Evolution of the tuberculin skin test reveals generalisable Mtb-reactive T cell metaclones

Domain: physics
Published: 2026-01-21
Editorial outcome: accept

Abstract
T cells contribute to immune protection and pathogenesis in tuberculosis, but measurements of polyclonal responses have failed to resolve correlates of outcome. We report the temporal evaluation of the human in vivo clonal repertoire of Mycobacterium tuberculosis (Mtb)-reactive T cell responses, by T cell receptor (TCR) sequencing at the site of the tuberculin skin test, as a model for a standardised antigenic challenge. Initial non-selective recruitment of T cells is followed by enrichment of Mtb-reactive clones arising from oligo-clonal T cell proliferation. We introduce a modular computational pipeline, Metaclonotypist, to sensitively cluster distinct TCRs with shared epitope specificity, which we apply here to establish a catalogue of public Mtb-reactive HLA-restricted T cell metaclones. Although most in vivo Mtb-reactive T cells are private, 10 metaclones were sufficient to identify Mtb-T cell reactivity across our study population (N ≥ 128), indicating striking population level immunodominance of specific TCR-peptide interactions that may inform patient stratification and vaccine development.

Introduction
Mycobacterium tuberculosis (Mtb) remains the commonest cause of death worldwide, but most incident infections do not progress to tuberculosis (TB) disease1,2. Immunodeficiency is associated with increased risk of disease, indicating a role for protective immunity. However, disease is mediated by immunopathology, associated with a failure to restrict Mtb growth. Better understanding of the immune correlates of protection and pathogenesis remain global research priorities to inform novel approaches to disease-risk stratification in Mtb infected people, vaccine development and evaluation, and identification of targets for host-directed therapies.

T cells are essential for protective immunity to TB. They are thought to augment bacterial restriction within intracellular niches such as macrophages3,4. T cell-mediated protection against TB is evident in increased disease risk associated with genetic deficiencies of IL-12 and IFNγ signalling5, T cell depletion in people living with HIV6, and experimental T cell depletion in non-human primates7,8. Yet, frequency of circulating Mtb-reactive T cells, and limited analysis of their functional attributes (cytokine production or cytolytic degranulation) do not predict natural or vaccine-inducible protective immunity in humans4. We have also reported evidence for T cell-mediated pathogenesis in TB, illustrated by enrichment of IL-17 producing T cells in people with disease compared to those who have controlled infection9, disease triggered by checkpoint inhibitor therapies that increase effector T cell function10 and direct stimulation of Mtb-growth by IFNγ11.

T cells exist as clonal populations identified by their T cell receptor (TCR), most commonly composed of αβ heterodimers produced by imprecise somatic gene recombination during T cell development and responsible for signalling T cell activation following recognition of antigen bound to MHC molecules. Mtb proteome-wide studies have identified immunodominant protein antigens12–14. To date, use of whole protein or pooled peptide antigens to quantify Mtb-reactive T cells has not resolved correlates of protection and pathogenesis, potentially because they measure polyclonal responses in which responses to distinct peptide-MHC targets have differential effects on outcome. TCR sequencing enables an antigen-agnostic approach to resolve clonal T cell responses. This has provided proof of concept for potential correlates of outcome15, but the generalisability of these findings is not known.

Importantly, studies of human T cell biology in TB have relied heavily on investigation of Mtb reactive T cells from blood samples that are limited by sampling depth, because they contain <0.001% of the T cell clonal repertoire of an individual16, only a small fraction of which is Mtb reactive. Alternatively, investigation of T cells from the site of disease, such as bronchoalveolar lavage specimens or tissue biopsies, can enrich for the antigen-specific cells of interest, but is confounded by the chronicity of infection and pathological processes. We have addressed these limitations by profiling immune responses at the site of the tuberculin skin test (TST)9,17–19. Tuberculin is a standardised clinical grade preparation of purified protein derivative (PPD) from Mtb. Inflammatory induration at the site of the TST after 2–3 days has been used extensively as a classical model of delayed type hypersensitivity dependent on T cell priming, and therefore a measure of T cell memory for Mtb antigens contained in PPD. We have previously used this model to quantify T cell recruitment and function, and to reveal exaggerated IL17 activity associated with active TB disease, which could not be detected in blood9. The clonal repertoire of the T cell response to Mtb challenge in vivo has not previously been systematically evaluated. The extent to which these responses converge onto dominant T cell clones, and whether these are generalisable or idiosyncratic within a population are not known.

We addressed these questions by TCR sequencing of biopsies from the site of the TST in a cohort of 223 individuals. This approach provided us with a sensitive, unbiased quantitation of T cell clones recruited and expanded in response to a standardised in vivo challenge. To evaluate convergence of the response to immunodominant epitopes, we developed Metaclonotypist, a modular bioinformatics pipeline for the grouping of TCR sequences based on sequence similarity. Using this pipeline, we discover a dominant immune response to TB driven by highly public HLA-associated TCR metaclonotypes, which we expect to be a valuable resource for future biomarker discovery and reverse epitope discovery efforts in tuberculosis.

Results
Transcriptome-wide evaluation of maturation of the immune response to TST from day 2 to day 7
Inflammatory induration in the TST is maximal at 2–3 days, but previous flow cytometric evaluation of T cells at the site of the TST reported maximal accumulation of antigen-specific T cell responses at 7 days20. Therefore, we investigated the evolution of the T cell response by bulk RNAseq and TCRseq in day 2 and day 7 TSTs (Supplementary Fig. 1). We recruited healthy volunteers with evidence of peripheral blood Mtb-reactive T cells identified during occupational health screening, TB index case contact screening or recent migrant screening, to undergo a TST in each arm (Table 1). The TST site was sampled on day 2 at one site and on day 7 at the contralateral site. Genome-wide TST-response transcriptomes at day 2 and day 7 were defined by differential gene expression compared to transcriptomes from the site of control saline injections performed in a separate set of volunteers. The TST-response transcriptomes at each time point were used to infer activity of immune response pathways at the level of cytokines, receptors, kinases and transcription factors. Both day 2 and day 7 TST transcriptomes showed activation of a comparable repertoire of canonical immune signalling pathways (Supplementary Fig. 2A, B). We next identified gene expression modules associated with individual upstream regulators, which were significantly upregulated in integrated data from day 2 and day 7 TST-response transcriptomes. We found that most module expression decreased between day 2 and day 7 (Supplementary Fig. 2C) consistent with homeostatic resolution of inflammatory changes. A small number of modules showed higher expression at day 7. These were all identified as being regulated by transcription factors known to be involved in cell cycle regulation (Supplementary Fig. 2D).

Direct comparison of day 2 and day 7 TST-response transcriptomes also revealed differences in expression at the level of individual genes (Supplementary Fig. 2E). Pathway enrichment analysis of the differentially expressed genes indicated increased cell cycle and mitotic activity in the day 7 TST (Supplementary Fig. 2F), suggestive of increased cell proliferation at this time point. Therefore, we tested the hypothesis that the evolution of transcriptional changes between the day 2 and day 7 TSTs reflected T cell proliferation by quantifying the correlation between independently derived gene expression modules for cellular proliferation and selected T cell and non-T cell subsets. By comparison to day 2 profiles, the transcriptomes from day 7 TSTs showed significantly higher expression of the modules for cell proliferation (Fig. 1A), pan-T cell, CD4 T cells and NK cells, but not modules for CD8 T cells or myeloid cells (Fig. 1B). The cell proliferation module showed greatest correlation to the pan-T cell and CD4 T cell modules (Fig. 1C). Clinical induration at TST sites on day 2 was positively correlated with multiple cell types at day 2, but only T cell accumulation at day 7, as measured by cell-type specific gene expression of modules (Supplementary Table 1).

Evolution of reduced T cell clonal diversity in the TST
To study the nature of the T cell proliferation response revealed by our transcriptional analysis, we tracked the temporal evolution of the T cell clonal repertoire in the TST by TCRα and TCRβ sequencing of bulk RNA from day 2 and day 7 TSTs (Table 2 and Supplementary Fig. 1). We compared TCR repertoire diversity metrics to those of unstimulated peripheral blood samples from the same population of study participants (Fig. 2A). We display metrics for the TCRβ-chain, which is more diverse and informative about TCR antigen specificity21, but found concordant results for metrics calculated on TCRα-chain repertoires (Supplementary Fig. 3A). The median number of total β-chain TCRs obtained was 267,057 (range 113–566,403) for day 7 TSTs; 53,756 (range 2596–100,381) for day 2 TSTs; and 66,452 (range 18,266–119,984) for peripheral blood samples. Since repertoire diversity metrics are affected by sequencing depth (Supplementary Fig. 3B, C), we excluded samples with very small repertoires and down-sampled repertoires to the same size (n = 16,000 total TCRs) prior to this analysis. Compared to blood, the day 2 TST repertoire had an increased frequency of TCR sequences with >1 copy and a greater inequality of clone sizes as measured by Gini index, indicative of recruitment of expanded memory T cell clones. Correspondingly, the number of unique TCR clones (Richness) was reduced compared to blood. In contrast, Shannon diversity did not differ significantly from blood at day 2 and Simpson diversity even slightly increased, indicative of limited clonal dominance at this time point. Day 7 TSTs showed still higher proportions of expanded TCR clones, further reduced richness and Shannon diversity, and were characterised by the emergence of clonal dominance in the TCR repertoire, measured as decreased Simpson diversity and increased Gini index. Taken together, these findings suggest that the selection of T cell clones recruited to the day 2 TST is not particularly stringent, but repertoires evolve towards oligoclonality as a result of selective CD4 T cell proliferation and clonal expansion.

Day 7 TST is highly enriched for expanded Mtb-reactive T cell clones
Since inflammatory responses in the TST are dependent on Mtb-reactive T cells, we tested the hypothesis that the day 2 and day 7 TST TCR repertoires are selectively enriched for Mtb-reactive T cells compared to blood samples from the same individuals. We tested this hypothesis first by evaluating enrichment of previously described Mtb-reactive CDR3 sequences. We compared the TST CDR3β-chain sequences to published CDR3 sequences derived from T cells with known antigen reactivity, including 32,869 reported Mtb-reactive TCRs identified by virtue of pMHC specificity or upregulation of T cell activation markers on ex vivo stimulation with Mtb15,22,23. Compared to blood, we found no enrichment for Mtb-reactive CDR3s among all TCRs or all expanded TCRs in the day 2 TST (Fig. 2B and Supplementary Fig. 4). This indicates that the early inflammatory response likely reflects the circulating frequency of Mtb-specific T cells in individuals with prior memory, rather than preferential recruitment of specific clones. However, there was statistically significant enrichment of published Mtb-reactive CDR3 sequences in day 7 TSTs compared to blood and to day 2 TSTs. As a comparison, we similarly calculated the enrichment of published CMV or EBV-reactive CDR3 sequences. Day 7 TSTs showed a statistically significant reduction in the relative frequencies of both CMV and EBV-reactive CDR3 sequences compared to blood and day 2 TSTs, consistent with larger clonal expansions of Mtb-reactive sequences. These clonal expansions were not explained by donor-unrestricted T cell responses to Mtb, since day 7 TSTs showed a significant reduction in the relative frequencies of TCRα sequences that match the gene usage of MAIT or iNKT cells, compared to day 2 TSTs and/or blood (Supplementary Fig. 5).

Next, we reasoned that antigen-driven selection of T cell responses in the TST would lead to increased functional convergence of TCR clones onto common CDR3 amino acid sequences24. This convergent sequence evolution was clearly evident within the repertoires of individual participants, which showed a progressive increase from blood to day 2 and then day 7 TST in coincidence probabilities (Fig. 2C and Supplementary Fig. 6A–C). Inter-individual analysis showed a more modest increase in average coincidence probability across day 2 TSTs from pairs of donors compared to blood, and no further significant increase in average coincidence probability among pairs of day 7 TSTs (Fig. 2D and Supplementary Fig. 6D–F). Interestingly, the variance of between donor coincidences increased substantially at day 7, compatible with differential skewing of the Mtb-reactive T cell repertoires among different individuals driven by diversity in MHC-restricted antigen presentation to T cells. To further test this hypothesis, we analysed how probabilities of inter-individual coincidence in day 7 TST TCR sequences depend on HLA allele sharing between pairs of individuals (Fig. 2E and Supplementary Fig. 6G–I). We found that pairs of individuals sharing multiple MHC class II or class I alleles had substantially more similar day 7 TST repertoires. The dependence of repertoire overlap on HLA similarity was fourfold stronger with MHC class II, consistent with a predominantly CD4 T cell response in the TST.

In view of the potential for inter-individual diversity of the Mtb-reactive TCR repertoire, we reasoned that evaluation of day 7 TSTs using published Mtb-reactive CDR3 sequences may substantially underestimate the enrichment of Mtb-reactive T cell clones because this analysis is inherently restricted to public TCRs. Therefore, we experimentally validated Mtb-reactive TCRs at the level of individual participants. We sequenced TCRs of peripheral blood mononuclear cells (PBMC) from a sample of the study population, following ex vivo stimulation for 6 days with PPD, or tetanus toxoid (TT) as antigen control, and selected all the CDR3s which expanded eightfold or more in the PPD-stimulated cultures but not the control unstimulated cultures25,26. The expanded PPD-reactive CDR3 sequences showed limited publicity among the sub-sampled study participants (Fig. 3A, B and Supplementary Fig. 7A, B). We therefore refer to these PPD-reactive CDR3 sequences as private. We then looked for overlap between the CDR3 sequences of in vitro expanded T cells and the TST repertoires from the same participant. There was no enrichment of ex vivo PPD-expanded CDR3s in the day 2 TST repertoires compared to unstimulated blood (Fig. 3C, D and Supplementary Fig. 6C–H). Both in blood and the day 2 TST, ex vivo PPD-expanded CDR3s were present in significantly greater proportion amongst TCR sequences with a count >1 (Fig. 3C, D and Supplementary Fig. 7C–H), suggesting that the PPD-reactive CDR3s were predominantly expanded, as would be expected for memory T cells26. We found significantly greater enrichment of ex vivo PPD-expanded CDR3s in day 7 TSTs than in unstimulated blood repertoires or in day 2 TST repertoires. This pattern remained the same whether overlap was calculated for total CDR3 sequences or for unique CDR3 sequences (Fig. 3C, D and Supplementary Fig. 7C–H). This enrichment of private PPD-reactive CDR3s in day 7 TSTs was further increased among the most expanded TCRs. No similar enrichment of ex-vivo TT-expanded CDR3s was observed. Additionally, the odds ratio (OR) for the overlap with ex vivo PPD-reactive CDR3s was substantially greater among CDR3s which significantly expanded between day 2 and day 7 TSTs, compared to non-expanded CDR3 sequences (Supplementary Fig. 8). Taken together, these results indicate that antigen non-specific accumulation of T cells in the day 2 TST is largely replaced by expanded Mtb-reactive T cell clones by day 7 post-TST.

Identification of Mtb-reactive metaclones in the day 7 TST
Identification of T cell metaclones, defined by similar but non-identical CDR3 sequences, which share specificity for the same peptide-MHC, can address the limitations of inter individual TCR sequence diversity and enable antigen-agnostic identification of generalisable T cell responses to specific pMHC targets24,27,28. The identification of metaclones involves clustering of TCRs by sequence similarity, followed by a test for HLA-association across a cohort (Fig. 4A). A number of approaches to clustering of TCR sequences have been proposed. Among these, the GLIPH2 algorithm has already been used to identify HLA-restricted Mtb-reactive T cell metaclones defined by sequence motifs15,28. However, selecting an appropriate clustering method for a given dataset remains a challenge, with no broad consensus on best practices29,30. A key difficulty lies in evaluating clustering performance, particularly in balancing the trade-off between sensitivity (for clusters often measured by retention) and positive predictive value (for clusters typically assessed as purity). Effective benchmarking of clustering algorithms, therefore, requires comparing the maximum achievable purity at fixed levels of retention. However, many existing tools generate only a single clustering solution and lack flexibility to produce clusters at multiple resolutions.

To address this limitation, we developed Metaclonotypist, a modular pipeline for metaclone discovery that allows easy substitution of sequence similarity measures, thresholding choices, and clustering algorithms. Using published sets of TCRβ sequences of known epitope specificity from the VDJdb database23, we used Metaclonotypist to systematically identify Pareto optimal algorithmic approaches (Fig. 4A). Using connected component clustering, we found that adjacency graphs constructed by thresholding pairwise CDR3 edit distances were inferior to those built using TCRdist, a metric that incorporated both CDR3 and V-gene similarity (Fig. 4B). At high levels of retention, we observed that Leiden clustering outperforms simple connected components clustering, as it breaks down large components into more modular, coherent clusters.

Based on these findings, we applied Leiden clustering to TCRdist adjacency graphs to analyse the day 7 TST TCRβ repertoires that are highly enriched for Mtb-reactive T cells, but for which the specific epitopes being recognised are unknown. To ensure scalability given the large number of TCRs to be clustered in our dataset, we leveraged our previously developed symmetric deletion lookup algorithm to rapidly identify candidate TCR neighbours in adjacency graphs31. We selected the TCRdist threshold, which identified the largest number of TCR clusters in our dataset that were significantly enriched for individuals with a shared HLA allele (Fig. 4C). The stringency of the false discovery rate control was tested by showing that random shuffling of the HLAs associated with each individual returned no HLA-enriched T cell metaclones regardless of TCRdist threshold. For the TCRβ repertoires, the optimal TCRdist threshold was 15, identifying 180 HLA-associated metaclones, of which 177 were restricted by HLA class II molecules. This is consistent with the known predominance of CD4 T cells in the PPD-reactive TST response, which are restricted by HLA class II. Among these, HLA-DRB1 associated metaclones were most frequent (Fig. 4D).

At the optimal threshold, >95% of study participants contributed to at least one T cell metaclone (Fig. 4C, middle panel). The identified single chain metaclones can be represented as sequence motifs and visualised as adjacency graphs in which labelling of the individual TCR sequence by the donor reveal substantial publicity (Fig. 4E and Supplementary Fig. 9B). We therefore consider metaclones a measure of public reactivity to an unknown peptide:MHC complex. However, only approximately 2.7% of unique TCR sequences were incorporated into HLA-associated metaclones, consistent with the hypothesis that the majority of Mtb reactive TCRs are private (Fig. 4C, right panel). We benchmarked our metaclone discovery pipeline against clusters identified by the GLIPH2 algorithm. After exploring different GLIPH2 filters (Supplementary Fig. 10A), we retained clusters which passed the same stringent HLA association test with multiple testing correction applied in the Metaclonotypist pipeline. These GLIPH2 clusters (Supplementary Data 7, 8) also primarily associated with class II alleles, which demonstrates the robustness of our findings with respect to the algorithmic approach. However, Metaclonotypist clustered almost three times as many unique CDR3s compared to GLIPH2, suggesting that Metaclonotypist offers increased sensitivity to detect metaclone associations (Fig. 4F). Indeed, comparison of CDR3s clustered by Metaclonotypist or GLIPH2 with VDJdb MHC class II-restricted β-chain CDR3 entries annotated as Mtb-reactive found three matches in the GLIPH2 output, but an additional match in the Metaclonotypist output (Fig. 4F and Supplementary Fig. 9A).

Validation of Mtb-reactivity of day 7 TST-derived T cell metaclones and population level immunodominance of Mtb-derived epitopes
To confirm that our day 7 TST-derived, class II associated T cell metaclones represented public Mtb-reactive T cell responses, we calculated their enrichment in independent TCR sequencing data derived from people with TB compared to other diseases, or at the site of TB disease compared to blood. Enrichment in this analysis suggests specificity for Mtb-reactive TCRs. For internal validation, we first confirmed that the day 7 TST metaclones identified by Metaclonotypist were significantly enriched in PBMC stimulated with PPD compared to PBMC stimulated with tetanus toxoid from the same study population (Fig. 5). For external validation, we then showed they were significantly enriched in peripheral blood single cell sequencing data derived from patients with TB compared to SARS-CoV-2 infection; bulk TCR sequencing of blood and lung tissue from patients with TB compared to cancer diagnoses; single cell TCR sequencing of lung tissue from patients with TB compared to cancer diagnoses; and in bulk TCR sequencing of CD4 T cells from the site of pulmonary TB disease compared to blood of the same patients (Fig. 5). We benchmarked this analysis for publicity and Mtb-reactivity of metaclones identified using Metaclonotypist against metaclones identified by the GLIPH2 algorithm. Metaclonotypist motifs showed comparable enrichment to CDR3β sequences from GLIPH2 clusters with statistically significant class II HLA-allele associations (Fig. 5; blue vs. red), suggesting that Metaclonotypist retains similarly high specificity, despite clustering a larger proportion of TCRs into metaclones (Fig. 4F). Additional GLIPH2 filters did not improve performance (Supplementary Fig. 10B). Importantly, amino acid metaclone motifs as defined by GLIPH2 were not substantially enriched in TB samples in these datasets (Fig. 5; light-brown), indicating the need for more restrictive motif definitions. Interestingly, compared to metaclones, we found substantially lower enrichment of the full discovery set of expanded day 7 TST β TCRs (Fig. 5; grey vs. blue), suggesting that a substantial proportion of day 7 TST TCR clones may not be specific to Mtb, and that identification of metaclones significantly improves antigen-agnostic enrichment of the Mtb-reactive T cell response.

Having confirmed that class II associated day 7 TST Metaclonotypist metaclones represented public Mtb-reactive TCRs, we investigated their emergence in the TST over time. Despite some evidence for between-donor convergence of TCR sequences suggesting emergence of public T cell responses in the day 2 TST (Fig. 2D), enrichment of T cell metaclones at day 2 compared to blood did not reach statistical significance (Fig. 6A). In contrast, at day 7 metaclones were about tenfold enriched in the TST samples compared to blood and day 2 TST repertoires (Fig. 6A and Supplementary Fig. 11), and the percentage of day 7 TST repertoires captured by metaclones was positively correlated with the TST induration measured at day 2 (Supplementary Table 1). However, putative Mtb-reactive metaclones still only represented the minority of the total TCR sequenced data at day 7. Consistent with this predominantly private response, we found no significant enrichment of previously published Mtb-reactive CDR3 sequences in the day 7 TST when these data were sub-sampled to the same number of CDR3s included within metaclones (Supplementary Fig. 12). Therefore, despite oligoclonal restriction of the T cell repertoire over time in the TST, the day 7 repertoire may retain a substantial breadth of epitope diversity. For the subset of the study population in which we had identified PPD-reactive CDR3s in ex vivo stimulated PBMC, we compared the relative proportions of TST TCRs that either contained private PPD-reactive CDR3 sequences or clustered in public T cell metaclones at the level of individual participants. TCRs with private PPD-reactive CDR3 sequences were more frequent than public metaclone motifs in both day 2 and day 7 TSTs across the range of TCR clone sizes (Fig. 6B and Supplementary Fig. 13A). There was no statistically significant difference between expansion of private PPD-reactive and public metaclone TCRs between day 2 and day 7 TSTs, within the limitations of the sample size in this analysis (Supplementary Fig. 13B, C). Despite the dominance of private PPD-reactive TCRs at individual level, evaluation of the cumulative publicity of metaclones ranked by their publicity suggests that as few as 10 metaclones are sufficient to identify Mtb-T cell reactivity in the entire study population (Fig. 6C, Table 3, and Supplementary Fig. 14). At the level of individual metaclones, there was substantial heterogeneity in publicity even among individuals with the cognate HLA allele, offering the possibility of testing Mtb-reactive T cell metaclone responses as correlates of protection and pathogenesis (Fig. 6D).

Finally, we stratified participants into groups that were expected to be enriched for either recent or remote exposures, based on epidemiological criteria. We found a modest increase in TST induration in the recent exposure group, but found no differences for any of the TST metrics measured on the molecular level (Supplementary Fig. 15).

Methods
Study approvals
Research ethics and regulatory approvals for the present study were provided by UK National Research Ethics Service (NRES) Committee (Fulham) reference nos 11/LO/1863 and 18/LO/0680, and the NRES Committee (Camden and Islington) reference no 14/LO/0505. All study participants provided written informed consent. Participants were compensated for travel expenses.

Study population and sampling
Study participants comprised healthy HIV seronegative adults, 18–60 years of age, with immune memory for Mtb-specific antigens identified by positive peripheral blood IFNγ release assays using the QuantiFERON Gold Plus Test, but no clinical or radiological evidence of active tuberculosis. Male and female participants were included in this study, and sex was determined by self-reporting. The analyses were not disaggregated by sex because this was not a component of the research question. Peripheral blood mononuclear samples purified by Ficoll-Paque Plus (GE Healthcare Biosciences) density gradient centrifugation of whole blood, were collected on participant enrolment, and cryopreserved in foetal calf serum (FCS, Sigma) supplemented with 10% DMSO (Sigma). Blood samples were collected into Tempus RNA preservative tubes (Thermo Fisher Scientific, cat no. 4342792) and total RNA extracted with the Tempus Spin RNA Isolation Kit (Ambion; Life Technologies, cat no. 4380204). Globin mRNA and genomic DNA were removed using the Globin Clear kit (Thermo Fisher Scientific, cat no. AM1980) and TURBO DNA free kit (Ambion, Life Technologies, cat no, AM2238). Participants then received 0.1 ml intradermal injections of either 2 U tuberculin (Serum Statens Institute) into the volar aspect of each forearm, or control saline in one arm9,17,19. The precise injection sites were marked with permanent marker pen and used to position the site of 3mm punch biopsies at designated time points. Saline controls were biopsied on day 2, TST sites were biopsied on day 2 at one site and on day 7 at the contralateral site. Skin biopsies were placed in RNALater (Qiagen) and cryopreserved at −70°C, prior to RNA extraction. Skin biopsies were thawed at room temperature for 30 min and transferred into CK14 lysing tubes (Bertin Instruments) containing 350µl Buffer RLT supplemented with 1% 2-mercaptoethanol (Sigma). Samples were homogenized on a Precellys Evolution instrument for 6 cycles of 23s at 6300 rpm, with 2 min cooling on ice between cycles. Following centrifugation to pellet debris and beads, total RNA was purified from the supernatant using the RNeasy Micro Kit (Qiagen, cat no. 74004), according to the manufacturer’s instructions. Contaminating DNA was removed using the TURBO DNA free kit (Ambion, Life Technologies, cat no. AM2238). In a sensitivity analysis, participants were stratified by the indication for QuantiFERON testing, as surrogate for possible interval time from exposure. In this context, household contacts of active TB index cases were expected to be enriched for individuals with more recent exposures than individuals being tested via routine occupational health screening.
Participant demographics were collected in a RedCAP database, are summarised in Tables 1 and 2, and detailed on a per-sample level in Supplementary Data 1.

RNA sequencing and analysis
Total RNA from TSTs was subjected to genome wide mRNA sequencing. cDNA libraries were generated using the KAPA Hyperprep kit (Roche, cat no. 07962363001), and sequencing was performed on the Illumina Nextseq using the Nextseq 500 High Output 75 cycle kit (Illumina, cat no. 20024906) according to manufacturers’ instructions, providing a median of 22 million (range 10–50 million) 41 bp paired-end reads per sample. RNAseq data were mapped to the reference transcriptome (Ensembl Human GRCh38 release 111) using Kallisto (v0.46)39. Transcript-level counts were summed on gene level, and annotated with Ensembl gene ID, gene symbol and gene biotype using the R/Bioconductor packages tximport and BioMart. Raw counts of 23,820 Ensembl gene IDs, retained after exclusion of pseudogenes, were used for differential expression analysis with the SARtools (v1.8.1) implementation of DeSeq2 (v1.42.1)40, with a false discovery rate (FDR) <0.05 and log2 fold difference of ≥1. For all other analyses, raw counts were converted into transcripts per millions (TPM) values, and log2 transformed after the addition of a pseudocount of 0.001. Duplicated gene symbols were filtered by retaining the gene with highest expression per sample.
Upstream regulator analysis of the differentially expressed genes was performed using Ingenuity Pathway Analysis (Qiagen). This was visualised as a network diagram using the ForceAtlas2 algorithm in Gephi v0.9.4, and used to derive co-regulated gene-expression networks41. This analysis was restricted to upstream regulators predicted to be significantly activated (Z-score>2, adjusted p-value<0.05), targeting at least 4 downstream genes, and annotated with one of the following functions: cytokine, kinase, transmembrane receptor, and transcriptional regulator, representing the canonical components of pathways which execute transcriptional reprogramming in immune responses. For each upstream regulator, pairwise Spearman correlations of the TPM expression values of the target genes were calculated among TST samples. Upstream regulators were selected as significant if the average co-correlation was significantly (FDR<0.05) greater than the distribution of average correlation coefficients obtained from 100 iterations of selecting an equivalent number of random genes. Reactome pathway enrichment of differentially expressed genes was analysed with the XGR (v1.1.9) R package42. For visualisation, 20 pathway groups were identified by hierarchical clustering of Jaccard indices to quantify similarity between the gene compositions of each pathway. For each group, the pathway with the largest total number of genes was then selected to provide a representative annotation.
Transcriptional modules for T cell proliferation41 and cell types present in the TST32 have been derived and published previously. Their gene composition is listed in Supplementary Data 2. The expression of each module was quantified as the arithmetic mean log2 TPM value of its constituent genes.

Ex vivo PBMC stimulation
Frozen PBMC were thawed at 37°C, washed in RPMI-1640 media (Thermo Fisher Scientific) with 10% FCS, and resuspended at 106 cells/ml in RPMI with 5% heat-inactivated male human AB serum (Sigma). 2×105 PBMC were seeded into individual wells of round bottom 96-well plates (Thermo Fisher Scientific) with one of 10µg/mL purified protein derivative of Mtb (PPD; Serum Statens Institute), 100µg/mL tetanus toxoid (TT; NIBSC), or control buffer for 6 days at 37°C and 5% CO2. At the end of the culture period, cells were harvested, washed in PBS, and resuspended in 350µl RLT buffer (Qiagen). Samples from triplicate wells were pooled for RNA extraction using the RNeasy Micro kit (Qiagen, cat no. 74004). Upto 5 separate pooled samples were collected for each individual, for each stimulus.

T cell receptor (TCR) sequencing and analysis
RNA extracted from skin samples, ex vivo stimulated PBMC, and peripheral blood Tempus tubes were subjected to sequencing of TCR α and β genes using an established quantitative TCR sequencing pipeline that integrates experimental library preparation and computational analysis with DecombinatorV426,43,44, which defines and quantifies a TCR clone by its nucleotide sequence and reports its V, J and CDR3 annotation. To account for different sequencing depth between samples, repertoire metrics were calculated after downsampling all samples to 16,000 unique molecular identified (UMI) reads (Supplementary Fig. 1). Since the UMI-based method quantifies the number of mRNA transcripts, we note that clone size does not measure the number of T cells directly. Nevertheless, mRNA number is a good proxy for clone size and clonal expansion since T cells do not substantially change TCR mRNA levels upon activation26.
TCR α and β CDR3s from whole blood or skin biopsies were annotated as CMV, EBV or Mtb-reactive, if they were listed as sequences known to target these pathogens in the VDJdb TCR repository (https://vdjdb.cdr3.net/; accessed 01/10/2024)23,45, the McPAS database (http://friedmanlab.weizmann.ac.il/McPAS-TCR/; accessed 16/09/2023)22 or in Table S2 from Musvosvi et al.15. The collated antigen-reactive CDR3 sequences are summarised in Supplementary Data 3. To identify antigen-reactive CDR3s from in vitro cultures, we identified CDR3 sequences with ≥eightfold increased abundance in antigen stimulated, but not unstimulated PBMC compared to whole blood from the same individual26. CDR3s absent in matching blood were set to the median blood CDR3 abundance of 1 to allow expansion calculations for all in vitro CDR3s. To complement this analysis, we also used a more stringent definition of expanded T cell clones41,46. In this approach, CDR3s in Day 7 TST samples were defined as significantly expanded if their observed abundance was greater than expected using a Poisson distribution derived from Day 2 TST counts with FDR <0.1%.
MAIT TCR enrichment was assessed based on their TCR α gene usage as sequences containing TRAV1-2, paired with TRAJ12, TRAJ20 or TRAJ33; iNKT TCRs were identified as TCRs containing TRAV10 paired with TRAJ18; and GEM TCRs were identified as TCRs containing TRAV1-2 paired with TRAJ947.
TCR repertoire diversity was assessed by the number of expanded TCR sequences (count >1), Gini index (repertoire inequality), and Hill Diversity indices. These diversity indices are defined as Dq = (1/R * sum(pi^q))^(1/(1-q)), where R is the number of distinct TCRs, pi the clonal frequency of the i-th clone, and q a parameter that determines the relative weight put on clonal abundance. We compared richness (total number of distinct TCRs), D0 = R, Shannon Diversity (exponential of Shannon entropy), D1 = exp(-sum(pi * ln pi)), and Simpson diversity (inverse of Simpson’s index), D2 = 1/sum(pi^2). Among these measures Simpson diversity is most sensitive to clonal dominance, while Richness completely disregards variability in clonal expansions.
Within- and cross-donor convergence of TCR sequences was calculated as previously described24. Within-donor convergence was calculated as the proportion of all pairs of distinct clonotypes (as defined by nucleotide sequence identity) which were functionally convergent, i.e., that encode the same protein. We define ni as the number of distinct clonotypes encoding the i-th TCR with i = 1, ..., S, where S is the number of unique clonotypes. We also define N = sum(ni) as the total number of clonotypes in the sample. The probability of within-donor convergence is p^ = sum(ni * (ni-1)) / (N * (N-1)). For cross-donor pairing across samples, we define clonotypes by nucleotide sequence and donor identity. Defining nI,1 and nI,2 as the sampled counts of the i-th TCR in donor 1 and donor 2, respectively, we estimate the probability of cross-donor convergence using: p^C = sum(nI,1 * nI,2) / (N1 * N2), where N1 = sum(nI,1) and N2 = sum(nI,2) is the total number of clonotypes in the two samples.

HLA genotype imputations
DNA from participants was extracted from cryopreserved whole blood using the QIAamp spin column (Qiagen). Genotyping was conducted using the Illumina Infinium Global Diversity Array. HLA imputation was performed on the Michigan Imputation Server48 using genotyped autosomal variants across the study population, filtered to include only SNPs with a minor allele frequency of ≥5% and a call rate of ≥95%. Briefly, typed SNPs within the MHC region (6:27970031-33965553; hg19) were phased with Eagle (v2.4) and imputed using Minimac4 with the four-digit multi-ancestry HLA imputation reference panel (v2). Imputed SNPs with an imputation score (R²) <0.8 were excluded, resulting in high-confidence HLA alleles for 158 individuals (Supplementary Data 4).

Benchmarking TCR clustering approaches using Metaclonotypist
To compare TCR clustering approaches, we implemented Metaclonotypist, a modular computational pipeline for metaclonotype discovery, and benchmarked clustering performance using TCRs with known pMHC specificity from the VDJdb database23.
Metaclonotypist proceeds in a series of steps (Fig. 4A). Metaclonotypist first calculates pairwise distances between TCRs according to sequence similarity metrics, from simple Levenshtein edit distances applied to the CDR3 sequence to more advanced metrics such as TCRdist. This first step can be optionally sped up by pre-filtering of candidate sequence neighbour pairs using the symmetric deletion lookup algorithm. It next generates an adjacency graph between sequences, by thresholding the pairwise sequence similarity with respect to a tuneable threshold. Each node in this graph represents a TCR found in an individual’s repertoire, and edges connect all nodes with a similarity below the threshold. Within the graph Metaclonotypist then identifies putative metaclones by clustering. Clustering is performed using community detection algorithms as implemented in igraph49. Importantly, each step is modular and supports multiple choices to allow benchmarking of alternative approaches using different sequence similarity metrics, threshold choices for adjacency graph construction, and clustering algorithms.
To construct a benchmarking task, we selected data from all pMHCs with at least 220 associated TCR β sequences from VDJdb following filtering and data standardisation using tidytcells50. We then randomly down-sampled TCR repertoires from pMHCs with a greater number of sequences to obtain a dataset of 4840 TCR β sequences equally balanced across 22 pMHCs.
Clustering involves a multi-objective optimisation, with ideal clustering having both high purity and retention. To allow controlled comparisons across similarity metrics and clustering algorithms, we systematically varied distance thresholds for each method to be able to identify Pareto optimal solutions. We defined cluster purity as the weighted average of the dominant class frequency in each cluster: Purity = 1/N * sum(max(Cj ∩ Lj)), where N is the total number of TCRs, K the total number of clusters, Ck the set of TCRs associated with cluster k, and Lj the set of TCRs associated with label j (here representing a specific epitope). We defined clustering retention as the fraction of all TCRs assigned to non-singleton clusters: Retention = 1/N * sum(I(|Ck| > 1) * |Ck|), where I(|Ck| > 1) is an indicator function that is one if |Ck| > 1 and 0 otherwise.
Using this benchmarking approach, we compared connected component clustering of adjacency graphs based on CDR3 Levenshtein distance, which simply groups all TCRs connected by at least one edge into a cluster, to more advanced algorithms. Our results suggest that the more advanced TCR sequence similarity metric TCRdist is superior to simple Levenshtein distance calculated on the CDR3 alone. We furthermore found that Leiden clustering, which breaks up large connected components into multiple clusters where this increases cluster modularity, maintains higher purity at larger thresholds.

Discovery of HLA-associated metaclones with Metaclonotypist
We considered metaclones as a set of β-chain TCR clones with an imputed common HLA-peptide specificity27. We identify putative metaclones by clustering TCRs based on sequence similarity and testing the HLA association of TCR clusters. We combined bulk-sequenced Day 7 TST repertoires for β chains. To reduce uneven sampling, we down-sampled larger repertoires to 10,000 total counts and excluded repertoires with <5000 total counts. To increase confidence of restricting metaclone discovery to Mtb-reactive TCRs, only those with count >1 in these down-sampled repertoires were selected for analysis. TCRs with CDR3 amino acid length ≤5 were excluded from analysis. We then identified all pairs of TCRs that differ by ≤2 edits in their CDR3 hypervariable region using the symmetric deletion lookup algorithm31. We next calculated TCRdist scores between these pre-pruned TCR pairs using TCRdist327. Based on our preliminary benchmarking, we used Leiden clustering for our identification of metaclonotypes in the day 7 TST (with parameters: resolution=0.1, objective_function=“CPM”, n_iterations=4). After examining the effect of varying thresholds, we represented the TCR repertoire as an undirected graph based on the sparse adjacency matrix obtained by thresholding TCRdist scores ≤15.
Each cluster was tested for HLA association, by comparing the expression of specific HLA alleles by Fisher’s exact test between two groups of individuals: those contributing at least one TCR to a cluster and the remainder of the population. Associations for HLA class II alleles (DP, DQ, DR) and class I alleles (A, B, C) were tested separately. HLA-association of metaclones with the DQ locus was tested with respect to all potential DQ heterodimers, by combining DQ alleles for the α and β HLA chain to account for the highly polymorphic nature of both the α and β chain of HLA DQ. P values were corrected for multiple testing using the Bonferroni-Hochberg procedure at a False Discovery Rate (FDR) of 0.1, where the number of tests was set equal to the product of the number of tested clusters and times the number of tested HLA alleles. To limit multiple testing, we only assessed association of clusters containing TCRs from ≥4 individuals with HLAs found in ≥4 individuals across the population. Where more than one significant HLA association was found, the most significant one was considered as lead HLA association. As a control, the link between HLA haplotype and individuals was randomly shuffled.

Metaclone visualisation
Sequence logos were constructed in Python, using the seqlogos_vj plotting submodule of the pyrepseq package51. Graphs of TCR sequence similarity within a metaclone were visualised using Python bindings to the igraph package. Each node represents a TCR, and distinct colours are used to indicate donor origin. Nodes are connected by unweighted edges whenever corresponding TCRs were below the threshold of sequence similarity used for metaclone discovery.

GLIPH2 analysis
GLIPH2 analysis was undertaken on the same set of Day 7 TST β-chain repertoires as described for metaclonotype discovery above, using default settings and CD48_v2.0 reference. The Metaclonotypist approach was then mirrored by selecting only GLIPH2 similarity clusters containing TCRs from ≥4 individuals and testing for associations with HLAs found in ≥4 individuals (done separately for class I and class II alleles). We explored the effect of filtering the GLIPH2 output further, as described before15. Filter 1 selected clusters that consisted of ≥3 unique CDR3s and had a Fisher_score, vb_score and length_score ≤0.05 each. Filter 2 applied the Fisher’s exact test for HLA association, either with a significance threshold of p<0.05 (as used by Musvosvi et al.15) or with an FDR <0.1 as applied in the Metaclonotypist pipeline.

Quantification of Metaclonotypist and GLIPH2 metaclones
Supplementary Data 5 and 6 list the HLA class II and class I restricted β metaclones, respectively, as identified by Metaclonotypist from day 7 TST samples. The tables include significant HLA allele associations for each metaclone, as well as the associated V gene(s) and a regular expression for the clustered CDR3 amino acid sequences. To identify and quantify β-chain TCR sequences from various datasets that match a pre-defined class II associated metaclone in the context of the correct TCR chain, the V gene and CDR3 of each TCR was compared against the V gene and CDR3 regular expression of each metaclone.
Supplementary Data 7 and 8 list the HLA class II and class I restricted β chain GLIPH2 clusters, respectively, identified from day 7 TST samples. The tables include significant HLA allele associations, as well as the clustered CDR3 sequences and a regular expression for the shared CDR3 pattern. β-chain TCR sequences from various datasets were identified as matching a pre-defined class II associated GLIPH2 cluster in two different ways: (a) if their CDR3 amino sequence contained the regular expression of the GLIPH2 motif (GLIPH2 pattern G.T was excluded from analysis to increase specificity), (b) if their CDR3 amino acid sequence was part of the GLIPH2-clustered set of CDR3 sequences.

Datasets for external validation of TST-derived metaclones
Processed single cell TCR sequencing data from activated T cells following in vitro stimulation of PBMC from n=70 individuals with Mtb lysate were accessed from Supplementary Table S2 in the publication by Musvosvi et al.15. Only good quality cells (flag=GOOD) were included, resulting in 21,212 cells with β-chain data.
Processed single cell TCR sequencing data from activated T cells following in vitro stimulation of PBMC from n=16 individuals with SARS-CoV2 were provided by Lindeboom et al52. All longitudinal samples per patient were included, resulting in 149,208 cells with β-chain data.
Single cell TCR sequencing FASTQ data from human lung of n=5 TB patients53 were downloaded from the NCBI Gene Expression Omnibus resource (GSE253828) and processed with 10x Genomics CellRanger (v7.1.0) using the vdj pipeline and VDJ-T reference version 7.1. Single cell TCR data from filtered_contig_annotations.csv output files were integrated across all patients, resulting in 20,025 cells with β-chain data.
Single cell TCR sequencing FASTQ data from human lung of n=3 lung cancer patients54 were downloaded from the NCBI Gene Expression Omnibus resource (GSE154826) and processed with 10× Genomics CellRanger (v7.1.0) using the vdj pipeline and VDJ-T reference version 7.1. Single cell TCR data from filtered_contig_annotations.csv output files were integrated across all samples (including tumour and normal lung tissue) from all patients, resulting in 17,019 cells with β-chain data.
Processed bulk TCR sequencing data for lung tissue and whole blood from TB patients and cancer controls, as well as for sorted CD4 T cells from TB lung and TB blood were provided on Adaptive Biotechnologies’ ImmunoSEQ website (https://clients.adaptivebiotech.com/). The cohort has been previously described55, and an overview of utilised files and their metadata is provided in Supplementary Data 9. Only functional TCR sequences were included (sequenceStatus=In), and the vMaxResolved column was used as V gene annotation, but with the allele information excluded (e.g., TCRBV06-01*01 became TCRBV06-01). Since the ImmunoSeq naming of TCR V genes differs from the IMGT nomenclature used for metaclone definitions, V gene names were made compatible prior to searching for metaclone matches. This included, within the ImmunoSeq annotations, replacement of TCRBV with TRBV, and the removal of leading zeroes from V gene alleles (e.g., replacing TRBV06-06 with TRBV6-6). β-chain data were available for the lung dataset (n=13 TB patients and n=3 cancer controls), the blood dataset (n=11 TB patients and n=4 cancer controls), and the CD4 T cell dataset (n=5 TB patients). All samples per patient were included, and data integrated across disease and tissue groups, resulting in n=1,615,131 TB-associated and n=218,372 cancer-associated β TCRs for the lung dataset; n=1,081,593 TB-associated and n=735,834 cancer-associated β TCRs for the blood dataset; and n=336,787 lung-derived and n=219,541 blood-derived β TCRs for the CD4 T cell dataset.

Statistics and data visualisation
Analyses were performed in R (version 4.3.3) or Python (version 3.10.4). Data were visualised and figures assembled using R’s tidyverse (v2.0.0) and ggpubr (v0.6.0) packages, and Inkscape (v0.92). Statistical differences were assessed using the tests and significance thresholds stated in the text and figure legends. Wilcoxon tests with FDR correction for multiple testing were performed with the wilcox_test or pairwise_wilcox_test functions from the rstatix (v0.7.2) package in R. Base R functions cor() and lm() were used for Spearman correlation and linear regression analyses, respectively, with confint() to calculate confidence intervals for regression coefficients. R packages pheatmap (v1.0.12) and ComplexHeatmap (v.2.18.0) were used to create heatmaps. Odds ratios and their confidence intervals were calculated with the fisher.test() function from the stats (v4.3.3) R package. Metaclones were identified using Metaclonotypist (v1.0; written in Python) and visualised as described above. To visualise overlap between CDR3 sequences, an area-proportional Venn diagram was drawn with DeepVenn (https://arxiv.org/abs/2210.04597).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Thanks to the authors for replying to my comments so extensively, including additional analysis. I think these extra data have\nimproved the manuscript and clarified my initial concerns on TCR numbers.", "reviewer_2": "All of my comments have been addressed.", "reviewer_3": "Thank you for the revised manuscript and detailed response letter. While the majority of my comments have been addressed\nand the manuscript provides new insights into TCR recruitment to a temporal antigen challenge site, without paired TCR\ndata or experimental validation of HLA restriction/public clones, the applicably of these findings as a resource for biomarker\ndiscovery and reverse epitope discovery is limited and the work likely does not significantly advance the field.\nOutside of this, my only other suggestion is to include the additional details about the participant cohort in the results section\nof the paper (line 90). The authors response to Reviewer #1, that they are healthy volunteers with known exposures, was a\nhelpful qualification and I believe the manuscript will be strengthen with more upfront inclusion of this information."}, "author_response": "As requested, we have provided point by point responses to the reviewers’ comments and indicated the\nchanges we have made below.\nReviewer #1\n1. The manuscript by Turner et al described the TCR repertoire analysis in TST skin test samples in\ncomparison to blood and in vitro stimulated T-cells. The study is well described and describes some\ninteresting findings.\nWe thank the reviewer for the positive comment.\n2. In Figure 2A, the authors describe the diversity in the TCR repertoire at day 2 and day 7 post TST\nand conclude that the repertoire is smaller on day 2 and larger at day 7 perhaps due to clonal\nexpansion of the T-cells at the site of the TST. However, there is no mention of the number of T-\ncells or TCR sequences derived from the biopsies at either of these time points. As the reduced\ndiversity at day 2 could be the result from decreased frequencies of T-cell recruited into the TST\nsite, this should be taken into the equation and mentioned in the data description. Especially when\nassessing total numbers of unique clones etc total number of TCR sequences obtained is critical to\nknow.\nThe reviewer is correct in saying that repertoire diversity metrics are affected by the total number of TCR\nsequences. We address this by down-sampling the repertoires to the same number of TCR sequences\nas stated in line 127 and the legend for Figure 2. For clarification, we have now added summary statistics\nfor the total number of TCR sequences obtained for each sample type and include the total number of\nTCR sequences to which each repertoire was down-sampled in the Results text (lines 121-125).\n“The median number of total β-chain TCRs obtained was 267,057 (range 113-566,403) for day 7 TSTs;\n53,756 (range 2,596-100,381) for day 2 TSTs; and 66,452 (range 18,266-119,984) for peripheral blood\nsamples. Since repertoire diversity metrics are affected by sequencing depth (Supplementary Figure\n3B-C), we excluded samples with very small repertoires and down-sampled repertoires to the same size\n(n=16,000 total TCRs) prior to this analysis.”\nImportantly, day 7 TSTs have significantly lower diversity and a higher frequency of expanded TCRs\n(with count >1) supporting our conclusion that there is oligoclonal T cell expansion between day 2 and\nday 7.\n3. In Figure 2B, the authors claim a specific depletion of CMV and EBV specific TCRs at day 7 post\nTST. However as there is also an expansion of Mtb specific T-cells at this time point, thus there may\nnot be a change in the absolute number of CMV and EBV specific TCRs, but only proportional due\nto expansion of the other populations. This should be clarified, and ideally related to absolute counts\nfor the CMV and EBV specific TCRs.\nWe agree with the reviewer that the expansion of Mtb-reactive T cells might drive the apparent depletion\nof T cells of other specificities from the day 7 TST repertoire. We have amended the Results text in line\nwith this possible interpretation (lines 148-150):\n“Day 7 TSTs showed a statistically significant reduction in the relative frequencies of both CMV and\nEBV-reactive CDR3 sequences compared to blood and day 2 TSTs, consistent with larger clonal\nexpansions of Mtb-reactive sequences.\"\nWe have also amended our Discussion (lines 324-328) to discuss this point explicitly as follows (see\nalso response to comment 6):\n“Our data cannot fully discriminate the extent to which reduction of non-Mtb-reactive T cells at the TST\nsite by day 7 is due to their clearance or their relative decrease caused by the expansion of Mtb-reactive\nTCRs dominating the sampled repertoire. The latter process is supported by our observation that the\nfull set of day 7 TST TCRs shows substantially lower enrichment in independent TB-associated datasets\ncompared to day 7 TST metaclones.”\nThe analysis in Figure 2B was performed in equal-sized repertoires. Therefore, depicting the absolute\ncount of CMV and EBV specific CDR3 sequences, instead of their percentage, would have no impact\non the data or their interpretation.\nis confounded by the large technical variability in the number of recovered TCR sequences per sample.\nHowever, for the reviewer’s interest, we have calculated these numbers for the beta chain repertoire as\nfollows:\nBlood (n=20) TST_D2 (n=17) TST_D7 (n=165)\nAll TCRs\nCMV-reactive CDR3s Median: 546 Median: 601 Median: 574\nRange: 221-1436 Range: 21-1584 Range: 2-6598\nEBV-reactive CDR3s Median: 190.5 Median: 202 Median: 225\nRange: 63-1687 Range: 9-1499 Range: 2-3578\nMtb-reactive CDR3s Median: 898.5 Median: 743 Median: 2633\nRange: 265-2080 Range: 35-3964 Range: 3-34430\nExpanded TCRs\nCMV-reactive CDR3s Median: 240 Median: 368 Median: 500\nRange: 57-564 Range: 21-1375 Range: 0-6352\nEBV-reactive CDR3s Median: 87 Median: 148 Median: 199\nRange: 13-1588 Range: 9-1441 Range: 0-3411\nMtb-reactive CDR3s Median: 366 Median: 485 Median: 2430\nRange: 83-948 Range: 33-3674 Range: 0-33995\n4. A. Related to Figure 2C, but also in general, the participants in this study were selected on having\na positive QFT result in the absence of aTB, which will be a rather heterogenous group. Is anything\nknown on duration of QFT positivity and thus could data be correlated to recent vs remote infection?\nOr are absolute QFT results available and can heterogeneity of response at day 7 be correlated to\nmagnitude of QFT response? It seems, looking at the diversity in Fig 2C, but also some of the other\ndata points that donors have considerably different responses to the TST administration and wonder\nif any would correlate to the more routine clinical classifications.\nNo data are available for duration of QFT positivity or absolute QFT results. However, we can stratify\nparticipants into presumed ‘recent’ and ‘remote’ exposure groups, based on epidemiological criteria. We\nhave included an additional Supplementary Figure (Supplementary Figure 15) and amended the\nmanuscript to reflect these additional analyses.\nMethods lines 423-426: “In a sensitivity analysis, participants were stratified by the indication for\nQuantiFERON testing as surrogate for possible interval time from exposure. In this context, household\ncontacts of active TB index cases were expected to be enriched for individuals with more recent\nexposures than individuals being tested via routine occupational health screening.”\nResults lines 292-295:“Finally, we stratified participants into groups that were expected to be enriched\nfor either ‘recent’ or ‘remote’ exposures, based on epidemiological criteria. We found a modest increase\nin TST induration in the ‘recent exposure’ group, but found no differences for any of the TST metrics\nmeasured on the molecular level (Supplementary Figure 15).”\nTo summarise, no differences were found between household contacts of TB index cases (enriched for\nrecent exposures) and occupational health screening participants (enriched for remote exposures) in\nterms of a) the extent of inflammation in the TST measured by transcriptional modules, b) TCR sequence\ndiversity or co-incidence metrics in the TST on day 7, and c) abundance of Mtb-reactive TCRs in the\nday 7 TSTs (measured by public Mtb-reactive CDR3s, percentage Mtb-reactive metaclones, and\npercentage of expanded TCRs).\n2\n40\n0.0451\n30\n20\n10\nrecent vs. remote exposure to TB. Household contacts (HC) of active TB index cases were defined\nas recently exposed to TB, and participants whose immunological memory to Mtb was identified via\nroutine occupational health (OH) screening were defined as remotely exposed to TB. (A) Day 2 TST\ninduration (n=27 HC vs. n=86 OH). (B) Expression of cellular proliferation and cell type-specific modules\nin Day 2 and Day 7 TST bulk RNAseq data (D2: n=30 HC vs. n=100 OH; D7: n=27 HC vs. n=67 OH).\n(C-E) Diversity metrics (C), within donor coincidence (D) and between donor coincidence (E) in down-\nsampled Day 7 TST bulk TCRseq beta chain data (n=17 HC vs. n=43 OH, resulting in n=136 pairwise\nHC and n=903 pairwise OC comparisons to calculate between donor coincidence). (F-H) Proxies for\nMtb-reactivity in down-sampled Day 7 TST bulk TCRseq beta chain data (n=17 HC vs. n=43 OH),\nincluding percentage of public Mtb-reactive CDR3 sequences (F) and metaclone TCRs (G) amongst all\nor expanded (clone size >1) TCR sequences, and percentage of expanded TCRs, stratified by clone\nsize (>1, >2, >3, >4) (H). Statistical significance was assessed with Wilcoxon tests.\n3\n)mm(\nnoitarudni\nTST\n2\nyaD\nA\nProlif T_cell CD4_T CD8_T myeloid NK\n0.905 0.34 0.78\n0.27\n0.467\n0.249\n0.179\n0.0636 0.168 0.098 0.118 0.0867\nTST_D2\nTST_D7\n15\n10\n5\n0\n-5\n15\n10\n5\n0\n-5\nerocs\nZ\neludoM\nB\nExpanded Gini Richness Shannon Simpson\n7.5\n0.221\n0.215\n5.0\n0.149\n0.209 0.15 2.5\n0.0\n-2.5\nerocs\nZ\nC\n0.221\n-4.5\n-5.0\n-5.5\necnedicnioc\nronod\nnihtiW\nD\n0.23\n-6\n-7\n-8\necnedicnioc\nronod\nneewteB\nE\nAll Expanded\n0.974\n0.838\n10\n5\ns3RDC\nevitcaer-btM\ncilbup\n%\nF\nAll Expanded\n0.388\n15 0.431\n10\n5\nsRCT\nenolcatem\n%\nG\n>1 >2 >3 >4\n100 0.209 0.154\n0.191\n0.18\n80\n60\n40\nsRCT\ndednapxe\n%\nH\nB. Related, did you measure TST induration in the volunteers before taking the punch\nbiopsy, any relation to the number of TCR sequences retrieved? In individuals with large TST induration, where\nwas the punch taken? In the center or also towards the edges?\nThe reviewer raises the question whether clinically measurable TST induration on day 2 is associated\nwith the extent of T cell reactivity as measured at the sequence level. The number of retrieved TCR\nsequences varies substantially in our dataset (see response to comment 2) and is likely not only\ndetermined by T cell density in the skin biopsy but also by technical factors such as RNA quality. Instead\nof showing correlation between TST induration and absolute number of recovered TCR sequences, we\ntherefore explored the relation between day 2 TST induration and percentage of metaclones or\npercentage of expanded TCRs, as proxies for the number of Mtb-reactive TCRs in the day 7 TST.\nThis shows a statistically significant, positive correlation of D2 TST induration with D7 TST metaclone\nproportion and expanded D7 TST TCRs (clone size >3), suggesting that both are the result of a common\ndeterminant of the TST response. Inflammatory induration recedes by day 7, therefore it is not possible\nto assess the relationship between induration and Mtb-reactive T cell frequencies at this time point.\nWe have included this observation and our interpretation of it in the Results and Discussion sections:\nLines 271-274: “In contrast, at day 7 metaclones were about 10-fold enriched in the TST samples\ncompared to blood and day 2 TST repertoires (Figure 5B, Supplementary Figure 8), and the percentage\nof day 7 TST repertoires captured by metaclones was positively correlated with the TST induration\nmeasured at day 2 (Supplementary Table 1).”\nLines 337-341: “We also observed a positive correlation between TST induration on day 2 and the extent\nof predicted Mtb reactivity on day 7 (measured as proportion of metaclones, or proportion of expanded\nTCRs), consistent with the hypothesis that both arise from a common determinant of the TST response,\nlikely to be the frequency of circulating Mtb-reactive memory T cells that initially drive inflammatory\ninduration and then proliferate in response to antigen.”\nDay 2 TST Day 7 TST Blood\nTranscriptional modules\nModule ‘All T cells’ R=0.32, p<0.001 R=0.32, p<0.001 NA\nModule ‘CD4 T cells’ R=0.31, p<0.001 R=0.4, p<0.001 NA\nModule ‘CD8 T cells’ R=0.47, p<0.001 R=0.22, p<0.001 NA\nModule ‘NK cells’ R=0.15, p=0.043 R=-0.03, p=0.732 NA\nModule ‘Myeloid cells’ R=0.45, p<0.001 R=0.12, p=0.174 NA\nModule ‘Proliferation’ R=-0.21, p=0.005 R=0.064, p=0.458 NA\nTCR metrics\n% metaclones R=0.83, p=0.058 R=0.22, p=0.021 R=0.018, p=0.969\n% TCRs with clone size >1 R=-0.43, p=0.419 R=0.061, p=0.525 R=0.71, p=0.088\n% TCRs with clone size >2 R=-0.14, p=0.803 R=0.18, p=0.064 R=0.61, p=0.167\n% TCRs with clone size >3 R=0.14, p=0.803 R=0.26, p=0.007 R=0.61, p=0.167\n% TCRs with clone size >4 R=0.14, p=0.803 R=0.31, p<0.001 R=0.61, p=0.167\nSupplementary Table 1: Day 2 TST induration is correlated with T cell gene signatures and\nproxies for Mtb reactivity in day 7 TSTs. Spearman correlation analysis of TST induration on day 2\nwith expression of gene signatures in TSTs from day 2 (n=184) or day 7 (n=135), and with percentage\nof β-chain metaclone TCRs or percentage of expanded TCRs (stratified by clone size) in down-sampled\nrepertoires from day 2 TST (n=6), day 7 TST (n=111) and blood (n=7). Significant correlations (p<0.05)\nare highlighted in bold.\nIn addition, we confirm that the punch biopsies were taken in the centre of the TST induration, and we\nhave now included this in the methods (lines 418-419): “The precise injection sites were marked with\npermanent marker pen and used to position the site of the punch biopsies at designated time points.”\n5. Does TST induration correlate with the inflammatory and proliferation transcriptomic profiles as\ndepicted in Figure 1?\nadditional supplementary figure (see also response to comment 4). We reference this in the Results\nsection (lines 111-113): “Clinical induration at TST sites on day 2 was positively correlated with multiple\ncell types at day 2, but only T cell accumulation at day 7, as measured by cell-type specific gene\nexpression of modules (Supplementary Table 1).”\n6. Figure 5A suggest that day 7 TST TCR clones are partially not specific to Mtb, could the authors\nspeculate what is happening in these TST sites? As in Figure 2B they described specific depletion\nof CMV and EBV specific T-cells, which suggests that there is not simply bystander proliferation of\nall local T-cells resulting from increased inflammatory cytokines. Do to they expect recruitment of T-\ncells with other specificities?\nWe have amended the Discussion (lines 320-328) to clarify our interpretation of the data: “Recruitment\nappears to be non-selective, demonstrated by the similar proportions of Mtb-, tetanus toxoid-, CMV- and\nEBV-reactive CDR3 sequences observed in the day 2 TSTs and blood samples. However, Mtb-specific\nsequences are enriched relative to CMV-, EBV- and tetanus toxoid-reactive CDR3 sequences in day 7\nTSTs, suggesting that the proliferation response is not simply due to bystander proliferation of all local\nT cells. Our data cannot fully discriminate the extent to which reduction of non-Mtb-reactive T cells at\nthe TST site by day 7 is due to their clearance or their relative decrease caused by the expansion of\nMtb-reactive TCRs dominating the sampled repertoire. The latter process is supported by our\nobservation that the full set of day 7 TST TCRs shows substantially lower enrichment in independent\ntuberculosis-associated datasets compared to day 7 TST metaclones.”\n7. The heterogeneity of the study population, with potentially large variation in time since Mtb infection\nshould be included as limitation in the discussion\nWe have added the following text to the Discussion (lines 358-363):“Inter-individual variation in total T\ncell reactivity and/or the clonal repertoire, including intra- and inter-donor co-incidence metrics in the\nTST may relate to duration of Mtb infection or time interval since immunological clearance of bacteria.\nWhile neither of these variables can be measured accurately, epidemiological classification of our study\ncohort into household contacts, likely to be enriched for recent exposures, and occupational health\nscreening participants, likely to be enriched for remote exposures, showed no differences in any of the\nmeasured molecular TST metrics.”\nReviewer #2\n8. In this study, the authors analyze gene expression and the TCR repertoire from bulk RNA isolated\nat days 2 and 7 following intradermal PPD placement, with saline serving as a control for gene\nexpression analyses. They report that day 7 responses are enriched for gene modules associated\nwith cell proliferation, pan–T cell, CD4 T cell, and NK cell activation, but not CD8 T cell responses.\nTCR repertoire analysis from the same bulk RNA shows that day 2 responses contain more TCRβ\nclonotypes with ≥2 copies than whol⁺e blood, suggesting recruitment of clonally⁺ expanded memory\nT cells to the PPD site. By day 7, clonal expansion increases further, with reduced diversity reflected\nin Shannon and inverse Simpson indices. Using CDR3 sequences from three published TB studies,\nthe authors define “Mtb-reactive” TCRs and demonstrate their enrichment in day 7, but not day 2,\nskin biopsy samples. Similarly, TCRs expanded in PPD-stimulated autologous PBMCs were\nenriched in day 7, but not day 2, skin biopsies. The absence of enrichment at day 2—the time point\ntypically used clinically to assess the PPD response—is noteworthy and unexpected. The authors\nalso evaluate TCR clustering methods, proposing a new workflow - metaclonotypist, ultimately\nselecting TCRdist3 grouping with a well-rationalized threshold of 15. They observe enrichment of\n“public” metaclones in day 7 PPD biopsies, alongside additional clonotypes lacking clear Mtb\nspecificity, leading to the conclusion that day 7 responses comprise a mix of Mtb-reactive (both\nprivate and public) and non–Mtb-specific TCRs. Finally, the manuscript explores HLA\nassociations and enrichment of Mtb-reactive TCRs using GLIPH2 (patterns present in >4 individuals),\nthough important methodological details regarding benchmarking and comparisons with GLIPH2 lacking.\nOverall, this manuscript provides an innovative analysis of T cell repertoires 2 and 7 days after PPD\nplacement in LTBI individuals, integrating clustering algorithms and antigen-specific annotations.\nNonetheless, several weaknesses remain, as detailed in the comments below.\nWe thank the reviewer for this summary.\n9. It is not clear how clonal frequency was calculated since TCRa and TCRb sequences were reverse-\ntranscribed from bulk RNA isolated from biopsy sites or whole blood. Transcription levels fluctuate\n5\nwith activation state and cell cycle, so repertoire measurements can be biased toward recently\nactivated clones. (e.g., one activated cell with high TCR mRNA might look “expanded”). These\nissues should at least be discussed, and it should be acknowledged that the interpretation of TCR\nrepertoire using bulk RNA (instead of genomic DNA) is semi-quantitative.\nThe reviewer is correct that the UMI-based method quantifies the number of mRNA transcripts, not the\nnumber of T cells. However, unlike B cells, T cells do not substantially change mRNA levels upon\nactivation (see Oakes et al. Quantitative Characterization of the T Cell Receptor Repertoire of Naïve\nand Memory Subsets Using an Integrated Experimental and Computational Pipeline Which Is Robust,\nEconomical, and Versatile. Front Immunol. 2017 Oct 12;8:1267. doi: 10.3389/fimmu.2017.01267. PMID:\n29075258; PMCID: PMC5643411. and references therein). In fact, there is a small downregulation of\nTCR mRNA upon activation. But the changes are in the order of 2-fold maximum, which is within the\nerror range of the PCR assay itself. We therefore are confident that the assay gives a good estimate of\nclonal expansion.\nWe have edited the text to clarify this point (lines 473-476): “Since the UMI-based method quantifies the\nnumber of mRNA transcripts, we note that ‘clone size’ does not measure the number of T cells directly.\nNevertheless, mRNA number is a good proxy for clone size and clonal expansion since T cells do not\nsubstantially change TCR mRNA levels upon activation26.”\n10. How do the authors explain that “Mtb-reactive” TCRs are only enriched in d7 but not d2 samples,\nwhen they also argue that d2 skin bx samples likely reflect recruitment but not yet proliferation of\nMtb-specific T cells? The number of clonotypes present in >1 copies is increased at d2, indicating\nmemory T cells are responding to PPD. Even if they are not significantly expanded, one would\nexpect to observe Mtb-reactive TCR clonotypes at d2. Could this be an issue with the sensitivity of\nthe technique or approach of using bulk RNA from biopsy samples for the purpose of TCR\nsequencing, which is overcome at the d7 time point by significantly greater clonal expansion of the\nMtb-specific T cells? Or that a comparison with the PBMC TCR repertoire is not a fair comparison\nwith skin biopsies using bulk RNA? Otherwise it would appear that the authors are claiming the\nentire PPD response at day 2 is likely not Mtb specific which seems unlikely since PPD responses are\nusually negative (ie no induration) in people without LTBI. This issue, and the limitations of the\ntechnique should at least be discussed and clarified since it is not consistent with the Discussion\npoint made in lines 294-295.\nThe reviewer raises an important point which we already discuss (lines 330-337) including reference to\nthe limit of sensitivity to detect Mtb-reactive sequences by enrichment compared to blood. Since there\nis no detectable proliferation at day 2, T cell accumulation in the TST at this time point must represent\nrecruitment from the circulation (lines 318-319), therefore inflammatory reactivity at day 2 most likely\nreflects frequency of circulating Mtb-reactive T cells, irrespective of the fact that they may not be\nenriched in the recruited T cell repertoire relative to the blood of individuals with prior exposure and\nmemory.\nWe have now clarified this both in the Results (line 143-145):\n“This indicates that the early inflammatory response likely reflects the circulating frequency of Mtb-\nspecific T cells in individuals with prior memory, rather than preferential recruitment of specific clones.\"\nAnd in the Discussion (lines 337-341): “We also observed a positive correlation between TST induration\non day 2 and the extent of predicted Mtb reactivity on day 7 (measured as proportion of metaclones, or\nproportion of expanded TCRs), consistent with the hypothesis that both arise from a common\ndeterminant of the TST response, likely to be the frequency of circulating Mtb-reactive memory T cells\nthat initially drive inflammatory induration and then proliferate in response to antigen.”\n11. The authors also compare the metaclonotypist TCR grouping approach to GLIPH2, including the\nability to associate HLA restriction (lines 226-229). However, as previously published (e.g. Musvosvi\net al., Nat Med, 2023), GLIPH2 generates large numbers of motifs / metaclones some of which are\nultimately removed by statistical thresholds in Fisher Exact Test, CDR3 Length Score, Vb usage\nscore, HLA association score, etc). There is no description of which tests (and their p value\nthresholds) were used to determine the final list of GLIPH2 patterns (and their TCRs) which were\ncompared with the metaclones. Therefore, the comparison could be invalid. The comparison should\nalso be depicted in one of the main figures since validation of the metaclonotypist approach is a\nmain element of the manuscript yet is not demonstrated in any figures- only an Extended Datasheet\nis referenced.\nWe agree with the reviewer that it is difficult to decide at what stage of a TCR grouping pipeline\ncomparisons should be made. This consideration motivated us to focus the main text figures on rigorous\n6\nbenchmarking of clustering algorithms using TCRs with known specificity, to identify Pareto optimal\napproaches (Fig. 4B), and on demonstrating false discovery control through HLA shuffling (Fig. 4C). We\nbelieve these analyses represent a key advance over prior studies comparing clustering tools‐, which\noften yielded inconclusive results as they did not isolate performance differences related to distinct\nstages of the computational pipelines.\nAs the reviewer correctly points out, the GLIPH2 output can be filtered by different criteria, thus\npotentially increasing specificity. In our primary analysis, we mirrored the Metaclonotypist approach and\nfiltered raw GLIPH2 clusters by publicity (containing TCRs from at least 4 different individuals) and HLA\nassociation (Fisher’s Exact Test with FDR<0.1), as already described in lines 583-586. This choice of\nfiltering strategy ensured a controlled comparison of algorithmic approaches at equivalent levels of\nclustering specificity (Figure 5A). In a new main text figure panel (Figure 4F), we now visualise the\noverlap of clustered unique CDR3 amino acid sequences between Metaclonotypist and GLIPH2, to\nhighlight the increased sensitivity of our approach.\nIn response to the reviewers question, the revised manuscript furthermore reports additional analyses\n(in new Supplementary Figure 10), which demonstrate the effect of applying filters to GLIPH2 output as\ndescribed by Musvosvi et al., either alone or in combination with the HLA association test at p<0.05 (as\ndescribed by Musvosvi et al.) or at FDR<0.1 (as used in the Metaclonotypist pipeline). These analyses\ndemonstrate that specificity (enrichment of metaclones in independent data associated with TB) is\nmarkedly improved by the Metaclonotypist approach of controlling HLA associations for multiple testing,\nirrespective of additional filtering, such as for CDR3 length or Vb usage score.\nWe have amended the manuscript as follows:\nResults, lines 234-242:\n“After exploring different GLIPH2 filters (Supplementary Figure 10A), we retained clusters which passed\nthe same stringent HLA association test with multiple testing correction applied in the Metaclonotypist\npipeline. These GLIPH2 clusters (Supplementary File S7-8) also primarily associated with class II\nalleles, which demonstrates the robustness of our findings with respect to the algorithmic approach.\nHowever, Metaclonotypist clustered almost three times as many unique CDR3s compared to GLIPH2,\nsuggesting that Metaclonotypist offers increased sensitivity to detect metaclone associations (Figure\n4F). Indeed, comparison of CDR3s clustered by Metaclonotypist or GLIPH2 with VDJdb class II-\nrestricted β-chain CDR3 entries annotated as Mtb-reactive found three matches in the GLIPH2 output,\nbut an additional match in the Metaclonotypist output (Figure 4F, Supplementary Figure 9A).”\nResults, lines 257-260:\n“Metaclonotypist motifs showed comparable enrichment to CDR3β sequences from GLIPH2 clusters\nwith statistically significant class II HLA-allele associations (Figure 5A; blue vs. red) suggesting that\nMetaclonotypist retains similarly high specificity, despite clustering a larger proportion of TCRs into\nmetaclones (Figure 4F). Additional GLIPH2 filters did not improve performance (Supplementary Figure\n10B).”\nMethods, lines 583-590:\n“GLIPH2 analysis was undertaken on the same set of Day 7 TST -chain repertoires as described for\nmetaclonotype discovery above, using default settings and CD48_v2.0 reference. The Metaclonotypist\napproach was then mirrored, by selecting only GLIPH2 similarity clusters containing TCRs from ≥4\nindividuals and testing for associations with HLAs found in ≥4 individuals (done separately for class I\nand class II alleles). We explored the effect of filtering the GLIPH2 output further, as described before\n15. Filter 1 selected clusters that consisted of ≥3 unique CDR3s and had a Fisher_score, vb_score and\nlength_score ≤0.05 each. Filter 2 applied the Fisher’s exact test for HLA association, either with a\nsignificance threshold of p<0.05 (as used by Musvosvi et al15) or with an FDR<0.1 as applied in the\nMetaclonotypist pipeline.”\nGLIPH2\n154\n319\n1069\n0\n1\n26VDJdb\n(class 2 Mtb)\nFigure 4F. Venn diagram showing the overlap of unique β chain CDR3 amino acid sequences included\nin class II-associated metaclone clusters by Metaclonotypist or GLIPH2 and annotated as class 2-\nrestricted Mtb-reactive TCRs in VDJdb.\nA\nNumber clusters Number unique CDR3s\nGliph2 + Filter 1 1837 6546\nGliph2 + Filter 1 + Filter 2 (p<0.05) 1342 5022\nGliph2 + Filter 1 + Filter 2 (FDR <0.1) 106 476\nGliph2 + Filter 2 (FDR <0.1) 128 476\n2.0\n1.5\n1.0\n0.5\n0.0\nDataset\nSupplementary Figure 10: Effect of filtering on sensitivity and specificity of Gliph2 similarity\nclusters. Gliph2 similarity clusters were derived from expanded (TCR count >1) day 7 TST β-chain\nrepertoires (122,253 TCR clones, 151 individuals). Gliph2 clusters containing CDR3s from ≥4\nparticipants were selected for analysis. Filter 1 retained clusters with ≥3 unique CDR3s, which had a\nFisher_score, vb_score and length_score ≤0.05 each. Filter 2 retained clusters with a Fisher’s exact\ntest for HLA class II association of p<0.05 or FDR<0.1. A. Number of Gliph2 clusters and number of\nunique day 7 TST CDR3s selected in each analysis iteration. B. Relative enrichment of CDR3s clustered\nby Gliph2 and filtered by different criteria in multiple external data sets, showing point estimates and\n95% confidence intervals of odds ratios in the pairwise comparisons indicated. See Figure 5A in the\nmain manuscript for a detailed description of the datasets. Results for ‘Gliph2 + Filter 2 (FDR<0.1)’ are\nreplicated in Figure 5A in the main manuscript as ‘Gliph2-matching CDR3s’.\n12. There are many statements about data made in lines 242-252 without any figure call-outs or\nreferences. Could the authors support these statements with figures since they don’t all seem to be\nrelated to Figure 5A.\nAll the text in this section does in fact relate to Figure 5A. We have included specific references to Figure\n5A to guide readers more clearly through this section (lines 255-266):\n“We benchmarked this analysis for publicity and Mtb-reactivity of metaclones identified using\nMetaclonotypist against metaclones identified by the GLIPH2 algorithm. Metaclonotypist motifs showed\n8\noitaR\nsddO\n01goL\nB TCR set\nGliph2 + Filter 1\nGliph2 + Filter 1 + Filter 2 (p<0.05)\nGliph2 + Filter 1 + Filter 2 (FDR<0.1)\nGliph2 + Filter 2 (FDR<0.1)\nSequencing method\nBulk\nSingleCell\ncomparable enrichment to CDR3β sequences from GLIPH2 clusters with statistically significant class II\nHLA-allele associations (Figure 5A; blue vs. red) suggesting that Metaclonotypist retains similarly high\nspecificity, despite clustering a larger proportion of TCRs into metaclones (Figure 4F). Additional\nGLIPH2 filters did not improve performance (Supplementary Figure 10). Importantly, amino acid\nmetaclone motifs as defined by GLIPH2 were not substantially enriched in TB samples in these datasets\n(Figure 5A; light-brown), indicating the need for more restrictive motif definitions. Interestingly, compared\nto metaclones, we found substantially lower enrichment of the full discovery set of expanded day 7 TST\nβ TCRs (Figure 5A; grey vs. blue), suggesting that a substantial proportion of day 7 TST TCR clones\nmay not be specific to Mtb, and that identification of metaclones significantly improves antigen-agnostic\nenrichment of the Mtb-reactive T cell response.”\n13. The HLA association for GLIPH2 does not always find just a single association, but often 2. This is\nespecially true for HLA alleles in linkage disequilibrium (ie. HLA DRB1*07:01 and DRB4*01:03)\nsince it is usually impossible to distinguish which DR allele is the restricting MHC for such alleles.\nHowever, only a single association is listed for these metaclones listed. Could the authors re-\nexamine the HLA associations to comprehensively report the HLA associations or elaborate on how\nsingle HLA associations were made for all TCRs in this dataset?\nWe thank the reviewer for prompting us to clarify this. Where more than one significant HLA association\nis found, we had chosen to report the most significant association, as it represents a likely candidate for\nthe causal association when linkage disequilibrium is not total. For greater clarity, we now refer to these\nas ‘lead HLA association’ and have amended Supplementary Files 5-8 to include all associations that\npassed our statistical testing. For metaclones with more than one significant association, the most\ncommon combinations are DQA1*05_DQB1*03 and DRB1*11; DQA1*02_DQB1*02 and DRB1*07; and\nDQA1*05_DQB1*02 and DRB1*03. All represent HLA allele combinations in linkage disequilibrium\n(Gragert et al., Human Immunology, 2013; https://doi.org/10.1016/j.humimm.2013.06.025).\nWe have amended the discussion (lines 381-387) as follows, to acknowledge this limitation:\n“Consistent with most data on HLA class II restriction of CD4 T cell responses, we found the lead HLA\nassociations for day 7 TST metaclones to be predominantly HLA-DR alleles. This skew is attributed to\nthe higher prevalence of diverse DR alleles, a structure that allows them to bind a wider variety of\npeptides, and potentially higher levels of expression than HLA-DP and HLA-DQ alleles on antigen\npresenting cells35–37. Several metaclones were significantly associated with more than one HLA allele,\nlikely due to the co-expression of HLA alleles in linkage disequilibrium 38, limiting the ability to resolve\nwhich allele is the restricting MHC for such metaclones.”\n14. For the large amount of data analyzed, the main figures are limited to summary statistics and group\nfrequency comparisons. This seems like a missed opportunity. There should at least be a\npresentation of the most meaningful Mtb-specific, HLA-restricted metaclones in the main figures\nand the antigen specificities for any that have been already defined in the literature\n(using VDJDB or IEDB).\nWe have included this additional analysis as requested by the reviewer. We compared 1392 CDR3\nsequences included in the 177 class 2-restricted metaclones against 30 CDR3 beta sequences,\nannotated in VDJdb with MHC class \"MHCII\", and antigen species \"M.tuberculosis\" or \"Mtb\".\nThis resulted in 4 matching CDR3s from 3 different metaclone clusters. We list those matches in\nSupplementary Figure 9A.\nIn Figure 4, we already visualise the adjacency graphs and sequence logos for the most public and most\nabundant metaclones in the Day 7 TST (index 8 and 21). In addition, we now include in Supplementary\nFigure 9B the adjacency graphs and sequence logos for the most public and most abundant metaclones\nfound in the set of Mtb-reactive T cells by Musvosvi et al. (index 156 and 100), and for metaclones with\nMtb CDR3 matches in VDJdb (index 100, 47 and 12).\nFurthermore, we now include in Table 3 the metaclone descriptions (including consensus CDR3 motif,\nV gene usage, and lead HLA association with its pvalue and odds ratio) for the 10 most public\nmetaclones in our down-sampled Day 7 TST dataset, which together identify Mtb-T cell reactivity in the\nentire study population as demonstrated in Figure 5D.\nWe reference these additional display items in Results lines 228-230, 238-242, and 286-289:\n“The identified single chain metaclones can be represented as sequence motifs and visualised as\nadjacency graphs in which labelling of the individual TCR sequence by the donor reveals substantial\npublicity (Figure 4D, Supplementary Figure 9B).”\n9\n“However, Metaclonotypist clustered almost three times as many unique CDR3s compared to GLIPH2,\nsuggesting that Metaclonotypist offers increased sensitivity to detect metaclone associations (Figure\n4F). Indeed, comparison of CDR3s clustered by Metaclonotypist or GLIPH2 with VDJdb class II-\nrestricted β-chain CDR3 entries annotated as Mtb-reactive found three matches in the GLIPH2 output,\nbut an additional match in the Metaclonotypist output (Figure 4F, Supplementary Figure 9A).”\n“Despite the dominance of private PPD-reactive TCRs at individual level, evaluation of the cumulative\npublicity of metaclones ranked by their publicity suggests that as few as 10 metaclones are sufficient to\nidentify Mtb-T cell reactivity in the entire study population (Figure 5D, Table 3, Supplementary Figure\n14).”\nA\nMetaclonedescription Matching CDR3 VDJdbentry\nIndex 100 CSARGQGNEQYF V: TRBV20-1*01\nConsensus CDR3: CSARGQGNEQFF Species: M.tuberculosis\nV: TRBV20-1 Protein: PPE33\nEpitope: PPQIAANRSQLISLV\nIndex 47 CSARASGGEAKNIQYF, V: TRBV20-1*01\nConsensus CDR3: CSARASGGEAKNIQYF CSARAGGGEAKNIQYF Species: M.tuberculosis\nV: TRBV20-1 Protein: Rv3874\nEpitope: AAVVRFQEAANKQKQ\nIndex 12 CASSLIENTEAFF V: TRBV12-3*01\nConsensus CDR3: CASSLIENTEAFF Species: M.tuberculosis\nV: TRBV12-4 Protein: Rv3804c\nEpitope: PSPSMGRDIKVQFQS\nB\nMetaclone 100: Metaclone 156:\nMost abundant in Mtb-reactive T cells from Musvosvi et al. 2023 Most public in Mtb-reactive T cells from Musvosvi et al. 2023\n1 member CDR3 annotated as reactive to Mtb PPE33 in VDJdb\nMetaclone 47: Metaclone 12:\n2 member CDR3s annotated as reactive to Mtb Rv3874 in VDJdb 1 member CDR3 annotated as reactive to Mtb Rv3804c in VDJdb\nSupplementary Figure 9: Published Mtb reactivity of class II-restricted metaclones.\nA. CDR3 sequences included in class II-restricted metaclones (n=1392) were compared against CDR3\nβ sequences annotated in VDJdb with MHC class \"MHCI\", and antigen species \"M.tuberculosis\" or \"Mtb\"\n(n=30). The middle column shows the matching CDR3; the left-hand column shows the description of\nthe metaclone that the CDR3 is a member of; the right-hand column shows VDJdb annotations for the\nCDR3. B. Adjacency graphs and sequence logos for metaclones with Mtb CDR3 matches in VDJdb,\nand for metaclones that are most abundant (matching 47 out of 21,212 cells with β-chain TCR data) or\nmost public (found in 14 out of 70 participants) in single cell TCRseq data from Mtb-reactive T cells\n(Musvosvi et al., 2023). Each node in the adjacency graph represents a single TCR stratified by colour\ninto distinct donors from the Day 7 TST discovery dataset.\nIndex Publicity Consensus CDR3aa V gene usage Lead HLA\nassociation\n(odds ratio, p-value)\n8 82/128 CSARVGGNTGELFF TRBV20-1 DRB1*15\n(10.2, 1.84E-09)\n76 78/128 CSAGGLAGNEQFF TRBV20-1 DQA1*01_DQB1*05\n(8.6, 9.30E-06)\n13 69/128 CASSLGSVSYEQYF TRBV7-9 DRB1*15\n(11.3, 3.55E-09)\n21 64/128 CSARDLGLAEETQYF TRBV20-1 DRB1*04\n(11.3, 4.41E-08)\n39 60/128 CSVGETQYF TRBV29-1 DQA1*05_DQB1*03\n(9.1, 4.63E-07)\n33 56/128 CSARAGYGYTF TRBV20-1 DRB1*10\n(44.0, 2.61E-07)\n91 50/128 CASSLEGETQYF TRBV7-9 DRB1*11\n(6.2, 2.17E-05)\n6 49/128 CASSRGAQTYEQYF TRBV18 DPB1*04\n(27.5, 8.91E-10)\n100 47/128 CSARGQGNEQFF TRBV20-1 DRB1*13\n(7.9, 2.73E-05)\n97 43/128 CASSPGRETQYF TRBV6-6|TRBV6-5|TRBV6-9 DRB1*10\n(26.8, 2.39E-05)\n15. Several publications have reported responses by donor-unrestricted T (DURT) cells to Mtb. The\ndataset generated in this paper allows analysis of whether any of the responding T cells in the Mtb-\nspecific subset (infected vs peptide-pulsed APCs) include TCR-alpha sequences that match those\nof canonical MR1-restricted T cells, or CD1B, CD1c and CD1d-restricted T cells. I would expect low\nenrichment at both the d2 and d7 time points (compared to PBMCs) since intradermal PPD was\nused.\nAs per the reviewer’s suggestion, we include an additional supplementary figure (Supplementary Figure\n5), showing percentage of TCR-alpha sequences in blood and TST that match the gene usage described\nfor MAIT, GEM and iNKT cells. There is no enrichment of these TCR sequences in the TST compared\nto blood, neither on day 2 nor on day 7. Instead, MAIT-associated TCR-alpha sequences are decreased\nin the day 2 TST compared to blood and further decreased in the day 7 TST, and iNKT-associated TCR-\nalpha sequences are also depleted in the day 7 TST compared to blood.\nWe have amended the text to reflect this additional analysis.\nMethods lines 489-491: “MAIT TCR enrichment was assessed based on their canonical TCR α gene\nusage as sequences containing TRAV1-2, paired with TRAJ12, TRAJ20 or TRAJ33; iNKT TCRs were\nidentified as TCRs containing TRAV10 paired with TRAJ18; and GEM TCRs were identified as TCRs\ncontaining TRAV1-2 paired with TRAJ947.\"\nResults lines 150-153: “Day 7 TSTs showed a statistically significant reduction in the relative frequencies\nof both CMV and EBV-reactive CDR3 sequences compared to blood and day 2 TSTs, consistent with\nlarger clonal expansions of Mtb-reactive sequences. These clonal expansions were not explained by\ndonor-unrestricted T cell responses to Mtb, since day 7 TSTs showed a significant reduction in the\nrelative frequencies of TCR α sequences that match the gene usage of MAIT or iNKT cells, compared\nto day 2 TSTs and/or blood (Supplementary Figure 5).”\n11\ndown-sampled full repertoires\n** **\n**** ****\n* *\nns ns\n**** ****\nns ns\nns ns\nns ns\nns ns\nSupplementary Figure 5: TCR alpha sequences associated with donor-unrestricted T cells are\nnot enriched in the TST. Percentage of alpha chain sequences associated with mucosal-associated\ninvariant T (MAIT), invariant natural killer T (iNKT) and germline-encoded mycolyl lipid-reactive (GEM)\nT cells in blood, day 2 and day 7 TST TCR repertoires. Abundance was calculated in full repertoires\n(n=20 Blood, n=17 Day 2 TST, n=165 Day 7 TST) and after individual alpha chain bulk TCR repertoires\nwere down-sampled to 16,000 total TCRs (n=20 Blood, n=16 Day 2 TST, n=119 Day 7 TST). Data are\nshown as boxplots depicting median and interquartile range (IQR), while outlier data points (more than\n1.5*IQR beyond the box hinges) are shown as dots. Statistical significance was assessed with Wilcoxon\ntests and corrected for multiple testing (ns FDR>0.05, * FDR<0.05, ** FDR<0.01, **** FDR<0.0001).\n16. In line 258, the context of the \"metaclones\" is unclear. Are the authors referring to Mtb-specific\nTCRbs?\nWe have amended the term “metaclones” in this sentence to “putative Mtb-reactive metaclones” (line\n274). We believe it is already clear that our metaclones are defined by analysis of bulk TCR beta-chain\ndata in day 7 TSTs, but are happy to make additional amendments if there are perceived to be further\nambiguities.\n17. The authors frequently refer to public and private metaclones (especially lines 253-275), public T\ncell responses, and TCRs throughout the manuscript without defining this terminology. Perhaps it\nsimply needs to be made consistent throughout the manuscript and clearly defined in the Results\nSection. Grouping algorithms are used to group what would be considered “private” TCRs (unique\nto individuals) that contain the same central motifs as other private TCRs, and therefore usually\nrecognize the same antigens in the context of the same MHC molecules. However, the TCRs,\nthemselves, are not necessarily public.\nWe have reviewed the manuscript carefully. There are no instances of “private metacones”. We have\namended the Results text to make the distinction clearer between PPD-reactive CDR3 sequences that\nwere defined in ex vivo stimulation experiments of paired PBMC (and therefore considered ‘private’),\nand Mtb-reactive metaclones that were defined by clustering similar TCR sequences from day 7 TSTs\nacross donors (and therefore considered ‘public’).\n18. It is not clear what is meant by the statement in lines 246-247. If broader metaclones were not\nenriched, then why would it be more likely that the more specific / less broad metaclones would be?\nThe reviewer raises an important question for clarification. Enrichment in this analysis is a function of\nspecificity. We have amended the text to make this point explicitly.\niNKT\nGEM\n10.0\n7.5\n5.0\n2.5\n0.0\n4\n3\n2\n1\n0\n3\n2\n1\n0\nsRCT\nlla\nfo\n%\nLines 245-248: “To confirm that our day 7 TST derived, class II associated T cell metaclones\nrepresented public Mtb-reactive T cell responses, we calculated their enrichment in independent TCR\nsequencing data derived from people with TB compared to other diseases, or at the site of TB disease\ncompared to blood. Enrichment in this analysis suggests specificity for Mtb-reactive TCRs.”\nIn the paragraph referenced by the reviewer, we first observe that Metaclonotypist-clustered TCRs are\nenriched to similar extent compared to GLIPH2-clustered CDR3s despite including a larger of proportion\nof day 7 TST TCRs. Since not all TCRs present in the day 7 TSTs are Mtb-reactive, the increased\nsensitivity of Metaclonotypist could have resulted in also clustering non-PPD-reactive TCRs. However,\nthe comparable enrichment in independent TB-associated datasets suggests that this is not the case.\nSecondly, we observe that the short CDR3 amino acid motifs used by GLIPH2 to describe its clusters\nare not substantially enriched in TB-associated TCR sequencing datasets, indicating a lack of specificity.\nThese short motifs capture a common sequence pattern of the included CDR3s. However, while the\nincluded CDR3s themselves are enriched in TB-associated datasets, the short motifs alone are not\nenriched and thus cannot be considered specific to Mtb-reactive TCRs.\n19. In lines 270-272 the authors state, “Despite the dominance of private TCRs at individual level, evaluation of the cumulative\npublicity of metaclones ranked by their publicity suggests that as few as 10 metaclones are sufficient to\nidentify Mtb-T cell reactivity in the entire study population”. It is not clear to me what this is intended to convey. All individuals would be\nexpected to have mostly private and some public TCRs in any immune response. Do the authors suggest a threshold for the\nnumber of metaclones (e.g. 10) present in a PPD response that would determine whether that\nresponse was Mtb-specific? Is there reason to think any of the responses do not contain at least\nsome Mtb-specific TCRs despite the fact that all participants were reportedly IGRA+?\nWe thank the reviewer for requesting this additional clarification. The important point is that metaclones\npotentially resolve T cell reactivity at the level of specific peptide-MHC complexes. Therefore, despite\nthe huge repertoire of potential peptide sequences that could be derived from Mtb, as few as 10 specific\npeptides are sufficient to identify an Mtb-reactive response in the whole study population. This\ninterpretation was elaborated further in our Discussion (lines 312-317), along with the potential\nimplications of this finding to investigate T cell correlates of clinical outcome and inform next generation\nvaccine design based on specific protective epitopes in place of protein antigens (lines 392-396).\nReviewer #3 (Remarks to the Author)\n20. In this manuscript Turner et al. take a unique approach to determine T cell recruitment and clonality\ninto a temporal antigen challenge site. Analysis at two timepoints (day 2 and 7) following antigen\nexposure provides beneficial insights into the timing and potential kinetics of T cell recruitment.\nTurner et al. found initial recruitment during the peak inflammatory\nresponse was predominantly non-antigen specific, as determined by no enrichment for Mtb-specific CDR3s compared to\nblood. These T cells were then replaced by day 7 with predominantly expanded Mtb-reactive T cells. Turner\net al. developed a modular computational pipeline to identify public alpha or beta TCR sequences, establishing of a catalogue of public Mtb-\nspecific HLA-restricted T cell sequences. While the majority of Mtb-reactive T cells were private, computational analysis\nrevealed 10 metaclones (single chain only), suggesting population-level immunodominance of Mtb-specific responses.\nWe thank the reviewer for this summary.\n21. All analysis seems to be performed in samples from healthy individuals with evidence of peripheral\nblood Mtb-reactive T cells. Do the authors believe the preexisting Mtb-reactive T cells in the\nperipheral blood were naïve T cells or the result of prior exposure?\nIt is well established that PPD responses in peripheral blood are dependent on memory T cells. This is\nthe basis of diagnostic use of interferon gamma release assays.\nOur data do not allow us to directly distinguish between TCRs from naïve and memory T cells. In figure\npanels 3C-D, we find that the set of private PPD-reactive CDR3s is present in significantly greater\nproportion amongst blood TCR sequences with a count >1, which are enriched for memory T cells\n(median 7.2% for count >1 vs. 4.2 % for count >0 of all CDR3s, and median 6.8% for count >1 vs. 2.7%\nfor count >0 of unique CDR3s). This suggests that the pre-existing Mtb-reactive T cells were\npredominantly expanded and thus the result of prior exposure. However, we cannot exclude that naïve\nMtb-reactive T cells may also contribute to the set of in vitro defined PPD-reactive T cells to a small\nextent.\nWe have added this observation to the Results description (lines 180-183):\n13\n“Both in blood and the day 2 TST, ex vivo PPD-expanded CDR3s were present in significantly greater\nproportion amongst TCR sequences with a count >1 (Figure 3C-D, Supplementary Figure 7C-H),\nsuggesting that the PPD-reactive CDR3s were predominantly expanded, as would be expected for\nmemory T cells26.”\n22. While examining T cell responses at the site of antigen challenge over peripheral blood is\ncommendable, it is worth noting that this is not the site of infection in TB. The authors should\nacknowledge that recruitment of T cell into the skin during a controlled antigenic challenge may be\nremarkably different to recruitment of T cells into sites like the lung during infection.\nThe reviewer raises an important potential limitation. Hence it was particularly important for us to confirm\nenrichment of Mtb-reactive metaclones derived from the TST in TCR sequencing data from the site of\nTB lung disease compared to lung cancer, and compared to the general circulating repertoire (Figure\n5A) in patients with TB.\nWe have amended the Discussion as follows (lines 306-311):\n“T cell metaclones derived from the day 7 TST reveal public T cell responses that are highly enriched\nin multiple sources of TCR sequence data from the blood and lung tissue of patients with TB compared\nto other diseases, and at the site of pulmonary TB compared to blood. The enrichment of TST\nmetaclones in the TB lung suggests that T cells recruited into PPD-challenged skin have clinical\nrelevance and supports the use of the TST as experimental human challenge model for immune\nresponses in TB.”\n23. Figure 3B, does each column across the x-axis represent one participant?\nThe reviewer is correct in their interpretation, and we have amended the figure legend for clarification:\n“(B) Heatmap of unique ex vivo PPD-reactive -chain CDR3s (clustered by Ward D2 linkage). Each\ncolumn across the x-axis represents one participant.”\n24. Which figure shows the lower enrichment of the full set of day 7 TST TCR clones compared to the\nmetaclones (page 8, line 249)?\nThis is shown in Figure 5A, comparing grey datapoints (Discovery TST CDR3s) to blue datapoints\n(Metaclonotypist regex). We have amended the Results description for clarification (lines 262-266):\n“Interestingly, compared to metaclones, we found substantially lower enrichment of the full discovery\nset of expanded day 7 TST β TCRs (Figure 5A; grey vs. blue), suggesting that a substantial proportion\nof day 7 TST TCR clones may not be specific to Mtb, and that identification of metaclones significantly\nimproves antigen-agnostic enrichment of the Mtb-reactive T cell response.”\n25. One limitation of this bulk sequencing approach, which the authors acknowledge, is the lack of\npaired alpha/beta-chain data, with the public metaclones identified being restricted to a beta-chain\nonly. I recommended the authors are careful in the language about generalizing immunodominance\nto specific peptides (line 292) as the metaclones identified are only beta-chains and therefore\ninsufficient to pursue reverse epitope discovery.\nWe agree with the reviewer and discuss this limitation explicitly in lines 388-396.\nWe have amended the discussion (lines 312-317) to reflect the reviewer’s concern:\n“Nonetheless, the cumulative publicity of the most public metaclones indicates striking population-level\ncoverage of Mtb reactivity. Although metaclones are defined by their β-chain motifs only, we hypothesize\nthat this reflects immunodominance of specific Mtb epitopes. This finding extends previous studies that\nreport generalisable antigenic immunodominance at the protein level12–14 by providing a scalable\nantigen-agnostic approach to potentially identifying generalisable immunodominance at the level of\nspecific peptides.”\n26. The work appears to be novel and supports the conclusions of the authors, providing interesting\ndata regarding the timing, clonality and publicness of T cell recruitment into an antigenic site.\nHowever, without paired alpha/beta TCR data, the applicably of these findings to disease\nstratification and epitope discovery appears to be limited.\nWe thank the reviewer for highlighting the novelty and interest of our results. As regards the\nidentification of paired α/β TCRs for these metaclones, we agree that this represents an important next\nstep, which we also discuss in lines 390–396.\nour preliminary results demonstrate the feasibility of linking metaclones to paired alpha/beta TCRs.\nFor 87 of the 177 class II restricted metaclones, we identified CDR3 beta motif matches in single cell\nTB lung data (~10k unique clones) or the single cell data reported by Musvosvi et al. (~17k unique\nclones). Therefore, even at the current scale of data, many metaclones can readily be extended t‐o\npai‐red alpha/beta TCRs, thus enabling reverse epitope discovery. In ongoing work, we are generating\nlarger single cell datasets to further increase recovery of paired α/β TCRs for our metaclones, which\nwill be the subject of a future publication.\n‐\nTaken together, these findings suggest that the lack of paired chain information, while a current limitation,\ndoes not substantially impact the applicability of our findings.\nWe confirm that our manuscript meets all of the stipulations detailed above.\nAs requested, we have provided point by point responses to the reviewers’ comments and indicated the\nchanges we have made below.\nReviewer #1\n1. Thanks to the authors for replying to my comments so extensively, including additional analysis. I\nthink these extra data have improved the manuscript and clarified my initial concerns on TCR\nnumbers.\nWe thank the reviewer for their supportive comments.\nReviewer #2\n2. All of my comments have been addressed.\nWe thank the reviewer for their supportive comments.\nReviewer #3\n3. Thank you for the revised manuscript and detailed response letter. While the majority of my\ncomments have been addressed and the manuscript provides new insights into TCR recruitment to\na temporal antigen challenge site, without paired TCR data or experimental validation of HLA\nrestriction/public clones, the applicably of these findings as a resource for biomarker discovery and\nreverse epitope discovery is limited and the work likely does not significantly advance the field.\nWe thank the reviewer for their supportive comments. We acknowledge the reviewer’s comments about\nthe limitations of the chain analyses used in the present manuscript, which we have discussed explicitly\n(lines 390-396) and is the focus of future work to collate paired TCR data for TST metaclones to\nexperimentally validate their HLA restriction and antigen reactivity.\n4. Outside of this, my only other suggestion is to include the additional details about the participant\ncohort in the results section of the paper (line 90). The authors response to Reviewer #1, that they\nare healthy volunteers with known exposures, was a helpful qualification and I believe the\nmanuscript will be strengthen with more upfront inclusion of this information.\nAs suggested by the reviewer, we have elaborated on the context in which immunological memory to\nMtb was identified in study participants, arising from occupational health, contact tracing and new\nmigrant screening programmes (line 91-92)."}]

Decision letter