Summary While a third of the world carries the burden of tuberculosis, disease control has been hindered by the lack of tools including a rapid, point-of-care diagnostic and a protective vaccine. In many infectious diseases, antibodies (Abs) are powerful biomarkers and important immune mediators. However, in Mycobacterium tuberculosis (Mtb) infection, a discriminatory or protective role for humoral immunity remains unclear. Using an unbiased antibody profiling approach we show that individuals with latent tuberculosis infection (Ltb) and active tuberculosis disease (Atb) have distinct Mtb-specific humoral responses, such that Ltb infection is associated with unique Ab Fc functional profiles, selective binding to FcγRIII, and distinct Ab glycosylation patterns. Moreover, compared to Abs from Atb, Abs from Ltb drove enhanced phagolysosomal maturation, inflammasome activation, and most importantly, macrophage killing of intracellular Mtb. Combined, these data point to a potential role for Fc-mediated Ab effector functions, tuned via differential glycosylation, in Mtb control.

While antibody titers and neutralization are considered the gold standard for the selection of successful vaccines, these parameters are often inadequate predictors of protective immunity. As antibodies mediate an array of extra-neutralizing Fc-functions, when neutralization fails to predict protection, investigating Fc-mediated activity may help identify immunological correlates and mechanism(s) of humoral protection. Here, we used an integrative approach termed Systems Serology to analyze relationships among humoral responses elicited in four HIV vaccine-trials. Each vaccine regimen induced a unique humoral “Fc-fingerprint”. Moreover, analysis of case:control data from the first moderately protective HIV vaccine trial, RV144, pointed to mechanistic insights into immune complex composition that may underlie protective immunity to HIV. Thus, multi-dimensional relational comparisons of vaccine humoral fingerprints offer a unique approach for the evaluation and design of novel vaccines against pathogens for which correlates of protection remain elusive.

Graphical AbstractHighlights d Ligand-receptor interactions in tumors were investigated using single-cell RNA-seq d Identified interactions were regressed against phenotypic measurements of tumors d The approach provides a tool for studying cell-cell interactions and their variability In BriefTumors are composed of cancer cells and many non-malignant cell types, such as immune and stromal cells. To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling. SUMMARYTumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome.We thank Merrimack Pharmaceuticals, Inc. for sponsoring the research and supporting publication of the results and the internal review team for reviewing the article prior to submission. We also acknowledge the NIGMS Interdisciplinary Biotechnology Training Program (T32-GM008334) and NCI (U01-CA215798) for funding (to M.P.K. and D.A.L.). K. performed all computational analyses. J.D. and Y.J. processed the mouse syngeneic tumor samples, performed the flow cytometry analysis, and obtained the scRNA-seq data. A.S. provided the tumor growth data. M.P.K., A.R., and G.L. helped design the computational analysis. D.C.

Background: Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. Results: To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knockout. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Conclusions: Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Methods to parse paracrine epithelial-stromal communication networks are a vital need in drug development, as disruption of these networks underlies diseases ranging from cancer to endometriosis. Here, we describe a modular, synthetic, and dissolvable extracellular matrix (MSD-ECM) hydrogel that fosters functional 3D epithelial-stromal co-culture, and that can be dissolved on-demand to recover cells and paracrine signaling proteins intact for subsequent analysis. Specifically, synthetic polymer hydrogels, modified with cell-interacting adhesion motifs and crosslinked with peptides that include a substrate for cell-mediated proteolytic remodeling, can be rapidly dissolved by an engineered version of the microbial transpeptidase Sortase A (SrtA) if the crosslinking peptide includes a SrtA substrate motif and a soluble second substrate. SrtA-mediated dissolution affected only 1 of 31 cytokines and growth factors assayed, whereas standard protease degradation methods destroyed about half of these same molecules. Using co-encapsulated endometrial epithelial and stromal cells as one model system, we show that the dynamic cytokine and growth factor response of co-cultures to an inflammatory cue is richer and more nuanced when measured from SrtA-dissolved gel microenvironments than from the culture supernate. This system employs accessible, reproducible reagents and facile protocols; hence, has potential as a tool in identifying and validating therapeutic targets in complex diseases.

By combining univariate and multivariate analyses, profiles of cytokines more likely to be enriched or depleted in infertility patients with endometriosis compared with those without endometriosis were identified. These findings may inform future analyses of pathophysiological mechanisms of endometriosis in infertile patients, including dysregulated Th1/Th2 response and mobilization and proliferation of hematopoietic stem cells.

No abstract

Nearly all studies of angiogenesis have focused on uni-family ligand-receptor binding, e.g., VEGFs bind to VEGF receptors, PDGFs bind to PDGF receptors, etc. The discovery of VEGF-PDGFRs binding challenges this paradigm and calls for investigation of other ligand-receptor binding possibilities. We utilized surface plasmon resonance to identify and measure PDGF-to-VEGFR binding rates, establishing cut-offs for binding and non-binding interactions. We quantified the kinetics of the recent VEGF-A:PDGFRβ interaction for the first time with KD = 340 pM. We discovered new PDGF:VEGFR2 interactions with PDGF-AA:R2 KD = 530 nM, PDGF-AB:R2 KD = 110 pM, PDGF-BB:R2 KD = 40 nM, and PDGF-CC:R2 KD = 70 pM. We computationally predict that cross-family PDGF binding could contribute up to 96% of VEGFR2 ligation in healthy conditions and in cancer. Together the identification, quantification, and simulation of these novel cross-family interactions posits new mechanisms for understanding anti-angiogenic drug resistance and presents an expanded role of growth factor signaling with significance in health and disease.