Identifying immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors (preprint)

Memory T cells are records of clonal expansion from prior immune exposures, such as infections, vaccines and chronic diseases like cancer. A subset of the receptors of these expanded T cells in a typical immune repertoire are highly public, i.e., present in many individuals exposed to the same exposure. For the most part, the exposures associated with these public T cells are unknown. To identify public T-cell receptor signatures of immune exposures, we mined the immunosequencing repertoires of tens of thousands of donors to define clusters of co-occurring T cells. We first built co-occurrence clusters of T cells responding to antigens presented by the same Human Leukocyte Antigen (HLA) and then combined those clusters across HLAs. Each cross-HLA cluster putatively represents the public T-cell signature of a single prevalent exposure. Using repertoires from donors with known serological status for 7 prevalent exposures (HSV-1, HSV-2, EBV, Parvovirus, Toxoplasma gondii, Cytomegalovirus and SARS CoV-2), we identified a single T-cell cluster strongly associated with each exposure and used it to construct a highly sensitive and specific diagnostic model for the exposure. These T-cell clusters constitute the public immune responses to prevalent exposures, 7 known and many others unknown. By learning the exposure associations for more T cell clusters, this approach could be used to derive a ledger of a person's past and present immune exposures.

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires (preprint)

Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-specific TCRs. Population genetics and sequencing depth can also have strong systematic effects on repertoires, which requires careful consideration when developing diagnostic models. We present an Adaptive Immune Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that learns a low-dimensional, interpretable, and compositional representation of TCR repertoires to disentangle such systematic effects in repertoires. We apply AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically show that we can disentangle the individual disease signals. We further demonstrate AIRIVA's capability to: learn from unlabelled samples; generate in-silico TCR repertoires by intervening on the latent factors; and identify disease-associated TCRs validated using TCR annotations from external assay data.

Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels (preprint)

T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,015 samples (from 827 COVID-19 subjects) as well as 3,500 controls to identify shared "public" T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3-7 = 83.8% [95% CI = 77.6-89.4]; Day 8-14 = 92.4% [87.6-96.6]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 96.7% [93.0-99.2]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in vaccine development as well as clinical diagnostics and monitoring.