Carlsbad, CA, USA). Two custom-made degenerated V region primer mixtures (VP1: GCIITKTIYTGGTAYMGACA, VP2: CTITKTWTTGGTAYCIKCAG, 0.2 pmol each), designed to cover most TRBV genes and a reverse primer (CP1: GCACCTCCTTCCCATTCAC, 0.2 pmol) covering both TRBC genes (Online Supplementary Figure S1B) were used.21 PCR products were purified using the Gel Extraction Kit (Qiagen, Hilden, Germany) and high-throughput sequencing was performed according to the manufacturer’s instructions (Roche/454, Basel, Switzerland).
CDR3 coverage and error estimation are described in the Online Supplementary Design and Methods section.
Analysis of T-cell diversity and development of a novel score to present TCR diversity
Individual amplicons were characterized by alignment-based identification of the rearranged germ line genes (TRBV, TRBD and TRBJ) and by determination of the CDR3 length, sequence, productivity and diversity (Online Supplementary Figure S1C). This information was used for the calculation of a next-generation-sequencing spectratype complexity score (CSNGS), as described in the Online Supplementary Appendix. The TCR profiler software tool and a User’s Manual are available from: http://bibiserv.techfak.uni-bielefeld.de/tcrprofiler.
TCR β-chain germ line reference sequences were derived from the IMGT/GENE-DB database (http://imgt.cines.org).22 The nomenclature provided by IMGT was used for the designation of TRBV, TRBD and TRBJ genes.23,24 Correspondence between different TCR gene nomenclatures is available from:
Skewing of complementarity determining region 3 length polymorphisms determined by next-generation-sequencing-spectratyping
The TCR repertoire of children at diagnosis of vSAA (n=7) was analyzed by next-generation-sequencing-spectratyping (NGS-S) as well as conventional CDR3 length spectratyping and compared to children who underwent haploidentical BMT (n=7) and healthy persons (adults (n=5) and children (n=5)) (Table 1). The CDR3 length spectratyping approach used analyzed polymorphic PCR products of 26 individual TRBV possibly rearranged with 13 TRBJ genes. By counting the number of fluorescent peaks generated after separation of PCR products on a gel matrix, the CDR3 length polymorphisms for each TRBV/TRBJ combination could be investigated (Figure 1A, upper panels). Skewing of CDR3 sequence length was observed in both the vSAA and the hBMT patient cohort, but hardly at all in healthy control cohorts. Complexity scores based on spectratyping results (CSSPECTRATYPING) were obtained for each patient and healthy adult individuals included in this study (Table 1). Lowest scores indicating lowest complexity were derived for samples from patients after bone marrow transplantation (41±21, mean value±standard deviation), followed by samples from vSAA patients (61±44). Within the SAA cohort, scores were lower for HAAA (29±4) than for IAA patients (86±44). Remarkably, 2 IAA patients (P6 and P7) not responding to immunosuppressive therapy had almost normal spectratyping complexity scores (124 and 123, respectively). This finding corresponds well to our earlier observations.20 Based on CSSPECTRATYPING, a distinction between diseased cohorts was not possible because of the associated high standard deviations. Healthy adult controls demonstrated high complexity scores with low standard deviation (127±8). As described earlier,20 bone marrow from healthy children had a high CSSPECTRATYPING similar to adult controls.
For TCR profiling using NGS-S (Figure 1A, lower panels), the CDR3 region was defined as starting at the first codon after the cysteine residue encoded by the CASS motif of the rearranged TRBV gene and ending with the phenylalanine of the conserved FGXG amino acid sequence motif in the TRBJ gene. The length of productive CDR3 sequences was analyzed for every TRBV/TRBD/TRBJ gene combination. Skewing of the CDR3 length distribution can be evaluated for the recombination of each gene separately or combined over all TRBV and TRBJ genes as an average distribution (Figure 2A). The length of the CDR3 sequences varied from 24 to 60 nucleotides coding for 8 to 20 amino acids in all cohorts. Individual-specific and average cohort analyses of CDR3 lengths (Figure 2A) in general show a bell-shape like distribution for healthy controls. Most CDR3 sequences in the healthy adult cohort were 12 amino acids long. This shifted to an average length of 15 amino acids in the pediatric control cohort. Patients after hBMT and vSAA patients showed a clear skewing of the CDR3 distribution. The CDR3 sequence length in vSAA patients reached a maximum at 16 amino acids. This was due to a dominant expansion in the HAAA patients P1, P2 and P3 only; the IAA patients had an almost normal CDR3 length profile.
T-cell pathologies are reliably distinguished by application of a new complexity score to next-generation-sequencing data
To analyze and compare the severity of TCR repertoire restriction in different T-cell pathologies we developed several new TCR complexity scores based on deep sequencing (Online Supplementary Design and Methods). Similar to the CSSPECTRATYPING score generated by analysis of conventional spectratyping results, the developed CDR3 Length Complexity Score CSL is a measure of the CDR3 length polymorphisms detected by NGS-S in each TRBV family. The CSL score readily distinguished samples from healthy (167±21, mean value±standard deviation) and diseased (vSAA: 116±15; hBMT: 111±25) individuals, but could not clearly differentiate between the two cohorts with constriction in their T-cell repertoire. To enhance discrimination between children with different T-cell pathologies, we developed the Combination Complexity Score CSC that was initially based on combinations of different TRBV and TRBJ genes identified in each sample (Figure 1B). However, we found a very high interrelationship between CSC and CSL, indicating that the numbers of different gene combinations and CDR3 length polymorphisms detected in each sample are dependent on one another. Thus, the CSC score provided basically the same information as CSL. Robust and complete separation of the diseased cohorts was achieved when the information of both scores was combined and diversity was further addressed on the basis of the amino acid sequence of individual CDR3 sequences (Figure 2B and Online Supplementary Figure S2). The calculated novel CSNGS complexity score showed that the repertoire of healthy children is more complex compared to healthy adults (Figure 2B, left panel). CSNGS further showed significant differences not only between healthy and diseased individuals, but also between children with vSAA and after hBMT (Figure 2B, middle panel), and also discriminated between vSAA cohorts with a different pathogenesis (HAAA and IAA, Figure 2B right panel). CSNGS values were significantly higher for children suffering from vSAA than for children during immune reconstitution after hBMT. This indicates that the constraint of the T-cell compartment was more severe in the children three months after hBMT than in the children newly diagnosed with vSAA. The comparison of patients with hepatitis-induced versus idiopathic vSAA indicated that the T-cell repertoire was more restricted in IAA patients.
Restricted usage of variable and joining T-cell receptor β-chain genes in severe aplastic anemia
The identification of recombined V genes in a next-generation-sequencing approach can be hampered due to the shortness of sequenced reads, the high homology of TRBV genes, and the addition of non-template nucleotides at the gene boundaries.25 Thus, we employed a previously reported method to generate amplicon libraries that span the entire Vβ-chain.21 After long-read next-generation-sequencing of these libraries, we could unambiguously identify every TRBV gene rearranged in each of the identified CDR3 sequences. We amplified and sequenced most of the 48 possible TRBV genes (Figure 1B). The overview of TRBV sequences recombined with each TRBJ gene detected clearly shows the reduced complexity of the TCR repertoires from children with vSAA (hepatitis induced as well as idiopathic) and children after hBMT compared to the healthy control group (Figure 1B). In samples derived from pediatric vSAA patients, TRBV11–2 was predominantly rearranged: 44% of all functional CDR3 sequences in HAAA patients, 26% in IAA, and 16–18% in healthy controls and hBMT patients, respectively. In the cohorts of healthy controls and children after hBMT, TRBV20-1 was rearranged most frequently. TRBV20-1 was used only in 3.9% of CDR3 sequences in the HAAA and in 0.4% in the IAA cohort.
Using the same approach, we further identified all 13 TRBJ genes. Use of the TRBJ genes was similar in all cohorts analyzed with a descending usage of TRBJ2–7, TRBJ2-1, TRBJ1-1 TRBJ2–3, 1–2 TRBJ2–5, TRBJ1–5 and TRBJ2-2 (Online Supplementary Figure S3). An expansion of sequences with rearranged TRBJ2–6 was obvious only in the patients P1, P2 and P3, who had acquired vSAA after an episode of hepatitis (Figure 3A and B). In the vSAA patient P2, these sequences accounted for 42% of all functional CDR3 sequences detected (vSAA P3: 30%, vSAA P1: 20%). The vast majority of these sequences had TRBJ2–6 rearranged to TRBV11–2 and coded for the amino acid sequence ASSGVGFSGANVLT (P1: 15%, P2: 32%, P3: 24% of all productive CDR3 sequences) (Figure 3C). Such an expansion was not observed in the investigated vSAA patients who acquired their bone marrow failure without preceding hepatitis.
Patients suffering from very severe aplastic anemia share a large number of complementarity determining region 3 amino acid sequences
Most of the CDR3 amino acid sequences analyzed in healthy adult controls were non-public, i.e. so-called private sequences that were not shared with other individuals. Only 37 (1.9%) of all unique CDR3 amino acid sequences were shared between healthy adult individuals and even less between healthy and diseased individuals (Figure 2C). Healthy children shared only 0.2% of CDR3 sequences. None of the CDR3 amino acid sequences shared between healthy individuals, children or adults, were exclusively expressed in this cohort.
In contrast, T cells from vSAA patients expressed a large number of CDR3 sequences that were present in all individuals of this cohort: 273 individual sequences, 17.2% of all CDR3 amino acid sequences detected in this cohort. Within this cohort, HAAA patients shared more sequences with one another than IAA patients. Frequent sequences exclusively expressed in the vSAA cohort may be considered as candidate sequences relevant for the pathogenesis of the disease (Table 2). vSAA patients shared more CDR3 sequences with hBMT patients (4%) than with healthy controls (2%). Of the clonotypes shared between all hBMT patients (5.9%), most were also expressed in vSAA patients and none was specifically expressed in this cohort.
Oligoclonal expansions of public T-cell receptor sequences are predominant in patients with hepatitis-induced very severe aplastic anemia
Strikingly, the most abundantly expressed sequences in vSAA patients belonged to the group of 22 clonotypes that were truly public sequences, i.e. present in more than one person. These were commonly expressed in all cohorts (Figure 2C and data not shown). The clonotype ASSGVGFSGANVLT was most highly expanded in the subcohort of HAAA patients P1, P2 and P3. This sequence constitutes a public sequence that was frequently found in all healthy controls and constituted one of the top twelve expressed sequences in 4 of 5 healthy individuals. In addition to this sequence, we found several public clonotypes expressed in all vSAA patients but in none of the control cohorts (Table 3). Due to dominance of the ASSGVGFSGANVLT sequence, other CDR3 sequences are rarer in the HAAA than in the IAA cohort. The finding of a vSAA repertoire dominated by public clonotypes may indicate the generation of TCR protein surfaces that recognize one or more common, maybe permanently present, antigens.
The TCR repertoire of patients with aplastic anemia has so far mainly been analyzed by conventional CDR3 length spectratyping or flow cytometry. Skewing of the distribution of CDR3 lengths in TRBV families has been observed in aplastic anemia using spectratyping.26–30 However, a deviation from the bell-shaped distribution typical of healthy individuals is commonly found also in normal immune responses and has been reported to become more frequent with age.31–35 When we analyzed our patient cohort of 7 children newly diagnosed with vSAA by spectratyping, the CDR3 length distribution was severely skewed only in 5 of 7 patients. Interestingly, the 2 patients with normal CSSPECTRATYPING complexity scores were not responding to immunosuppressive therapy. The TCR repertoire is skewed during immune reconstitution after BMT36–38 and this could be shown also in the cohort of children that we analyzed shortly after bone marrow transplantation. Large variations between the individuals of this cohort were observed. Conventional CDR3 spectratyping did not allow us to judge the severity of the T-cell constriction or to distinguish between a vSAA- or hBMT-induced defective repertoire. Furthermore, to obtain sequence information of potentially pathogenic T-cell clones, a multitude of individual PCR products would have to be cloned and sequenced.
With the advent of next-generation-sequencing technologies, it has now become feasible to directly sequence a massive number of TCR β chains in parallel. We applied NGS to TCR spectratyping and developed a high throughput method to analyze the TCR repertoire in clinical samples that we termed NGS-Spectratyping (NGS-S). For every patient sample, an average of 15,000 to 30,000 reads were sequenced and analyzed by our newly designed software. Currently, only 3844 mRNA sequences of the TCR β chain are published in GenBank and only a very few have already been associated with specific antigens. On average, we identified 3,800–9,000 unique functional CDR3 sequences per patient, most of them with an as yet unknown antigen specificity.
A side-by-side comparison with conventional CDR3 spectratyping showed that ‘virtual’ NGS-S based spectratypes contained all the information presented by conventional spectratypes. But NGS-S was clearly superior to spectratyping in terms of sensitivity. In many V gene families, virtual spectratyping revealed additional CDR3 sequences not detected by conventional spectratyping. A recent study evaluating TCR diversity in 2 healthy adults based on next-generation-sequencing and spectratyping similarly showed improved sensitivity using NGS.39 Moreover, after implementing our newly developed CSNGS score we were able to reliably distinguish different T-cell abnormalities from one another.
The strength of the long-sequencing read method used is the unambiguous identification of all rearranged TRBV and TRBJ genes due to the length of the accomplished sequencing reads. The combination of TRBV and TRBJ genes was not random and differed between the cohorts analyzed. Specific genes were used and recombined more frequently than others. Similar to a previous report,25 we observed a dominance of the TRBV20-1 gene in healthy individuals but also in transplanted patients (24% of all CDR3 sequences in both cohorts). In contrast, only 2% of CDR3 sequences in vSAA patients used this gene. Some reports have observed a dominant usage of certain TRBV genes in SAA patients, such as TRBV629 or TRBV24.40–42 Most obvious in the present study was a severely skewed CDR3 profile, and dominance and expansion of sequences using TRBV11–2 combined with TRBJ2–6 in the patients with hepatitis-associated vSAA that was not observed in patients with idiopathic pathogenesis. IAA patients had an almost normal CDR3 length profile in the NGS analysis and showed no expansion of CDR3 sequences using specific TRBV genes. All HAAA patients, but only 2 of the IAA patients, responded well to immunosuppressive therapy after 6–12 months. The pattern of TRBJ gene usage in our study was otherwise similar to previous reports describing T-cell compartments of healthy individuals using NGS-based CDR3 sequence analysis.25,39 With the exception of the preferred TRBD2 gene usage in the HAAA patients associated with the expansion of the ASSGVGFSGANVLT sequence, we did not detect any differences in TRBD gene usage between the cohorts.
Interestingly, patients at diagnosis of vSAA shared an unexpectedly high number of CDR3 amino acid sequences (17.2%). In a previous study, approximately 1% of the CDR3 sequences were shared between 2 healthy adults.39 This is very similar to our results: 1.9% were shared between 5 healthy adult individuals and only 0.2% between healthy children under 13 years of age. Transplanted patients showed only a moderate rise in the percentage of common sequences. Such shared sequences have been suggested to be nearer to the germ line sequence.39 Alternatively, they may be strongly selected by common or disease-associated antigens resulting in semi-public repertoires of certain patient groups.30 In addition, we identified 15 high frequent CDR3 sequences only present in vSAA patients. As we focused our analyses on marrow-derived CD8 T cells, some of these CDR3 sequences may indeed identify T-cell clones involved in the autoimmune pathogenesis of the disease. It will be interesting to test these candidate pathological T cells in further in vitro studies by co-cultivation with target CD34 stem cells or in vivo after adoptive transfer in mice.
During many chronic infections, as well as cancer, T-cell dysfunction occurs because of an exhaustion of T cells.43 Persistent antigen presence leads to a functional decline in antigen specific effector T cells and eventually to their depletion.44–46 Exhausted cells lose the ability to produce large amounts of INFγ. Interestingly, low or absent production of this cytokine by bone marrow T cells was observed in some SAA patients and was predictive of a lack of response to immunosuppressive therapy.47 Recently, we consistently found that CD34 stem cells of individual non-responding vSAA patients lacked expression of typical IFNγ stimulated genes.48 Taking into account our current finding of a mainly public repertoire in vSAA patients, it may be speculated that non-responding SAA could alternatively be a disease of chronic immune stimulation finally leading to an exhaustion and depletion of antigen-specific private T cells. In this respect, it is interesting to observe that the 2 non-responding cases included in our study (P6 and P7) had a repertoire characterized by an even lower frequency of residual clonotypes and a lack of clonal expansions compared to responding patients. More patients should be analyzed in a prospective clinical study to draw firm conclusions about the value of NGS-S in predicting response.
The authors wish to thank the colleagues in pediatric oncology centers and all parents who gave their consent to use the biological material from their minors. AB was supported by grants from the “Bundesministerium für Forschung und Technologie” (BMBF).
- The online version of this paper has a Supplementary Appendix. PFIK, SR, UF, TK contributed equally to this manuscript.
- Authorship and Disclosures Information on authorship, contributions, and financial & other disclosures was provided by the authors and is available with the online version of this article at www.haematologica.org.
- Received May 7, 2012.
- Accepted May 2, 2013.
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