Site-Specific DC Surface Signatures Influence CD4⁺ T Cell Co-stimulation and Lung-Homing

Dendritic cells (DCs) that drain the gut and skin are known to favor the establishment of T cell populations that home to the original site of DC-antigen (Ag) encounter by providing soluble "imprinting" signals to T cells in the lymph node (LN). To study the induction of lung T cell-trafficking, we used a protein-adjuvant murine intranasal and intramuscular immunization model to compare in vivo-activated Ag+ DCs in the lung and muscle-draining LNs. Higher frequencies of Ag+ CD11b+ DCs were observed in lung-draining mediastinal LNs (MedLN) compared to muscle-draining inguinal LNs (ILN). Ag+ CD11b+ MedLN DCs were qualitatively superior at priming CD4+ T cells, which then expressed CD49a and CXCR3, and preferentially trafficked into the lung parenchyma. CD11b+ DCs from the MedLN expressed higher levels of surface podoplanin, Trem4, GL7, and the known co-stimulatory molecules CD80, CD86, and CD24. Blockade of specific MedLN DC molecules or the use of sorted DC and T cell co-cultures demonstrated that DC surface phenotype influences the ability to prime T cells that then home to the lung. Thus, the density of dLN Ag+ DCs, and DC surface molecule signatures are factors that can influence the output and differentiation of lung-homing CD4+ T cells

Vaccination with M2e-Based Multiple Antigenic Peptides: Characterization of the B Cell Response and Protection Efficacy in Inbred and Outbred Mice

The extracellular domain of the influenza A virus protein matrix protein 2 (M2e) is remarkably conserved between various human isolates and thus is a viable target antigen for a universal influenza vaccine. With the goal of inducing protection in multiple mouse haplotypes, M2e-based multiple antigenic peptides (M2e-MAP) were synthesized to contain promiscuous T helper determinants from the Plasmodium falciparum circumsporozoite protein, the hepatitis B virus antigen and the influenza virus hemagglutinin. Here, we investigated the nature of the M2e-MAP-induced B cell response in terms of the distribution of antibody (Ab) secreting cells (ASCs) and Ab isotypes, and tested the protective efficacy in various mouse strains.Immunization of BALB/c mice with M2e-MAPs together with potent adjuvants, CpG 1826 oligonucleotides (ODN) and cholera toxin (CT) elicited high M2e-specific serum Ab titers that protected mice against viral challenge. Subcutaneous (s.c.) and intranasal (i.n.) delivery of M2e-MAPs resulted in the induction of IgG in serum and airway secretions, however only i.n. immunization induced anti-M2e IgA ASCs locally in the lungs, correlating with M2-specific IgA in the bronchio-alveolar lavage (BAL). Interestingly, both routes of vaccination resulted in equal protection against viral challenge. Moreover, M2e-MAPs induced cross-reactive and protective responses to diverse M2e peptides and variant influenza viruses. However, in contrast to BALB/c mice, immunization of other inbred and outbred mouse strains did not induce protective Abs. This correlated with a defect in T cell but not B cell responsiveness to the M2e-MAPs.Anti-M2e Abs induced by M2e-MAPs are highly cross-reactive and can mediate protection to variant viruses. Although synthetic MAPs are promising designs for vaccines, future constructs will need to be optimized for use in the genetically heterogeneous human population

SPADEVizR: an R package for Visualization, Analysis and Integration of SPADE results

International audienceMotivation: Flow, hyperspectral and mass cytometry are experimental techniques measuring cell marker expressions at the single cell level. The recent increase of the number of markers simultaneously measurable has led to the development of new automatic gating algorithms. Especially, the SPADE algorithm has been proposed as a novel way to identify clusters of cells having similar phenotypes in high-dimensional cytometry data. While SPADE or other cell clustering algorithms are powerful approaches, complementary analysis features are needed to better characterize the identified cell clusters. Results: We have developed SPADEVizR, an R package designed for the visualization, analysis and integration of cell clustering results. The available statistical methods allow highlighting cell clusters with relevant biological behaviors or integrating them with additional biological variables. Moreover, several visualization methods are available to better characterize the cell clusters, such as volcano plots, streamgraphs, parallel coordinates, heatmaps, or distograms. SPADEVizR can also generate linear, Cox or random forest models to predict biological outcomes, based on the cell cluster abundances. Additionally, SPADEVizR has several features allowing to quantify and to visualize the quality of the cell clustering results. These analysis features are essential to better interpret the behaviors and phenotypes of the identified cell clusters. Importantly, SPADEVizR can handle clustering results from other algorithms than SPADE

A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data

International audienceBackground: Cytometry is an experimental technique used to measure molecules expressed by cells at a single cell resolution. Recently, several technological improvements have made possible to increase greatly the number of cell markers that can be simultaneously measured. Many computational methods have been proposed to identify clusters of cells having similar phenotypes. Nevertheless, only a limited number of computational methods permits to compare the phenotypes of the cell clusters identified by different clustering approaches. These phenotypic comparisons are necessary to choose the appropriate clustering methods and settings. Because of this lack of tools, comparisons of cell cluster phenotypes are often performed manually, a highly biased and time-consuming process. Results: We designed CytoCompare, an R package that performs comparisons between the phenotypes of cell clusters with the purpose of identifying similar and different ones, based on the distribution of marker expressions. For each phenotype comparison of two cell clusters, CytoCompare provides a distance measure as well as a p-value asserting the statistical significance of the difference. CytoCompare can import clustering results from various algorithms including SPADE, viSNE/ACCENSE, and Citrus, the most current widely used algorithms. Additionally, CytoCompare can generate parallel coordinates, parallel heatmaps, multidimensional scaling or circular graph representations to visualize easily cell cluster phenotypes and the comparison results. Conclusions: CytoCompare is a flexible analysis pipeline for comparing the phenotypes of cell clusters identified by automatic gating algorithms in high-dimensional cytometry data. This R package is ideal for benchmarking different clustering algorithms and associated parameter