The functions and transcriptional profiles of dendritic cells (DCs) result from the interplay between ontogeny and tissue imprinting. How tumors shape human DCs is unknown. Here we used RNA-based next-generation sequencing to systematically analyze the transcriptomes of plasmacytoid pre-DCs (pDCs), cell populations enriched for type 1 conventional DCs (cDC1s), type 2 conventional DCs (cDC2s), CD14 DCs and monocytes-macrophages from human primary luminal breast cancer (LBC) and triple-negative breast cancer (TNBC). By comparing tumor tissue with non-invaded tissue from the same patient, we found that 85% of the genes upregulated in DCs in LBC were specific to each DC subset. However, all DC subsets in TNBC commonly showed enrichment for the interferon pathway, but those in LBC did not. Finally, we defined transcriptional signatures specific for tumor DC subsets with a prognostic effect on their respective breast-cancer subtype. We conclude that the adjustment of DCs to the tumor microenvironment is subset specific and can be used to predict disease outcome. Our work also provides a resource for the identification of potential targets and biomarkers that might improve antitumor therapies.

Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.

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Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.

COVID-19 can lead to life-threatening acute respiratory failure, characterized by simultaneous increase in inflammatory mediators and viral load. The underlying cellular and molecular mechanisms remain unclear. We performed single-cell RNA-sequencing to establish an exhaustive high-resolution map of blood antigen-presenting cells (APC) in 7 COVID-19 patients with moderate or severe pneumonia, at day-1 and day-4 post-admission, and two healthy donors. We generated a unique dataset of 31,513 high quality APC, including monocytes and rare dendritic cell (DC) subsets. We uncovered multiprocess and previously unrecognized defects in anti-viral immune defense in specific APC compartments from severe patients: i) increase of pro-apoptotic genes exclusively in pDC, which are key effectors of antiviral immunity, ii) sharp decrease of innate sensing receptors, TLR7 and DHX9, in pDC and cDC1, respectively, iii) down-regulation of antiviral effector molecules, including Interferon stimulated genes (ISG) in all monocyte subsets, and iv) decrease of MHC class II-related genes, and MHC class II transactivator (CIITA) activity in cDC2, suggesting a viral inhibition of antigen presentation. These novel mechanisms may explain patient aggravation and suggest strategies to restore defective immune defense.

Only some cancer patients respond to the immune-checkpoint inhibitors being used in the clinic, and other therapeutic targets are sought. Here, we investigated the HLA-G/ILT2 checkpoint in clear-cell renal-cell carcinoma (ccRCC) patients and focused on tumor-infiltrating CD8+ T lymphocytes (TIL) expressing the HLA-G receptor ILT2. Using transcriptomics and flow cytometry, we characterized both peripheral blood and tumor-infiltrating CD8+ILT2+ T cells from cancer patients as late-differentiated CD27–CD28–CD57+ cytotoxic effectors. We observed a clear dichotomy between CD8+ILT2+ and CD8+PD-1+ TIL subsets. These subsets, which were sometimes present at comparable frequencies in TIL populations, barely overlapped phenotypically and were distinguished by expression of exclusive sets of surface molecules that included checkpoint molecules and activating and inhibitory receptors. CD8+ILT2+ TILs displayed a more mature phenotype and higher expression of cytotoxic molecules. In ex vivo functional experiments with both peripheral blood T cells and TILs, CD8+ILT2+ T cells displayed significantly higher cytotoxicity and IFNγ production than their ILT2– (peripheral blood mononuclear cells, PBMC) and PD-1+ (TILs) counterparts. HLA-G expression by target cells specifically inhibited CD8+ILT2+ T-cell cytotoxicity, but not that of their CD8+ILT2– (PBMC) or CD8+PD-1+ (TIL) counterparts, an effect counteracted by blocking the HLA-G/ILT2 interaction. CD8+ILT2+ TILs may therefore constitute an untapped reservoir of fully differentiated cytotoxic T cells within the tumor microenvironment, independent of the PD1+ TILs targeted by immune therapies, and specifically inhibited by HLA-G. These results emphasize the potential of therapeutically targeting the HLA-G/ILT2 checkpoint in HLA-G+ tumors, either concomitantly with anti–PD-1/PD-L1 or in cases of nonresponsiveness to anti–PD-1/PD-L1.

Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles.We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expertcurated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets. ICELLNET also revealed autocrine IL-10 as a switch to control human dendritic cell communication with up to 12 other cell types, four of which were experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from single or multiple cell-based transcriptomic profile(s). IntroductionCell-to-cell communication is at the basis of the higher order organization observed in tissues, organs, and organisms, at steady-state and in response to stress. It involves a "messenger" or "sender" cell, which transmits information signals to a "receiving" or "target" cell. Information is generally coded in the form of a chemical molecule that is sensed by the target cell through a cognate receptor.Multiple cells or cell types communicating with each other form cell communication networks.In mammalian organisms, endocrine communication involves cells that may be at very distant anatomical sites. However, cell communication also takes place locally through cell-to-cell contacts, or through inflammatory molecules. Cytokines and other mediators can be involved in distant as well as local communication 1-3 . Hence, when deciphering cell-to-cell communication, one should account for potential signals coming both from spatially proximal and distal cells.Most studies in the past decades have focused on a limited number of communication molecules in a given anatomical site or physiological process. The availability of large-scale transcriptomic datasets from several cell types, tissue locations, and cell activation states, opened the possibility of reconstructing cell-to-cell interactions based on the expression of specific ligand-receptor pairs on sender and target cells, respectively. Many of them exploit single cell RNAseq datasets to infer communication between groups of cells within the same dataset 4-7 . Despite leading to interesting and often innovative hypotheses 4,6,8 , these methods do not integrate putative signals that may come from more distant cells. Also, they cannot be applied to bulk transcriptomic data derived from a given cell population. Such datasets are numerous in public databases, and can be a source of novel insights into how...