Michelle L. McKeague

Limiting CD4 + T cell responses is important to prevent solid organ transplant rejection. In a mouse model of costimulation blockade-dependent cardiac allograft tolerance, we previously reported that alloreactive CD4 + conventional T cells (Tconvs) develop dysfunction, losing proliferative capacity. In parallel, induction of transplantation tolerance is dependent on the presence of regulatory T cells (Tregs). Whether susceptibility of CD4 + Tconvs to Treg suppression is modulated during tolerance induction is unknown. We found that alloreactive Tconvs from transplant tolerant mice had augmented sensitivity to Treg suppression when compared with memory T cells from rejector mice and expressed a transcriptional profile distinct from these memory T cells, including down-regulated expression of the transcription factor Special AT-rich sequence-binding protein 1 (Satb1). Mechanistically, Satb1 deficiency in CD4 + T cells limited their expression of CD25 and IL-2, and addition of Tregs, which express higher levels of CD25 than Satb1-deficient Tconvs and successfully competed for IL-2, resulted in greater suppression of Satb1-deficient than wild-type Tconvs in vitro. In vivo, Satb1-deficient Tconvs were more susceptible to Treg suppression, resulting in significantly prolonged skin allograft survival. Overall, our study reveals that transplantation tolerance is associated with Tconvs’ susceptibility to Treg suppression, via modulated expression of Tconv-intrinsic Satb1. Targeting Satb1 in the context of Treg-sparing immunosuppressive therapies might be exploited to improve transplant outcomes.

SARS-CoV-2 infection of vaccinated individuals is increasingly common but rarely results in severe disease, likely due to the enhanced potency and accelerated kinetics of memory immune responses. However, there have been few opportunities to rigorously study early recall responses during human viral infection. To better understand human immune memory and identify potential mediators of lasting vaccine efficacy, we used high-dimensional flow cytometry and SARS-CoV-2 antigen probes to examine immune responses in longitudinal samples from vaccinated individuals infected during the Omicron wave. These studies revealed heightened Spike-specific responses during infection of vaccinated compared to unvaccinated individuals. Spike-specific CD4 T cells and plasmablasts expanded and CD8 T cells were robustly activated during the first week. In contrast, memory B cell activation, neutralizing antibody production, and primary responses to non-Spike antigens occurred during the second week. Collectively, these data demonstrate the functionality of vaccine-primed immune memory and highlight memory T cells as rapid responders during SARS-CoV-2 infection.

Recent advances in cytometry have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance in cytometry has posed a formidable challenge during the gating process, especially for the initial pre-gates which deal with unpredictable events, such as debris and technical artifacts. To mitigate the labor-intensive manual gating process, we propose UNITO, a framework to rigorously identify the hierarchical cytometric subpopulations. UNITO transforms a cell-level classification task into an image-based segmentation problem. The framework is validated on three independent cohorts (two mass cytometry and one flow cytometry datasets). We compare its results with previous automated methods using the consensus of at least four experienced immunologists. UNITO outperforms existing methods and deviates from human consensus by no more than any individual does. UNITO can reproduce a similar contour compared to manual gating for post-hoc inspection, and it also allows parallelization of samples for faster processing. High-throughput cytometry generates complex single-cell data with challenging manual gating process. Here, authors introduce UNITO, a framework that transforms cell classification into image segmentation, outperforming existing methods in identifying cytometric subpopulations across diverse datasets.