Adaptive immune responses to SARS-CoV-2 infection have been extensively characterized in blood; however, most functions of protective immunity must be accomplished in tissues. Here, we report from examination of SARS-CoV-2 seropositive organ donors (ages 10 -74) that CD4 + T, CD8 + T, and B cell memory generated in response to infection is present in bone marrow, spleen, lung, and multiple lymph nodes (LNs) for up to 6 months post-infection. Lungs and lung-associated LNs were the most prevalent sites for SARS-CoV-2-specific memory T and B cells, with significant correlations between circulating and tissue-resident memory T and B cells in all sites. We further identified SARS-CoV-2-specific germinal centers in the lung-associated LNs up to 6 months post-infection. SARS-CoV-2-specific follicular helper T cells were also abundant in lung-associated LNs and lungs. Together, the results indicate local tissue coordination of cellular and humoral immune memory against SARS-CoV-2 for site-specific protection against future infectious challenges.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML. 1.

Several immunotherapies have demonstrated endogenous insulin preservation in recent-onset type 1 diabetes (T1D). We considered the primary results of rituximab, abatacept, teplizumab, alefacept, high-dose antithymocyte globulin (ATG), low-dose ATG, and low-dose ATG ± granulocyte-colony–stimulating factor trials in an attempt to rank the effectiveness of the agents studied. C-peptide 2-h area under the curve means were modeled using analysis of covariance. The experimental treatment group effect for each study, compared with its internal control, was estimated after adjusting for baseline C-peptide and age. Percentage increase in C-peptide over placebo and the absolute difference within study were calculated to compare and contrast effect size among interventions. Low-dose ATG (55% and 103%) and teplizumab (48% and 63%) ranked highest in C-peptide preservation at 1 and 2 years, respectively. Low-dose ATG and teplizumab show the greatest impact on C-peptide preservation among recent new-onset T1D studies; these should be further explored as core immunotherapies in the T1D prevention setting.

The persistence of anti-viral immunity is essential for protection and exhibits profound heterogeneity across individuals. Here, we elucidate the factors that shape maintenance and function of anti-viral T cell immunity in the body by comprehensive profiling of virus-specific T cells across blood, lymphoid organs, and mucosal tissues of organ donors. We use flow cytometry, T cell receptor sequencing, single-cell transcriptomics, and cytokine analysis to profile virus-specific CD8 + T cells recognizing the ubiquitous pathogens influenza and cytomegalovirus. Our results reveal that virus specificity determines overall magnitude, tissue distribution, differentiation, and clonal repertoire of virus-specific T cells. Age and sex influence T cell differentiation and dissemination in tissues, while T cell tissue residence and functionality are highly correlated with the site. Together, our results demonstrate how the covariates of virus, tissue, age, and sex impact the anti-viral immune response, which is important for targeting, monitoring, and predicting immune responses to existing and emerging viruses.

Background: Increased circulating myeloid-derived suppressor cells (MDSCs) are independently associated with poor long-term clinical outcomes in sepsis. Studies implicate subsets of MDSCs having unique roles in lymphocyte suppression; however, characterization of these cells after sepsis remains incomplete. We performed a pilot study to determine the transcriptomic landscape in MDSC subsets in sepsis using single-cell RNAseq (scRNA-seq). Methods: A mixture of whole blood myeloid-enriched and Ficoll-enriched PBMCs from two late septic patients on post-sepsis day 21 and two control subjects underwent Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq). Results: We successfully identified the three MDSC subset clusters-granulocytic (G-), monocytic (M-), and early (E-) MDSCs. Sepsis was associated with a greater relative expansion of G-MDSCs versus M-MDSCs at 21 days as compared to control subjects. Genomic analysis between septic patients and control subjects revealed cell-specific and common differential expression of genes in both G-MDSC and M-MDSC subsets. Many of the common genes have previously been associated with MDSC proliferation and immunosuppressive function. Interestingly, there was no differential expression of several genes demonstrated in the literature to be vital to immunosuppression in cancer-induced MDSC. Conclusion: This pilot study successfully demonstrated that MDSCs maintain a transcriptomic profile that is immunosuppressive in late sepsis. Interestingly, the landscape in chronic critical illness is partially dependent on the original septic insult. Preliminary data would also indicate immunosuppressive MDSCs from late sepsis patients appear to have a somewhat unique transcriptome from cancer and/or other inflammatory diseases.