CD4+ TSCMs in the Bone Marrow Assist in Maturation of Antibodies against Influenza in Mice

MMP2 and TLRs modulate immune responses in the tumor microenvironment

The presence of an immunosuppressive tumor microenvironment is a major obstacle in the success of cancer immunotherapies. Because extracellular matrix components can shape the microenvironment, we investigated the role of matrix metalloproteinase 2 (MMP2) in melanoma tumorigenesis. We found that MMP2 signals proinflammatory pathways on antigen presenting cells, and this requires both TLR2 and TLR4. B16 melanoma cells that express MMP2 at baseline have slower kinetics in Tlr2^–/–Tlr4^–/– mice, implicating MMP2 in promoting tumor growth. Indeed, Mmp2 overexpression in B16 cells potentiated rapid tumor growth, which was accompanied by reduced intratumoral cytolytic cells and increased M2 macrophages. In contrast, knockdown of Mmp2 slowed tumor growth and enhanced T cell proliferation and NK cell recruitment. Finally, we found that these effects of MMP2 are mediated through dysfunctional DC–T cell cross-talk as they are lost in Batf3^–/– and Rag2^–/– mice. These findings provide insights into the detrimental role of endogenous alarmins like MMP2 in modulating immune responses in the tumor microenvironment.

Targeting TIGIT Inhibits Bladder Cancer Metastasis Through Suppressing IL-32

Bladder cancer is a highly metastatic tumor and one of the most common malignancies originating in the urinary tract. Despite the efficacy of immune checkpoints, including programmed cell death-1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), the effect of immunotherapy for bladder cancer remains unsatisfactory. Therefore, it is urgent to develop new targets to expand immunotherapeutic options. In this study, we utilized single-cell sequencing to explore the cell composition of tumors and detected a subset of Treg cells with high expression of T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) and interleukin (IL)-32. The antitumor immune response was suppressed by this subset of Treg cells, while IL-32 promoted bladder cancer metastasis. Nevertheless, targeting TIGIT not only reversed immunosuppression by restoring the antitumor immune response mediated by T cells but also suppressed the secretion of IL-32 and inhibited the metastasis of bladder cancer cells. Thus, our study provided novel insights into immunosuppression in bladder cancer and highlighted TIGIT as a novel target for immunotherapy of bladder cancer. We also illustrated the mechanism of the dual effect of targeting TIGIT and revealed the metastasis-promoting effect of IL-32 in bladder cancer. Collectively, these findings raise the possibility of utilizing TIGIT as a target against bladder cancer from the bench to the bedside.

Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease

Flow cytometers can now analyze up to 50 parameters per cell and millions of cells per sample; however, conventional methods to analyze data are subjective and time-consuming. To address these issues, we have developed a novel flow cytometry analysis pipeline to identify a plethora of cell populations efficiently. Coupled with feature engineering and immunological context, researchers can immediately extrapolate novel discoveries through easy-to-understand plots. The R-based pipeline uses Fluorescence Minus One (FMO) controls or distinct population differences to develop thresholds for positive/negative marker expression. The continuous data is transformed into binary data, capturing a positive/negative biological dichotomy often of interest in characterizing cells. Next, a filtering step refines the data from all identified cell phenotypes to populations of interest. The data can be partitioned by immune lineages and statistically correlated to other experimental measurements. The pipeline’s modularity allows customization of statistical testing, adoption of alternative initial gating steps, and incorporation of other datasets. Validation of this pipeline through manual gating of two datasets (murine splenocytes and human whole blood) confirmed its accuracy in identifying even rare subsets. Lastly, this pipeline can be applied in all disciplines utilizing flow cytometry regardless of cytometer or panel design. The code is available at https://github.com/aef1004/cyto-feature_engineering.