T cell receptors (TCRs) are formed by stochastic gene rearrangements, theoretically generating 19 >>10 19 sequences 1 . They are selected during thymopoiesis, which releases a repertoire of 20 about 10 8 unique TCRs 2,3 per individual. How evolution shaped a process that produces TCRs 21 that would effectively respond to infectious agents is a central question of immunology. The 22 paradigm is that a diverse enough repertoire of TCRs should always provide a proper, though 23 rare, specificity for any given need. Expansion of such rare T cells would provide enough fighters 24 for an efficacious immune response and enough antigen-experienced cells for memory 3,4 . We 25
show here that thymopoiesis releases a large population of CD8 + T cells harbouring diverse 26 a/bTCRs with innate-like properties. These TCRs (i) have high generation probabilities and a 27 preferential usage of some V and J genes, (ii) are shared between individuals, (iii) are highly 28 enriched for viral antigen recognition and (iv) have a fuzzy rather than tight specificity. In vitro, 29 T cells expressing these TCRs bind to and are activated by multiple unrelated viral peptides; in 30 vivo, they respond to vaccination and infection, being notably found in bronchoalveolar lavages 31 of COVID-19 infected patients. Our results support an evolutionary selection of pleiospecific 32 a/bTCRs for broad antiviral responses and heterologous immunity. 33 thymocytes bCDR3s across individuals (Supplementary Figure 3) , we investigated their private 67 or public nature (Fig. 1g, Supplementary table 1 ). We found a significant increase of public (i.e. 68 shared between at least 2 individuals) bCDR3s in CD8 + versus DPCD3 + thymocytes, which is 69 mostly that of the clustered bCDR3s. For CD8 + thymocytes, up to 31.7% of clustered bCDR3s 70 are public compared to barely 1% of the dispersed ones (Fig. 1g, Supplementary Figure 6) . 
Representation of βCDR3 LD≤1 networks from DPCD3 -, DPCD3 + and CD8 + thymocytes for one 85 representative donor. Each node represents a single βCDR3. b. Node degree (number of connections) 86 of clustered βCDR3 for DPCD3 + and CD8 + thymocytes from 6 donors (P11 to P25). Each node represents 87 a single βCDR3, the colour of which represents its degree (log scale). c-e. Data are mean ± s.e.m. from 88 two DPCD3 -, ten DPCD3 + and twelve CD8 + thymocyte samples; c. Percentage 
The preferential selection of clustered public TCRs that could represent over 8% of the 96 sampled repertoire (Fig. 1g ) raises the question of their specificities 13 . As the main function of 97 CD8 + T cells is cytotoxicity towards virally infected cells, we investigated whether the clustered 98
TCRs could be associated with virus recognition. We curated databases of βCDR3s specific for 99 human infectious pathogens 14,15 to retain only those 5,437 that had been identified by 100 tetramer-based selection, i.e. binding to a soluble HLA bound to a defined peptide. We 101 detected an enrichment of these βCDR3s in CD8 + versus DPCD3 + thymocytes (p<0.0001) and in 102 clustered versus dispersed CD8 + thymocytes (p<0.0001) ( Fig. 2a were highly shared between individuals (Fig. 2d) . 106
We aimed to confirm these observations with virus-specific paired a and b TCR chains 107 obtained from single-cell TCR sequencing. These sequences were obtained from 160,914 blood 108 CD8 + T cells isolated from four healthy donors and incubated simultaneously with barcoded 109 dextramers complexed with peptides from CMV, EBV, HIV, HPV, HTLV and influenza 16 . We 110 observed a significant increase in the representation of the virus-specific a and b TCR chains in 111 CD8 + versus DPCD3 + thymocytes ( Fig. 2e and f), with a higher representation of the virus-112 specific alpha chains than that of the beta chains. Noteworthily, these TCRs are also mostly 113 represented in clustered rather than dispersed TCRs from CD8 + thymocytes (Fig. 2g,  114 Supplementary table 3). Altogether, these results indicate that the selection of clustered TCRs 115 with high generation probabilities corresponds, at least in part, to the selection of virus-116 associated TCRs whose CDR3s are remarkably conserved between individuals independently of 117 their HLA. 118 
Intriguingly, we noted that TCRs with different assigned specificities could be detected 135 within close proximity in single clusters (Supplementary Figure 12) , although this should 136 suggest similar specificities 6-12 (Supplementary Figure 1. ). We thus investigated this 137 observation in greater detail, at the single-cell level 16 . We first analysed the different TCRs 138 assigned to recognise EBV peptides, i.e. binding dextramers (Dex + ) matching the HLA of a given 139 individual loaded with EBV peptides (Fig. 3a) . Single TCRs were found to bind multiple 140 dextramers loaded with distinct peptides from EBV ( Noteworthily, there is only rare negative correlation (red dots) for the binding of dextramer 150 harbouring different peptides. 151
We also analysed the binding of HLA-mismatched dextramers. When HLA-mismatch 152 EBV Dex + cells are overlaid on a TSNE representation based on single-cell specificity (Fig. 3e) , 153 some of them corresponded to cells also labelled by HLA-matched EBV dextramers (Fig. 3a) , 154 while some others did not (Fig. 3e , red arrow). As for the HLA-matched dextramers, single TCRs 155 were found to bind HLA-mismatched dextramers loaded with distinct unrelated peptides from 156 EBV (Fig. 3f ) and some TCRs even bound HLA-mismatched dextramers loaded with epitopes 157 from different viruses such as CMV, HIV, HPV, HTLV-1 or influenza (Fig. 3g ). There were also 158 mostly positive correlations for the binding to different HLA-mismatched dextramer 159 specificities (Fig. 3h) . Altogether, within a dataset of >160,000 single CD8 + T cells, among the 160 66,191 that did bind dextramers, 24,083 could bind more than one viral-derived peptide from 
These peculiar properties led us to assess the functional relevance of the innate-like 192 recognition of psT cells' TCRs. We first evaluated the in vitro cross-activation of T cells with 193 different peptides. Human effector memory CD8 + T cells (emCD8 + ) were purified according to 194 their binding of CMV or EBV fluorescent dextramers; the sorted cells were then stimulated by 195 either the peptide that was used to purify them, or by different ones, and their activation was 196 measured by their IFNg production (Fig. 4a ). All T cells were efficiently non-specifically activated 197 by PMA/ionomycin. T cells that did not bind any dextramers could not be stimulated by any 198 peptide. In contrast, dextramer-sorted cells could be activated by their cognate peptide and 199 almost as well by the other (Fig. 4b) . Thus, the binding of multiple dextramers appears 200 functionally relevant, translating into proper psT cells activation by multiple unrelated peptides. 201
We then investigated whether we could detect the involvement of psT cells during in vivo 202 immune responses. We first analysed individuals vaccinated against yellow fever (YFV) (Fig.  203 4c) 20 or influenza (Flu) (Fig. 4d) , identifying their TCR repertoires responding to YFV and Flu 204 using the ALICE 21 and TCRNET algorithms 22,23 , respectively. Within these repertoires, we looked 205
for known viral-specific TCR sequences. Besides YFV-and Flu-specific b and a CDR3s (Fig. 4c &  206 d), we could also detect CDR3s of psT cells, i.e. assigned to one or even multiple other viral 207 specificities, notably for CMV and EBV. We then also analysed the TCR repertoire of T cells from 208 
The occurrence of each combination is shown for a (top bar plot) and β CDR3s (bottom bar plot). to fulminant immunopathology 34-36 . Interestingly, heterologous immunity has rarely been 254 linked to B cell/antibody responses, which might thus be the mediators of more specific 255 immune responses. In this regard, it is noteworthy that B cells have a machinery for somatic 256 mutations of their BCRs that ultimately allows them to generate antibodies with increased 257 affinity (specificity) for antigens. While TCR generation and BCR generation share many 258 common mechanisms, the fact that T cells did not evolve to use this available machinery is 259 another indication that T cell recognition could have been selected to be more fuzzy than 260 stringent. Further studies will have to evaluate the contribution of fuzzy immune responses to 261 the efficacy but also the immunopathology of antimicrobial responses and to autoimmunity. based on the following labelling: DPCD3were gated as CD3 -CD4 + CD8 + , DPCD3 + were gated as 283 CD3 + CD4 + CD8 + and CD8 + were gated as CD3 + CD4 -CD8 + . RNA was isolated from sorted To construct a network, we computed a distance matrix of pairwise Levenshtein distances 310 between CDR3s using the "stringdist" 38 R package. When two sequences were similar under 311 the defined threshold, LD>1 (i.e., at most one amino acid difference), they were connected and 312 designated as "clustered" nodes. CDR3s with more than one amino acid difference from any 313 other sequences are not connected and were designated as "dispersed" nodes. 314
Layout of networks for Fig. 1b and Supplementary Fig. 9a were obtained by using the graphopt 315 algorithm of the "Igraph" 39 R package and plotted in 2D with "ggplot2" to generate figures 40 . 316
Only clustered nodes are represented, edges are not shown and colours represent the node 317 degree (log scale). 318
Layouts of detailed networks in Fig. 1a, Fig. 2g, Fig. 4c, Fig. 4d & Supplementary Fig. 1a , for the b repertoire were: two for DPCD3 -, ten for DPCD3 + and twelve for CD8 + . The numbers 326 of samples included in the statistical analysis of the a repertoire were: six for DPCD3 + and ten 327 for CD8 + . Statistical tests used to analyse data are included in the figure legends. Comparisons 328 of two groups were done using the Mann-Whitney test (Fig. 1c, Fig. 1d, Fig. 1e, Fig. 1g, Fig. 2a , 329 Fig. 2c, Fig. 2e Fig. 2f, Fig. 4b) and multiple t-test ( Supplementary Fig. 5, Supplementary Fig. 9) . 330
The correlation coefficient was calculated using the Pearson correlation coefficient 331 ( Supplementary Fig. 4) . Enrichment of public CDR3s or virus-associated CDR3s was done using 332 the two-tailed Chi-square test with Yate's correction (Fig. 2f, Supplementary table 1, 2 & 3) and 333 the Fisher test ( Supplementary Fig. 8, Supplementary Fig. 11 ). Statistical comparisons and 334 multivariate analyses were performed using Prism (GraphPad Software, La Jolla, CA) and using 335 The generation probability (Pgen) of a sequence is inferred using the Olga 33 algorithm, which 346 is inferred by IGoR 44 , for Fig. 1e, Fig. 2c . IGoR uses out-of-frame sequence information to infer 347 patient-dependent models of VDJ recombination, effectively bypassing selection. From these 348 models, the probability of a given recombination scenario can be computed. The generation 349 probability of a sequence is then obtained by summing over all the scenarios that are 350 compatible with it. We also used OLGA to generate a random repertoire of 500,000 sequences 351 for each a or b repertoire and 3 down-sampling (of unique sequences) to get control repertoire 352 equal to the size of COVID-19 BAL dataset used in fig. 4c, 4d . The control repertoire was 353 parametrized by the predefined genomic templates provided with the package. In Supplementary Fig. 9 , the top 1,500 bCDR3s were sampled from each of the 12 datasets of 357 DPCD3 + and CD8 + , then merged to obtain two datasets of 18,000 bCDR3s for DPCD3 + and CD8 + . 358
We generated and represented networks, as described above, to investigate the bCDR3 inter-359 individual network structure. >95% to collect populations based on the following labelling: naïve CD8 were gated as CD3 + CD8 -418 CD45RA + CCR7 + and effector memory CD8 were gated as CD3 + CD8 -CD45RA -CCR7 -. RNA extraction, library preparation, sequencing and raw data processing were performed as 420 