Germinal centers (GCs) are transient organized microstructures within secondary lymphoid tissues, which support the development of high-affinity antibodies by B cells. Even though GCs are mainly B cell driven, their formation and maintenance depends on follicular T helper cells (Tfh), a specialized subset of effector CD4 T cells. The differentiation into Tfh initiates in T cell zones of lymphoid organs before GC formation starts. After their interaction with B cells at the T-B border, Tfh locate within GC (GC-Tfh) and regulate the survival of proliferating GC-B cells, which compete for antigen (Ag) and for signals from GC-Tfh to undergo further somatic hypermutation and to mature into high-affinity-antibody-producing plasma cells and memory B cells (Crotty, 2019; Qi, 2016). Thus, GC-Tfh are central players in the regulation of humoral immune responses. Both (i) their differentiation into GC-Tfh and (ii) their survival and clonal expansion within GCs are constantly driven by competition for Ag contacts (Baumjohann et al., 2013; Fazilleau et al., 2009; Hwang et al., 2015; Knowlden and Sant, 2016; Tubo et al., 2013; Merkenschlager et al., 2021).

Here, we asked whether this clonal competition of Tfh for the limited space in GCs involves GC reactions of one or more activated lymphoid organs. This is of special interest for autoimmune conditions when multiple inflamed tissue sites and autoreactive GC reactions exist in distinct lymphoid organs. Tfh can shuttle between GCs of one lymph node or spleen indicating a local competition within one lymphoid organ (Merkenschlager et al., 2021; Shulman et al., 2013). The finding that CXCR5+ Tfh circulate in blood (Brenna et al., 2020; He et al., 2013) opens the possibility for a systemic exchange, in which each Tfh clone could participate in the clonal competition within distinct lymphoid organs in one individual. However, the diversity of individual T cell receptor (TCR) repertoires is extremely high, and it has been shown that individual T cell responses towards Ag are unique and polyclonal (Textor et al., 2018; Thomas et al., 2014; Zarnitsyna et al., 2013). This might be especially valid for auto-Ag because TCR bind with low affinities (Dolton et al., 2018; Rius et al., 2018). In line, the number of autoreactive T cell clones within one individual is particularly low due to the thymic negative selection process, which could support the involvement of multiple cross-reactive T cell clones specific for the same auto-Ag within one individual as it has been suggested previously (Ritvo et al., 2018).

To find out how polyclonal autoreactive Tfh responses are, we studied the distribution of GC-Tfh clones between two activated lymph nodes and challenged the endogenous TCR repertoire by injecting one auto-Ag, directed against a structural protein of murine skin, into the two hind footpads of the same mouse and analyzed the TCRβ sequences of GC-Tfh in both draining popliteal lymph nodes (pln). In contrast to our expectations, we found that the GC-Tfh clonotypes were highly shared between the two pln. This high overlap between both pln was restricted to the dominant GC-Tfh clonotypes and disappeared after establishment of skin pathology even though GC reaction continued. These temporal variations in the distribution of Tfh clones should be considered when utilizing Tfh repertoires to monitor and diagnose autoimmune diseases.

CD4 T cells are pivotal in adaptive immune responses during infection, autoimmune diseases, cancer, and vaccinations. Each CD4 T cell expresses one specific αβ TCR interacting with specific peptide-Ag presented in MHCII complexes (Itano and Jenkins, 2003). Upon interaction of the TCR with peptide:MHCII complexes, naive CD4 T cells undergo proliferation and functional differentiation into distinct T helper subsets such as Th1, Th2, Th17 effector cells, Tfh, and immunosuppressive Treg cells (Zhu et al., 2010). The main factor that regulates CD4 T cell differentiation is the TCR signaling strength, which is controlled by TCR/peptide:MHCII interactions, co-stimulation, and/or optimal dwell times. The differentiation into Tfh depends on strong TCR signals. It is suggested that they are strictly Ag-specific (Hwang et al., 2015; Knowlden and Sant, 2016; Tubo et al., 2013; Merkenschlager et al., 2021). Consistently, it has been shown that the maintenance and expansion of Tfh requires sustained Ag stimulation (Baumjohann et al., 2013; Merkenschlager et al., 2021).

In this study, we asked how this particular CD4 T cell subset is distributed within one individual at the clonal level. We used an autoantibody-mediated disease model of the skin, epidermolysis bullosa acquisita, and induced GC-Tfh in two separate draining pln by injecting auto-Ag in adjuvant (Ag1 or Ag2 group) or adjuvant only (PBS group).

Our study shows two major findings. First, we found a high overlap of dominant GC-Tfh clonotypes in both pln of one mouse (Figure 2a, b). This data demonstrates that even though the response to the auto-Ag was polyclonal in each mouse, a group of almost identical GC-Tfh clonotypes became dominant in each draining pln. Thus, exposures to the auto-Ag (Ag1 or Ag2) decreased the diversity of the endogenous Tfh repertoire compared to the PBS group. To establish this finding, we used multiplex iRepertiore PCRs and subsequent MiTCR analysis for preparing libraries and annotating TCRβ sequences (Bolotin et al., 2013), which ensures to yield the highest possible diversity of TCRβ clonotypes (Afzal et al., 2019). To confirm this decrease in diversity of the endogenous Tfh repertoire, we reanalyzed our data for Ag1 with a more precise analysis tool MiXCR that is advantageous for error corrections and adjusts for a more accurate clonal composition (Bolotin et al., 2013; Bolotin et al., 2015; Team I, 2019). The high overlap of the dominant Tfh clonotypes in the Ag1 group was clearly confirmed (Figure 2—figure supplement 1). To further compare both analysis tools, we calculated the MHI from samples of the Ag1 and PBS groups analyzed either with MiTCR or MiXCR. Data show a high similarity in both the Ag1 group (0.70 ± 0.04) and the PBS group (0.69 ± 0.039). Obviously, our finding that Tfh clonotypes highly overlap in contralateral pln is very robust independent of the analysis tools. On the other side, the suitability of the multiplex PCRs can be seen by the correct detections of the deletions in the TRBV genes of SJL mice (Figure 3c; Behlke et al., 1986). In summary, this data that the endogenous Tfh repertoire decreased after Ag exposure indicates that the number of T cell clones specific for the injected auto-Ag is restricted per mouse and supports previous reports demonstrating that the precursor frequency specific for auto-Ag within the T cell population of an individual is rather low (Jenkins and Moon, 2012). However, the precursor frequency of naive T cells specific for different peptide:MHC complexes varies in size. Therefore, it will be interesting whether this high clonal overlap would be found after injection of xenogeneic or pathogenic Ag. Moreover, because both Ag used in this study are polypeptides of a relatively large size (200–400 aa) and clearly differ from the size of peptides (13–25 aa) that are usually presented in MHCII (Natarajan et al., 2018), it is likely that they contain more than one antigenic epitope. It is reasonable to assume that injecting smaller peptides will further narrow the diversity of the individual GC-Tfh repertoire. In an initial experiment as an example for a recombinant xenogeneic protein antigen, we injected the Schistosoma mansoni-derived glutathione-s-transferase (GST)-tag of Ag1 (Rao et al., 2003), emulsified in TM into both footpads, and analyzed the distribution of Tfh clonotypes in both draining pln 4 weeks p.i. (Figure 2—figure supplement 2, Supplementary file 6). We found that the distribution of the GST-specific Tfh clonotypes between both pln was less controlled compared to the auto-Ag1. Dot plot diagrams showed a higher variability between the mice. In line, the MHI was not significantly different from the PBS group (Figure 2—figure supplement 2) and the number of overlapping Tfh clonotypes is lower in the GST group compared to the Ag1 group (Figure 3—figure supplement 1). Analysis of the V/J usage revealed significant differences in expression of TRBV3. The J2-4-segment was significantly less abundant after GST/TM immunization compared to the PBS/-group and the J2-5 segment was jointly higher abundant after GST/TM and Ag1 immunization compared to the control group (Figure 3—figure supplement 1). These data confirm the strict Ag specificity of Tfh cells in GCs, especially during the first 4 weeks p.i. The higher inconsistency in the number of overlapping Tfh clonotypes in the GST group could be explained by an earlier consumption of the Ag or the presence of more high-affinity T cell clones. These findings might be strongly influenced by the time point and the Ag concentration, which should be further investigated in future studies.

Another arising question is when this overlap of T cell clonotypes between both pln of one mouse starts. For example, T cells could travel between pln and the clonal selection could be a continuous organism-wide systemic process. The presence of circulating Tfh cells has been described in humans and mice (Brenna et al., 2020; He et al., 2013); however, it is not clear whether these circulating Tfh cells can enter ongoing GC reactions. Alternatively, it could take place during the priming phase. In this case, either the naive TCR repertoire is sufficiently broad that the Ag-driven selection for the GC reactions in both pln would robustly yield groups of similar clones independently or recently activated CD4 T cell clones would exchange between both pln after priming before GC reaction starts. To address this, we performed an initial experiment and identified TCRβ sequences of entire left and right pln of the same mouse (naive mice and mice that had been exposed to Ag1 for 1 or 3 days; please find detailed information in Supplementary file 5) and compared the relative percentage of overlapping T cell clonotypes between left and right pln by using the MHI. We found that the MHI did not differ between naive mice and Ag-exposed mice at 1 day p.i. (0.2035 + 0.01962 in naive mice, 0.2113 + 0.009 at 1 day p.i.), but a significant increased MHI at 3 days p.i. (0.3716 + 0.031; p<0.01, Kruskal–Wallis test, mean + SD for n = 3 mice, Figure 4—figure supplement 1). In parallel, the number of proliferating T cells and the mRNA expression for the proliferation marker Ki67 increased significantly (Figure 4—figure supplement 1). One may speculate that a high number of progenies emerges from the high-affinity T cell clones 3 days p.i. (Cho et al., 2017), which then migrate into other activated lymphoid tissues before differentiating into GC-Tfh. In this case, the initial progeny of activated T cell clones could originate from identical precursors, which would enter the circulation and migrate into the other pln, in which they outcompete low-affinity competitors and develop into GC-Tfh. However, on the other side it is assumed that Tfh stay resident in lymph nodes to provide B cell help (Crotty, 2019). One possibility to observe potential exchanges of Tfh cells between lymph nodes would be treatment with the T cell trafficking blocker FTY720. However, previous studies revealed no effect in this autoimmune mouse model (Niebuhr et al., 2017; Thieme et al., 2019). The second major finding of our study is that the PBS group, which received only adjuvant, develops GCs to a similar extent as the Ag group. Here, in contrast to the Ag group, the GC-Tfh repertoire remains diverse. No overlap of dominant GC-Tfh clonotypes was found between both pln. TM is a water-in-oil adjuvant that contains squalene among other components but no peptides that could be presented in MHCII (TiterMax Inc). It is proposed that adjuvants stress or kill local cells, which leads to the release of endogenous peptide-epitopes that are subsequently presented to T cells (Riteau et al., 2016) and induce the formation of GC reactions as observed in this study (Figure 1c). In this scenario, numerous endogenous peptide-epitopes at low concentrations might be released by the local inflammation, which leads to the diverse GC-Tfh repertoire in both pln (Figure 2a, b). However, to break the tolerance in our model, both auto-Ag had to be injected at very high concentrations (60 µg/Ag1 or 120 µg/Ag2, respectively) (Sitaru et al., 2006). This data leads to the hypothesis that the concentration of the Ag is critically involved in the emergence of overlapping GC-Tfh repertoires in separate pln (summarized in Figure 6). It will be interesting to find out how low Ag concentrations at distinct tissue sites affect the GC-Tfh repertoires. Moreover, it will be important to study the adjuvant-stimulated peptide-epitopes in future studies, especially because squalene compounds are frequently used in human vaccines and might evoke unwanted autoimmune responses (Pellegrini et al., 2009).

Figure 6

Download asset Open asset

Another conclusion of our study is that the total number of GC-Tfh within one individual is regulated by the number of lymphoid tissues that drain Ag-exposed tissue sites. This data suggests that Ag injection at two or more tissue sites could improve the development of high-affinity antibodies. We believe that this information will be highly useful for the development of new and more efficient vaccination strategies.

Finally, we wish to highlight that this in vivo approach could help to identify new peptide-specific TCR sequences and confirm already established datasets (Teraguchi et al., 2020). Even though TCR datasets are growing, the number of TCRs with known Ag specificity and function is extremely low (Shugay et al., 2018; Zvyagin et al., 2020). Especially the detection of self-reactive CD4 TCR is problematic due to the low binding affinities between TCR and autoantigenic-peptide:MHCII complexes (Dolton et al., 2018; Rius et al., 2018). Thus, even though our approach is restricted to mouse models, the use of HLA-transgenic mice might enable to link TCR sequences also to human antigenic epitopes.

T cell receptor-RNA sequencing data generated in this study are deposited in the sequence read archive hosted at https://www.ncbi.nlm.nih.gov/sra under the primary accession code PRJNA731654. Some of the data (skin effector T cells) have been deposited under the accession code PRJNA586880.

1. 1. Kalies K
   2. Niebuhr M

   (2021) NCBI BioProject

   ID PRJNA731654. TRBV sequences of murine follicular T helper cells in experimental epidermolysis bullosa acquisita.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731654
2. 1. Kalies K
   2. Niebuhr M

   (2021) NCBI BioProject

   ID PRJNA586880. TCRb-seq data from dermal T cells in SJL mice upon immunization.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA586880

1. 1. Afzal S
   2. Gil-Farina I
   3. Gabriel R
   4. Ahmad S
   5. von Kalle C
   6. Schmidt M
   7. Fronza R

   (2019) Systematic comparative study of computational methods for T-cell receptor sequencing data analysis

   Briefings in Bioinformatics 20:222–234.

   https://doi.org/10.1093/bib/bbx111

   • PubMed
   • Google Scholar
2. 1. Baumjohann D
   2. Preite S
   3. Reboldi A
   4. Ronchi F
   5. Ansel KM
   6. Lanzavecchia A
   7. Sallusto F

   (2013) Persistent antigen and germinal center B cells sustain T follicular helper cell responses and phenotype

   Immunity 38:596–605.

   https://doi.org/10.1016/j.immuni.2012.11.020

   • PubMed
   • Google Scholar
3. 1. Behlke MA
   2. Chou HS
   3. Huppi K
   4. Loh DY

   (1986) Murine T-cell receptor mutants with deletions of beta-chain variable region genes

   PNAS 83:767–771.

   https://doi.org/10.1073/pnas.83.3.767

   • PubMed
   • Google Scholar
4. 1. Bolotin DA
   2. Shugay M
   3. Mamedov IZ
   4. Putintseva EV
   5. Turchaninova MA
   6. Zvyagin IV
   7. Britanova OV
   8. Chudakov DM

   (2013) MiTCR: software for T-cell receptor sequencing data analysis

   Nature Methods 10:813–814.

   https://doi.org/10.1038/nmeth.2555

   • PubMed
   • Google Scholar
5. 1. Bolotin DA
   2. Poslavsky S
   3. Mitrophanov I
   4. Shugay M
   5. Mamedov IZ
   6. Putintseva EV
   7. Chudakov DM

   (2015) MiXCR: software for comprehensive adaptive immunity profiling

   Nature Methods 12:380–381.

   https://doi.org/10.1038/nmeth.3364

   • PubMed
   • Google Scholar
6. 1. Brenna E
   2. Davydov AN
   3. Ladell K
   4. McLaren JE
   5. Bonaiuti P
   6. Metsger M
   7. Ramsden JD
   8. Gilbert SC
   9. Lambe T
   10. Price DA
   11. Campion SL
   12. Chudakov DM
   13. Borrow P
   14. McMichael AJ

   (2020) CD4⁺ T follicular helper cells in human tonsils and blood are clonally convergent but divergent from Non-Tfh CD4⁺Cells

   Cell Reports 30:137–152.

   https://doi.org/10.1016/j.celrep.2019.12.016

   • PubMed
   • Google Scholar
7. 1. Cho YL
   2. Flossdorf M
   3. Kretschmer L
   4. Höfer T
   5. Busch DH
   6. Buchholz VR

   (2017) TCR signal quality modulates fate decisions of single CD4⁺ T Cells in a Probabilistic Manner

   Cell Reports 20:806–818.

   https://doi.org/10.1016/j.celrep.2017.07.005

   • PubMed
   • Google Scholar
8. 1. Crotty S

   (2019) T follicular helper cell biology: a decade of discovery and diseases

   Immunity 50:1132–1148.

   https://doi.org/10.1016/j.immuni.2019.04.011

   • PubMed
   • Google Scholar
9. 1. Dolton G
   2. Zervoudi E
   3. Rius C
   4. Wall A
   5. Thomas HL
   6. Fuller A
   7. Yeo L
   8. Legut M
   9. Wheeler S
   10. Attaf M
   11. Chudakov DM
   12. Choy E
   13. Peakman M
   14. Sewell AK

   (2018) Optimized Peptide-MHC multimer protocols for detection and isolation of autoimmune T-Cells

   Frontiers in Immunology 9:1378–1391.

   https://doi.org/10.3389/fimmu.2018.01378

   • PubMed
   • Google Scholar
10. 1. Ellebrecht CT
    2. Srinivas G
    3. Bieber K
    4. Banczyk D
    5. Kalies K
    6. Künzel S
    7. Hammers CM
    8. Baines JF
    9. Zillikens D
    10. Ludwig RJ
    11. Westermann J

    (2016) Skin microbiota-associated inflammation precedes autoantibody induced tissue damage in experimental epidermolysis bullosa acquisita

    Journal of Autoimmunity 68:14–22.

    https://doi.org/10.1016/j.jaut.2015.08.007

    • PubMed
    • Google Scholar
11. 1. Fähnrich A
    2. Krebbel M
    3. Decker N
    4. Leucker M
    5. Lange FD
    6. Kalies K
    7. Möller S

    (2017) ClonoCalc and ClonoPlot: immune repertoire analysis from raw files to publication figures with graphical user interface

    BMC Bioinformatics 18:164–170.

    https://doi.org/10.1186/s12859-017-1575-2

    • PubMed
    • Google Scholar
12. 1. Fähnrich A
    2. Klein S
    3. Sergé A
    4. Nyhoegen C
    5. Kombrink S
    6. Möller S
    7. Keller K
    8. Westermann J
    9. Kalies K

    (2018) CD154 costimulation shifts the local T-Cell receptor repertoire not only during thymic selection but also during peripheral T-Dependent humoral immune responses

    Frontiers in Immunology 9:1019–1030.

    https://doi.org/10.3389/fimmu.2018.01019

    • PubMed
    • Google Scholar
13. 1. Fazilleau N
    2. McHeyzer-Williams LJ
    3. Rosen H
    4. McHeyzer-Williams MG

    (2009) The function of follicular helper T cells is regulated by the strength of T cell antigen receptor binding

    Nature Immunology 10:375–384.

    https://doi.org/10.1038/ni.1704

    • PubMed
    • Google Scholar
14. 1. Gaide O
    2. Emerson RO
    3. Jiang X
    4. Gulati N
    5. Nizza S
    6. Desmarais C
    7. Robins H
    8. Krueger JG
    9. Clark RA
    10. Kupper TS

    (2015) Common clonal origin of central and resident memory T cells following skin immunization

    Nature Medicine 21:647–653.

    https://doi.org/10.1038/nm.3860

    • PubMed
    • Google Scholar
15. 1. Hammers CM
    2. Bieber K
    3. Kalies K
    4. Banczyk D
    5. Ellebrecht CT
    6. Ibrahim SM
    7. Zillikens D
    8. Ludwig RJ
    9. Westermann J

    (2011) Complement-fixing anti-type VII collagen antibodies are induced in Th1-polarized lymph nodes of epidermolysis bullosa acquisita-susceptible mice

    The Journal of Immunology 187:5043–5050.

    https://doi.org/10.4049/jimmunol.1100796

    • PubMed
    • Google Scholar
16. 1. He J
    2. Tsai LM
    3. Leong YA
    4. Hu X
    5. Ma CS
    6. Chevalier N
    7. Sun X
    8. Vandenberg K
    9. Rockman S
    10. Ding Y
    11. Zhu L
    12. Wei W
    13. Wang C
    14. Karnowski A
    15. Belz GT
    16. Ghali JR
    17. Cook MC
    18. Riminton DS
    19. Veillette A
    20. Schwartzberg PL
    21. Mackay F
    22. Brink R
    23. Tangye SG
    24. Vinuesa CG
    25. Mackay CR
    26. Li Z
    27. Yu D

    (2013) Circulating precursor CCR7(lo)PD-1(hi) CXCR5⁺ CD4⁺ T cells indicate tfh cell activity and promote antibody responses upon antigen reexposure

    Immunity 39:770–781.

    https://doi.org/10.1016/j.immuni.2013.09.007

    • PubMed
    • Google Scholar
17. 1. Horn HS

    (1966) Measurement of "Overlap" in Comparative Ecological Studies

    The American Naturalist 100:419–424.

    https://doi.org/10.1086/282436

    • Google Scholar
18. 1. Hwang S
    2. Palin AC
    3. Li L
    4. Song KD
    5. Lee J
    6. Herz J
    7. Tubo N
    8. Chu H
    9. Pepper M
    10. Lesourne R
    11. Zvezdova E
    12. Pinkhasov J
    13. Jenkins MK
    14. McGavern D
    15. Love PE

    (2015) TCR ITAM multiplicity is required for the generation of follicular helper T-cells

    Nature Communications 6:6982–6995.

    https://doi.org/10.1038/ncomms7982

    • PubMed
    • Google Scholar
19. 1. Irla M
    2. Guenot J
    3. Sealy G
    4. Reith W
    5. Imhof BA
    6. Sergé A

    (2013) Three-dimensional visualization of the mouse Thymus organization in health and immunodeficiency

    The Journal of Immunology 190:586–596.

    https://doi.org/10.4049/jimmunol.1200119

    • PubMed
    • Google Scholar
20. 1. Itano AA
    2. Jenkins MK

    (2003) Antigen presentation to naive CD4 T cells in the lymph node

    Nature Immunology 4:733–739.

    https://doi.org/10.1038/ni957

    • PubMed
    • Google Scholar
21. 1. Iwata H
    2. Bieber K
    3. Tiburzy B
    4. Chrobok N
    5. Kalies K
    6. Shimizu A
    7. Leineweber S
    8. Ishiko A
    9. Vorobyev A
    10. Zillikens D
    11. Köhl J
    12. Westermann J
    13. Seeger K
    14. Manz R
    15. Ludwig RJ

    (2013) B cells, dendritic cells, and macrophages are required to induce an autoreactive CD4 helper T cell response in experimental epidermolysis bullosa acquisita

    The Journal of Immunology 191:2978–2988.

    https://doi.org/10.4049/jimmunol.1300310

    • Google Scholar
22. 1. Jenkins MK
    2. Moon JJ

    (2012) The role of naive T cell precursor frequency and recruitment in dictating immune response magnitude

    The Journal of Immunology 188:4135–4140.

    https://doi.org/10.4049/jimmunol.1102661

    • Google Scholar
23. 1. Knowlden ZA
    2. Sant AJ

    (2016) CD4 T cell epitope specificity determines follicular versus non-follicular helper differentiation in the polyclonal response to influenza infection or vaccination

    Scientific Reports 6:28287–28300.

    https://doi.org/10.1038/srep28287

    • PubMed
    • Google Scholar
24. 1. Lefranc MP
    2. Giudicelli V
    3. Busin C
    4. Malik A
    5. Mougenot I
    6. Déhais P
    7. Chaume D

    (1995) LIGM-DB/IMGT: an integrated database of ig and TcR, part of the immunogenetics database

    Annals of the New York Academy of Sciences 764:47–49.

    https://doi.org/10.1111/j.1749-6632.1995.tb55805.x

    • PubMed
    • Google Scholar
25. 1. Li KP
    2. Fähnrich A
    3. Roy E
    4. Cuda CM
    5. Grimes HL
    6. Perlman HR
    7. Kalies K
    8. Hildeman DA

    (2017) Temporal expression of bim limits the development of Agonist-Selected thymocytes and skews their TCRbeta repertoire

    Journal of Immunology 198:257–269.

    https://doi.org/10.4049/jimmunol.1601200

    • PubMed
    • Google Scholar
26. 1. Madi A
    2. Shifrut E
    3. Reich-Zeliger S
    4. Gal H
    5. Best K
    6. Ndifon W
    7. Chain B
    8. Cohen IR
    9. Friedman N

    (2014) T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity

    Genome Research 24:1603–1612.

    https://doi.org/10.1101/gr.170753.113

    • PubMed
    • Google Scholar
27. 1. Madi A
    2. Poran A
    3. Shifrut E
    4. Reich-Zeliger S
    5. Greenstein E
    6. Zaretsky I
    7. Arnon T
    8. Laethem FV
    9. Singer A
    10. Lu J
    11. Sun PD
    12. Cohen IR
    13. Friedman N

    (2017) T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

    eLife 6:e22057.

    https://doi.org/10.7554/eLife.22057

    • PubMed
    • Google Scholar
28. 1. Meli AP
    2. King IL

    (2015) Identification of mouse T follicular helper cells by flow cytometry

    Methods in Molecular Biology 1291:3–11.

    https://doi.org/10.1007/978-1-4939-2498-1_1

    • PubMed
    • Google Scholar
29. 1. Merkenschlager J
    2. Finkin S
    3. Ramos V
    4. Kraft J
    5. Cipolla M
    6. Nowosad CR
    7. Hartweger H
    8. Zhang W
    9. Olinares PDB
    10. Gazumyan A
    11. Oliveira TY
    12. Chait BT
    13. Nussenzweig MC

    (2021) Dynamic regulation of T_FH selection during the germinal centre reaction

    Nature 591:458–463.

    https://doi.org/10.1038/s41586-021-03187-x

    • PubMed
    • Google Scholar
30. 1. Morisita M

    (1962) I σ-Index, a measure of dispersion of individuals

    Researches on Population Ecology 4:1–7.

    https://doi.org/10.1007/BF02533903

    • Google Scholar
31. 1. Natarajan K
    2. Jiang J
    3. May NA
    4. Mage MG
    5. Boyd LF
    6. McShan AC
    7. Sgourakis NG
    8. Bax A
    9. Margulies DH

    (2018) The role of molecular flexibility in antigen presentation and T cell Receptor-Mediated signaling

    Frontiers in Immunology 9:1657.

    https://doi.org/10.3389/fimmu.2018.01657

    • PubMed
    • Google Scholar
32. 1. Nazarov VI
    2. Pogorelyy MV
    3. Komech EA
    4. Zvyagin IV
    5. Bolotin DA
    6. Shugay M
    7. Chudakov DM
    8. Lebedev YB
    9. Mamedov IZ

    (2015) tcR: an R package for T cell receptor repertoire advanced data analysis

    BMC Bioinformatics 16:175–180.

    https://doi.org/10.1186/s12859-015-0613-1

    • PubMed
    • Google Scholar
33. 1. Niebuhr M
    2. Kasperkiewicz M
    3. Maass S
    4. Hauenschild E
    5. Bieber K
    6. Ludwig RJ
    7. Westermann J
    8. Kalies K

    (2017) Evidence for a contributory role of a xenogeneic immune response in experimental epidermolysis bullosa acquisita

    Experimental Dermatology 26:1207–1213.

    https://doi.org/10.1111/exd.13439

    • PubMed
    • Google Scholar
34. 1. Niebuhr M
    2. Bieber K
    3. Banczyk D
    4. Maass S
    5. Klein S
    6. Becker M
    7. Ludwig R
    8. Zillikens D
    9. Westermann J
    10. Kalies K

    (2020) Epidermal damage induces Th1 polarization and defines the site of inflammation in murine epidermolysis bullosa acquisita

    Journal of Investigative Dermatology 140:1713–1722.

    https://doi.org/10.1016/j.jid.2020.01.022

    • Google Scholar
35. 1. Pellegrini M
    2. Nicolay U
    3. Lindert K
    4. Groth N
    5. Della Cioppa G

    (2009) MF59-adjuvanted versus non-adjuvanted influenza vaccines: integrated analysis from a large safety database

    Vaccine 27:6959–6965.

    https://doi.org/10.1016/j.vaccine.2009.08.101

    • PubMed
    • Google Scholar
36. 1. Qi H

    (2016) T follicular helper cells in space-time

    Nature Reviews Immunology 16:612–625.

    https://doi.org/10.1038/nri.2016.94

    • PubMed
    • Google Scholar
37. 1. Rao KV
    2. He YX
    3. Kalyanasundaram R

    (2003) Expression of a 28-kilodalton glutathione S-transferase antigen of Schistosoma mansoni on the surface of filamentous phages and evaluation of its vaccine potential

    Clinical and Vaccine Immunology 10:536–541.

    https://doi.org/10.1128/CDLI.10.4.536-541.2003

    • PubMed
    • Google Scholar
38. 1. Riteau N
    2. Radtke AJ
    3. Shenderov K
    4. Mittereder L
    5. Oland SD
    6. Hieny S
    7. Jankovic D
    8. Sher A

    (2016) Water-in-Oil-Only adjuvants selectively promote T follicular helper cell polarization through a type I IFN and IL-6-Dependent pathway

    The Journal of Immunology 197:3884–3893.

    https://doi.org/10.4049/jimmunol.1600883

    • PubMed
    • Google Scholar
39. 1. Ritvo PG
    2. Saadawi A
    3. Barennes P
    4. Quiniou V
    5. Chaara W
    6. El Soufi K
    7. Bonnet B
    8. Six A
    9. Shugay M
    10. Mariotti-Ferrandiz E
    11. Klatzmann D

    (2018) High-resolution repertoire analysis reveals a major bystander activation of tfh and tfr cells

    PNAS 115:9604–9609.

    https://doi.org/10.1073/pnas.1808594115

    • PubMed
    • Google Scholar
40. 1. Rius C
    2. Attaf M
    3. Tungatt K
    4. Bianchi V
    5. Legut M
    6. Bovay A
    7. Donia M
    8. Thor Straten P
    9. Peakman M
    10. Svane IM
    11. Ott S
    12. Connor T
    13. Szomolay B
    14. Dolton G
    15. Sewell AK

    (2018) Peptide-MHC class I tetramers can fail to detect relevant functional T cell clonotypes and underestimate Antigen-Reactive T cell populations

    The Journal of Immunology 200:2263–2279.

    https://doi.org/10.4049/jimmunol.1700242

    • PubMed
    • Google Scholar
41. 1. Sergé A
    2. Bailly AL
    3. Aurrand-Lions M
    4. Imhof BA
    5. Irla M

    (2015) For3D: full organ reconstruction in 3D, an automatized tool for deciphering the complexity of lymphoid organs

    Journal of Immunological Methods 424:32–42.

    https://doi.org/10.1016/j.jim.2015.04.019

    • PubMed
    • Google Scholar
42. 1. Shugay M
    2. Bagaev DV
    3. Zvyagin IV
    4. Vroomans RM
    5. Crawford JC
    6. Dolton G
    7. Komech EA
    8. Sycheva AL
    9. Koneva AE
    10. Egorov ES
    11. Eliseev AV
    12. Van Dyk E
    13. Dash P
    14. Attaf M
    15. Rius C
    16. Ladell K
    17. McLaren JE
    18. Matthews KK
    19. Clemens EB
    20. Douek DC
    21. Luciani F
    22. van Baarle D
    23. Kedzierska K
    24. Kesmir C
    25. Thomas PG
    26. Price DA
    27. Sewell AK
    28. Chudakov DM

    (2018) VDJdb: a curated database of T-cell receptor sequences with known antigen specificity

    Nucleic Acids Research 46:D419–D427.

    https://doi.org/10.1093/nar/gkx760

    • PubMed
    • Google Scholar
43. 1. Shulman Z
    2. Gitlin AD
    3. Targ S
    4. Jankovic M
    5. Pasqual G
    6. Nussenzweig MC
    7. Victora GD

    (2013) T follicular helper cell dynamics in germinal centers

    Science 341:673–677.

    https://doi.org/10.1126/science.1241680

    • PubMed
    • Google Scholar
44. 1. Sitaru C
    2. Chiriac MT
    3. Mihai S
    4. Büning J
    5. Gebert A
    6. Ishiko A
    7. Zillikens D

    (2006) Induction of complement-fixing autoantibodies against type VII collagen results in subepidermal blistering in mice

    The Journal of Immunology 177:3461–3468.

    https://doi.org/10.4049/jimmunol.177.5.3461

    • PubMed
    • Google Scholar
45. 1. Stamm C
    2. Barthelmann J
    3. Kunz N
    4. Toellner KM
    5. Westermann J
    6. Kalies K

    (2013) Dose-dependent induction of murine Th1/Th2 responses to sheep red blood cells occurs in two steps: antigen presentation during second encounter is decisive

    PLOS ONE 8:e67746.

    https://doi.org/10.1371/journal.pone.0067746

    • PubMed
    • Google Scholar
46. Software

    1. Team I

    (2019) immunarch: An R Package for Painless Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires, version 0.4.1

    Zenodo.

    https://doi.org/10.5281/zenodo.3383240
47. 1. Teraguchi S
    2. Saputri DS
    3. Llamas-Covarrubias MA
    4. Davila A
    5. Diez D
    6. Nazlica SA
    7. Rozewicki J
    8. Ismanto HS
    9. Wilamowski J
    10. Xie J
    11. Xu Z
    12. Loza-Lopez MJ
    13. van Eerden FJ
    14. Li S
    15. Standley DM

    (2020) Methods for sequence and structural analysis of B and T cell receptor repertoires

    Computational and Structural Biotechnology Journal 18:2000–2011.

    https://doi.org/10.1016/j.csbj.2020.07.008

    • PubMed
    • Google Scholar
48. 1. Textor J
    2. Fähnrich A
    3. Meinhardt M
    4. Tune C
    5. Klein S
    6. Pagel R
    7. König P
    8. Kalies K
    9. Westermann J

    (2018) Deep sequencing reveals transient segregation of T cell repertoires in splenic T cell zones during an immune response

    The Journal of Immunology 201:350–358.

    https://doi.org/10.4049/jimmunol.1800091

    • PubMed
    • Google Scholar
49. 1. Thieme M
    2. Bieber K
    3. Sezin T
    4. Wannick M
    5. Gupta Y
    6. Kalies K
    7. Ludwig RJ
    8. Mousavi S
    9. Zillikens D
    10. Sadik CD

    (2019) The Sphingosine-1-Phosphate receptor modulator fingolimod aggravates murine epidermolysis bullosa Acquisita

    Journal of Investigative Dermatology 139:2381–2384.

    https://doi.org/10.1016/j.jid.2019.03.1159

    • Google Scholar
50. 1. Thomas N
    2. Best K
    3. Cinelli M
    4. Reich-Zeliger S
    5. Gal H
    6. Shifrut E
    7. Madi A
    8. Friedman N
    9. Shawe-Taylor J
    10. Chain B

    (2014) Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence

    Bioinformatics 30:3181–3188.

    https://doi.org/10.1093/bioinformatics/btu523

    • PubMed
    • Google Scholar
51. 1. Tubo NJ
    2. Pagán AJ
    3. Taylor JJ
    4. Nelson RW
    5. Linehan JL
    6. Ertelt JM
    7. Huseby ES
    8. Way SS
    9. Jenkins MK

    (2013) Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection

    Cell 153:785–796.

    https://doi.org/10.1016/j.cell.2013.04.007

    • PubMed
    • Google Scholar
52. 1. Wittenbrink N
    2. Weber TS
    3. Klein A
    4. Weiser AA
    5. Zuschratter W
    6. Sibila M
    7. Schuchhardt J
    8. Or-Guil M

    (2010) Broad volume distributions indicate nonsynchronized growth and suggest sudden collapses of germinal center B cell populations

    The Journal of Immunology 184:1339–1347.

    https://doi.org/10.4049/jimmunol.0901040

    • Google Scholar
53. 1. Zarnitsyna VI
    2. Evavold BD
    3. Schoettle LN
    4. Blattman JN
    5. Antia R

    (2013) Estimating the diversity, completeness, and cross-reactivity of the T cell repertoire

    Frontiers in Immunology 4:485.

    https://doi.org/10.3389/fimmu.2013.00485

    • PubMed
    • Google Scholar
54. 1. Zhu J
    2. Yamane H
    3. Paul WE

    (2010) Differentiation of effector CD4 T cell populations (*)

    Annual Review of Immunology 28:445–489.

    https://doi.org/10.1146/annurev-immunol-030409-101212

    • PubMed
    • Google Scholar
55. 1. Zvyagin IV
    2. Tsvetkov VO
    3. Chudakov DM
    4. Shugay M

    (2020) An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity

    Immunogenetics 72:77–84.

    https://doi.org/10.1007/s00251-019-01139-4

    • PubMed
    • Google Scholar

Author details

1. Markus Niebuhr

   Institute for Anatomy, University of Lübeck, Lübeck, Germany

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

2. Julia Belde

   Institute for Anatomy, University of Lübeck, Lübeck, Germany

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

3. Anke Fähnrich

   1. Institute for Anatomy, University of Lübeck, Lübeck, Germany
   2. Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany

   Contribution

   Data curation, Formal analysis, Validation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1904-9544

4. Arnauld Serge

   Laboratoire Adhésion et Inflammation, Inserm U1067 CNRS, Aix-Marseille Université, Marseille, France

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1280-6277

5. Magali Irla

   Centre d'Immunologie de Marseille Luminy (CIML), INSERM U1104, Aix-Marseille Université UM2, Marseille, France

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8803-9708

6. Christoph T Ellebrecht

   1. Institute for Anatomy, University of Lübeck, Lübeck, Germany
   2. Department of Dermatology, University of Pennsylvania, Philadelphia, United States

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

7. Christoph M Hammers

   1. Institute for Anatomy, University of Lübeck, Lübeck, Germany
   2. Department of Dermatology, University of Lübeck, Lübeck, Germany

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5631-2415

8. Katja Bieber

   Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3855-6683

9. Jürgen Westermann

   Institute for Anatomy, University of Lübeck, Lübeck, Germany

   Contribution

   Resources, Funding acquisition

   Competing interests

   No competing interests declared

10. Kathrin Kalies

    Institute for Anatomy, University of Lübeck, Lübeck, Germany

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    kathrin.kalies@uni-luebeck.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8929-4249

• 893

  views

• 91

  downloads

• 3

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.