iGATE analysis improves the interpretability of single-cell immune landscape of influenza infection.

Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We have developed a new analytical approach, in silico gating annotating training elucidating (iGATE), based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared with BALB/c was associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Furthermore, we demonstrate that the most prominent effect of aging is a defective T cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibited differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varied with host genotype or aging. The software is available at https://github.com/UmichWenLab/iGATE.

Antigen-Clustered Nanovaccine Achieves Long-Term Tumor Remission by Promoting B/CD 4 T Cell Crosstalk.

Current cancer vaccines using T cell epitopes activate antitumor T cell immunity through dendritic cell/macrophage-mediated antigen presentation, but they lack the ability to promote B/CD4 T cell crosstalk, limiting their anticancer efficacy. We developed antigen-clustered nanovaccine (ACNVax) to achieve long-term tumor remission by promoting B/CD4 T cell crosstalk. The topographic features of ACNVax were achieved using an iron nanoparticle core attached with an optimal number of gold nanoparticles, where the clusters of HER2 B/CD4 T cell epitopes were conjugated on the gold surface with an optimal intercluster distance of 5-10 nm. ACNVax effectively trafficked to lymph nodes and cross-linked with BCR, which are essential for stimulating B cell antigen presentation-mediated B/CD4 T cell crosstalk in vitro and in vivo. ACNVax, combined with anti-PD-1, achieved long-term tumor remission (>200 days) with 80% complete response in mice with HER2+ breast cancer. ACNVax not only remodeled the tumor immune microenvironment but also induced a long-term immune memory, as evidenced by complete rejection of tumor rechallenge and a high level of antigen-specific memory B, CD4, and CD8 cells in mice (>200 days). This study provides a cancer vaccine design strategy, using B/CD4 T cell epitopes in an antigen clustered topography, to achieve long-term durable anticancer efficacy through promoting B/CD4 T cell crosstalk.

Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS).

CD4+ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.

Rapid microsphere-assisted peptide screening (MAPS) of promiscuous MHCII-binding peptides in Zika virus envelope protein.

Despite promising developments in computational tools, peptide-class II MHC (MHCII) binding predictors continue to lag behind their peptide-class I MHC counterparts. Consequently, peptide-MHCII binding is often evaluated experimentally using competitive binding assays, which tend to sacrifice throughput for quantitative binding detail. Here, we developed a high-throughput semiquantitative peptide-MHCII screening strategy termed microsphere-assisted peptide screening (MAPS) that aims to balance the accuracy of competitive binding assays with the throughput of computational tools. Using MAPS, we screened a peptide library from Zika virus envelope (E) protein for binding to four common MHCII alleles (DR1, DR4, DR7, DR15). Interestingly, MAPS revealed a significant overlap between peptides that promiscuously bind multiple MHCII alleles and antibody neutralization sites. This overlap was also observed for rotavirus outer capsid glycoprotein VP7, suggesting a deeper relationship between B cell and CD4+ T cell specificity which can facilitate the design of broadly protective vaccines to Zika and other viruses.

Elucidating structure-performance relationships in whole-cell cooperative enzyme catalysis.

Cooperative enzyme catalysis in nature has long inspired the application of engineered multi-enzyme assemblies for industrial biocatalysis. Despite considerable interest, efforts to harness the activity of cell-surface displayed multi-enzyme assemblies have been based on trial and error rather than rational design due to a lack of quantitative tools. In this study, we developed a quantitative approach to whole-cell biocatalyst characterization enabling a comprehensive study of how yeast-surface displayed multi-enzyme assemblies form. Here we show that the multi-enzyme assembly efficiency is limited by molecular crowding on the yeast cell surface, and that maximizing enzyme density is the most important parameter for enhancing cellulose hydrolytic performance. Interestingly, we also observed that proximity effects are only synergistic when the average inter-enzyme distance is > ~130 nm. The findings and the quantitative approach developed in this work should help to advance the field of biocatalyst engineering from trial and error to rational design.