Back

Immunosequencing of the T-cell receptor repertoire reveals signatures specific for diagnosis and characterization of early Lyme disease

Greissl, J.; Pesesky, M.; Dalai, S. C.; Rebman, A. W.; Soloski, M. J.; Horn, E. J.; Dines, J. N.; Gittelman, R. M.; Snyder, T. M.; Emerson, R. O.; Meeds, E.; Manley, T.; Kaplan, I. M.; Baldo, L.; Carlson, J. M.; Robins, H. S.; Aucott, J. N.

2021-08-02 infectious diseases
10.1101/2021.07.30.21261353 medRxiv
Show abstract

Highly specific T-cell responses play key roles in pathogen clearance and maintaining immunologic memory. Next-generation sequencing of the T-cell receptor (TCR) repertoire is an emerging diagnostic technology that capitalizes on the specificity of T-cell responses to probe pathogen exposure. The spirochete Borrelia burgdorferi (Bb) wields an array of antigens with dynamic and complex immunogenic potential, and application of TCR immunosequencing to characterize Bb infection presents opportunities to improve detection of early Lyme disease (LD). By immunosequencing TCR repertoires in blood samples from 3 independent cohorts of patients with early LD and controls from Lyme-endemic/non-endemic regions, we identified 251 public, LD-associated TCRs. These TCRs were used to train a classifier for detection of early LD. The classifier identified LD with 99% specificity and showed 1.9-fold higher sensitivity (56% vs 30%) compared with standard two-tiered testing (STTT). TCR positivity predicted subsequent seroconversion in 37% of STTT-negative patients, suggesting that the T-cell response is detectable before the humoral response. Higher TCR scores were associated with clinical measures of disease severity, including abnormal liver function tests, disseminated rash, and number of symptoms. A subset of LD-associated TCRs mapped to Bb antigens, supporting specificity of this approach. These results suggest that TCR testing may be a highly specific and sensitive approach for identifying LD, particularly in the initial days of illness.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.