Autoimmune responses to meiotic germ cell antigens (MGCA) that are expressed on sperm and testis occur in human infertility and after vasectomy. Many MGCA are also expressed as cancer/testis antigens (CTA) in human cancers, but the tolerance status of MGCA has not been investigated. MGCA are considered to be uniformly immunogenic and nontolerogenic, and the prevailing view posits that MGCA are sequestered behind the Sertoli cell barrier in seminiferous tubules. Here, we have shown that only some murine MGCA are sequestered. Nonsequestered MCGA (NS-MGCA) egressed from normal tubules, as evidenced by their ability to interact with systemically injected antibodies and form localized immune complexes outside the Sertoli cell barrier. NS-MGCA derived from cell fragments that were discarded by spermatids during spermiation. They egressed as cargo in residual bodies and maintained Treg-dependent physiological tolerance. In contrast, sequestered MGCA (S-MGCA) were undetectable in residual bodies and were nontolerogenic. Unlike postvasectomy autoantibodies, which have been shown to mainly target S-MGCA, autoantibodies produced by normal mice with transient Treg depletion that developed autoimmune orchitis exclusively targeted NS-MGCA. We conclude that spermiation, a physiological checkpoint in spermatogenesis, determines the egress and tolerogenicity of MGCA. Our findings will affect target antigen selection in testis and sperm autoimmunity and the immune responses to CTA in male cancer patients.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Previously we have shown that T cell responses to the mycobacterial 60-kDa heat shock protein (hsp60) peptide M256–270 mediated protection against adjuvant arthritis in Lewis rats. We have demonstrated now that M256–270-primed T cells become highly reactive to naive syngeneic APC upon repetitive restimulation in vitro with peptide M256–265, comprising the conserved core of peptide M256–270. These autoproliferative responses in the absence of added Ag were MHC class II restricted and resulted in the production of IL-4/IL-10 and IFN-γ. Enhanced autoproliferation and expression of the cell surface molecule B7.2 by these T cells were observed in response to syngeneic heat-shocked APC, which indicated that the autoproliferation and expression of B7.2 resulted from the recognition of endogenously expressed and processed hsp. Despite their strong autoreactivity, upon transfer such T cells were found to induce a significant disease reduction in adjuvant arthritis. In contrast, T cells both primed and restimulated with peptide M256–270 became unresponsive toward syngeneic APC as well as toward the conserved core peptide M256–265, and they were devoid of protective capacity. This study demonstrates that the loss of self-tolerance toward hsp60 does not necessarily lead to autoimmune disease, but that hsp60-specific self-reactive and autoproliferative T cells may mediate T cell regulation in arthritis.

Flow cytometry facilitates the detection of multiple cell parameters simultaneously with a high level of resolution and throughput, enabling in-depth immunological evaluations. High data resolution in flow cytometry depends on multiple factors, including the concentration of reagents used in the staining protocol, and reagent validation and titration should be the first step in any assay optimization. Titration is the process of finding the concentration of the reagent that best resolves a positive signal from the background, with the saturation of all binding sites, and minimal antibody excess. The titration process involves the evaluation of serial reagent dilutions in cells expressing the antigen target for the tested antibody. The concentration of antibody that provides the highest signal to noise ratio is calculated by plotting the percentage of positive cells and the intensity of the fluorescence of the stained cells with respect to the negative events, in a concentration–response curve. The determination of the optimal antibody concentration is necessary to ensure reliable and reproducible results and is required for each sample type, reagent clone and lot, as well as the methods used for cell collection, staining, and storage conditions. If the antibody dilution is too low, the signal will be too weak to be accurately determined, leading to suboptimal data resolution, high variability across measurements, and the underestimation of the frequency of cells expressing a specific marker. The use of excess antibodies could lead to non-specific binding, reagent misuse, and detector overloading with the signal off scale and higher spillover spreading. In this publication, we summarized the titration fundamentals and best practices, and evaluated the impact of using a different instrument, sample, staining, acquisition, and analysis conditions in the selection of the optimal titer and population resolution.