Charles-Antoine Dutertre

Bio: Charles-Antoine Dutertre is an academic researcher from Institut Gustave Roussy. The author has contributed to research in topics: Immune system & Biology. The author has an hindex of 28, co-authored 67 publications receiving 5589 citations. Previous affiliations of Charles-Antoine Dutertre include National University of Singapore & Centre national de la recherche scientifique.

Dimensionality reduction for visualizing single-cell data using UMAP.

Abstract: Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

The XC chemokine receptor 1 is a conserved selective marker of mammalian cells homologous to mouse CD8α+ dendritic cells

Abstract: Human BDCA3+ dendritic cells (DCs) were suggested to be homologous to mouse CD8alpha+ DCs. We demonstrate that human BDCA3+ DCs are more efficient than their BDCA1+ counterparts or plasmacytoid DCs (pDCs) in cross-presenting antigen and activating CD8+ T cells, which is similar to mouse CD8alpha+ DCs as compared with CD11b+ DCs or pDCs, although with more moderate differences between human DC subsets. Yet, no specific marker was known to be shared between homologous DC subsets across species. We found that XC chemokine receptor 1 (XCR1) is specifically expressed and active in mouse CD8alpha+, human BDCA3+, and sheep CD26+ DCs and is conserved across species. The mRNA encoding the XCR1 ligand chemokine (C motif) ligand 1 (XCL1) is selectively expressed in natural killer (NK) and CD8+ T lymphocytes at steady-state and is enhanced upon activation. Moreover, the Xcl1 mRNA is selectively expressed at high levels in central memory compared with naive CD8+ T lymphocytes. Finally, XCR1-/- mice have decreased early CD8+ T cell responses to Listeria monocytogenes infection, which is associated with higher bacterial loads early in infection. Therefore, XCR1 constitutes the first conserved specific marker for cell subsets homologous to mouse CD8alpha+ DCs in higher vertebrates and promotes their ability to activate early CD8+ T cell defenses against an intracellular pathogenic bacteria.

Mapping the human DC lineage through the integration of high-dimensional techniques

Abstract: INTRODUCTION Dendritic cells (DC) are professional antigen-presenting cells that orchestrate immune responses. The human DC population comprises multiple subsets, including plasmacytoid DC (pDC) and two functionally specialized lineages of conventional DC (cDC1 and cDC2), whose origins and differentiation pathways remain incompletely defined. RATIONALE As DC are essential regulators of the immune response in health and disease, potential intervention strategies aiming at manipulation of these cells will require in-depth insights of their origins, the mechanisms that govern their homeostasis, and their functional properties. Here, we employed two unbiased high-dimensional technologies to characterize the human DC lineage from bone marrow to blood. RESULTS We isolated the DC-containing population (Lineage − HLA − DR + CD135 + cells) from human blood and defined the transcriptomes of 710 individual cells using massively parallel single-cell mRNA sequencing. By combining complementary bioinformatic approaches, we identified a small cluster of cells within this population as putative DC precursors (pre-DC). We then confirmed this finding using cytometry by time-of-flight (CyTOF) to simultaneously measure the expression of a panel of 38 different proteins at the single-cell level on Lineage − HLA − DR + cells and found that pre-DC possessed a CD123 + CD33 + CD45RA + phenotype. We confirmed the precursor potential of pre-DC by establishing their potential to differentiate in vitro into cDC1 and cDC2, but not pDC, in the known proportions found in vivo . Interestingly, pre-DC also express classical pDC markers, including CD123, CD303, and CD304. Thus, any previous studies using these markers to identify or isolate pDC will have inadvertently included CD123 + CD33 + pre-DC. We provide here new markers that can be used to identify unambiguously pre-DC from pDC, including CD33, CX3CR1, CD2, CD5, and CD327. When CD123 + CD33 + pre-DC and CD123 + CD33 − pDC were isolated separately, we observed that pre-DC have unique functional properties that were previously attributed to pDC. Although pDC remain bona fide interferon-α–producing cells, their reported interleukin-12 (IL-12) production and CD4 T cell allostimulatory capacity can likely be attributed to “contaminating” pre-DC. We then asked whether the pre-DC population contained both uncommitted and committed pre-cDC1 and pre-cDC2 precursors, as recently shown in mice. Using microfluidic single-cell mRNA sequencing (scmRNAseq), we showed that the human pre-DC population contains cells exhibiting transcriptomic priming toward cDC1 and cDC2 lineages. Flow cytometry and in vitro DC differentiation experiments further identified CD123 + CADM1 − CD1c − putative uncommitted pre-DC, alongside CADM1 + CD1c − pre-cDC1 and CADM1 − CD1c + pre-cDC2. Finally, we found that pre-DC subsets expressed T cell costimulatory molecules and induced comparable proliferation and polarization of naive CD4 T cells as adult DC. However, exposure to the Toll-like receptor 9 (TLR9) ligand CpG triggered IL-12p40 and tumor necrosis factor–α production by early pre-DC, pre-cDC1, and pre-cDC2, in contrast to differentiated cDC1 and cDC2, which do not express TLR9. CONCLUSION Using unsupervised scmRNAseq and CyTOF analyses, we have unraveled the complexity of the human DC lineage at the single-cell level, revealing a continuous process of differentiation that starts in the bone marrow (BM) with common DC progenitors (CDP), diverges at the point of emergence of pre-DC and pDC potential, and culminates in maturation of both lineages in the blood and spleen. The pre-DC compartment contains functionally and phenotypically distinct lineage-committed subpopulations, including one early uncommitted CD123 + pre-DC subset and two CD45RA + CD123 lo lineage-committed subsets. The discovery of multiple committed pre-DC populations with unique capabilities opens promising new avenues for the therapeutic exploitation of DC subset-specific targeting.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Abstract: UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology The result is a practical scalable algorithm that applies to real world data The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning

Integrated analysis of multimodal single-cell data

Abstract: The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

Fast, sensitive and accurate integration of single-cell data with Harmony.

Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony ( https://github.com/immunogenomics/harmony ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.