Dynamic allostery drives autocrine and paracrine TGF-ß signaling.

TGF-ß, essential for development and immunity, is expressed as a latent complex (L-TGF-ß) non-covalently associated with its prodomain and presented on immune cell surfaces by covalent association with GARP. Binding to integrin αvß8 activates L-TGF-ß1/GARP. The dogma is that mature TGF-ß must physically dissociate from L-TGF-ß1 for signaling to occur. Our previous studies discovered that αvß8-mediated TGF-ß autocrine signaling can occur without TGF-ß1 release from its latent form. Here, we show that mice engineered to express TGF-ß1 that cannot release from L-TGF-ß1 survive without early lethal tissue inflammation, unlike those with TGF-ß1 deficiency. Combining cryogenic electron microscopy with cell-based assays, we reveal a dynamic allosteric mechanism of autocrine TGF-ß1 signaling without release where αvß8 binding redistributes the intrinsic flexibility of L-TGF-ß1 to expose TGF-ß1 to its receptors. Dynamic allostery explains the TGF-ß3 latency/activation mechanism and why TGF-ß3 functions distinctly from TGF-ß1, suggesting that it broadly applies to other flexible cell surface receptor/ligand systems.

Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data.

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Cyclone: an accessible pipeline to analyze, evaluate and optimize multiparametric cytometry data.

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

LTßR overexpression promotes plasma cell accumulation.

Multiple myeloma (MM), a malignancy of plasma cells (PCs), has diverse genetic underpinnings and in rare cases these include amplification of the lymphotoxin b receptor (Ltbr) locus. LTßR has well defined roles in supporting lymphoid tissue development and function through actions in stromal and myeloid cells, but whether it is functional in PCs is unknown. Here we showed that Ltbr mRNA was upregulated in mouse PCs compared to follicular B cells, but deficiency in the receptor did not cause a reduction in PC responses to a T-dependent or T-independent immunogen. However, LTßR overexpression (OE) enhanced PC formation in vitro after LPS or anti-CD40 stimulation. In vivo, LTßR OE led to increased antigen-specific splenic and bone marrow (BM) plasma cells responses. LTßR OE PCs had increased expression of Nfkb2 and of the NF-kB target genes Bcl2 and Mcl1, factors involved in the formation of long-lived BM PCs. Our findings suggest a pathway by which Ltbr gene amplifications may contribute to MM development through increased NF-kB activity and induction of an anti-apoptotic transcriptional program.