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Blood contains many types of cells, including many immune system components. Immune cells used to be characterized by marker-based assays, but now classification relies on the genes that cells express. Villani et al. used deep sequencing at the single-cell level and unbiased clustering to define six dendritic cell and four monocyte populations. This refined analysis has identified, among others, a previously unknown dendritic cell population that potently activates T cells. Further cell culture revealed possible differentiation progenitors within the different cell populations. Science , this issue p. eaah4573 Discovery of additional immune cell subtypes will help identify functions and immune monitoring during disease. Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C + subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease.

During early human pregnancy the uterine mucosa transforms into the decidua, into which the fetal placenta implants and where placental trophoblast cells intermingle and communicate with maternal cells. Trophoblast–decidual interactions underlie common diseases of pregnancy, including pre-eclampsia and stillbirth. Here we profile the transcriptomes of about 70,000 single cells from first-trimester placentas with matched maternal blood and decidual cells. The cellular composition of human decidua reveals subsets of perivascular and stromal cells that are located in distinct decidual layers. There are three major subsets of decidual natural killer cells that have distinctive immunomodulatory and chemokine profiles. We develop a repository of ligand–receptor complexes and a statistical tool to predict the cell-type specificity of cell–cell communication via these molecular interactions. Our data identify many regulatory interactions that prevent harmful innate or adaptive immune responses in this environment. Our single-cell atlas of the maternal–fetal interface reveals the cellular organization of the decidua and placenta, and the interactions that are critical for placentation and reproductive success. Transcriptomes of about 70,000 single cells from first-trimester deciduas and placentas reveal subsets of perivascular, stromal and natural killer cells in the decidua, with distinct immunomodulatory profiles that regulate the environment necessary for successful placentation.

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer. The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types. Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al. demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.

Immune-mediated inflammatory diseases (IMIDs) are typically characterised by relapsing and remitting flares of inflammation. However, the unpredictability of disease flares impedes their study. Addressing this critical knowledge gap, we use the experimental medicine approach of immunomodulatory drug withdrawal in rheumatoid arthritis (RA) remission to synchronise flare processes allowing detailed characterisation. Exploratory mass cytometry analyses reveal three circulating cellular subsets heralding the onset of arthritis flare – CD45RO + PD1 hi CD4 + and CD8 + T cells, and CD27 + CD86 + CD21 - B cells – further characterised by single-cell sequencing. Distinct lymphocyte subsets including cytotoxic and exhausted CD4 + memory T cells, memory CD8 + CXCR5 + T cells, and IGHA1 + plasma cells are primed for activation in flare patients. Regulatory memory CD4 + T cells (Treg cells) increase at flare onset, but with dysfunctional regulatory marker expression compared to drug-free remission. Significant clonal expansion is observed in T cells, but not B cells, after drug cessation; this is widespread throughout memory CD8 + T cell subsets but limited to the granzyme-expressing cytotoxic subset within CD4 + memory T cells. Based on our observations, we suggest a model of immune dysregulation for understanding RA flare, with potential for further translational research towards novel avenues for its treatment and prevention. Immune-mediated inflammatory diseases such as rheumatoid arthritis (RA) are characterised by relapsing-remitting flares, which are difficult to study due to their unpredictable nature. Here the authors use an experimental model of immunomodulatory drug cessation in RA patients combined with multi-omic analysis of circulating leukocytes to characterise the immune response for those with arthritis flare versus drug-free remission.

Healthy alveolar repair relies on the ability of alveolar stem cells to differentiate into specialized epithelial cells for gas exchange. In chronic fibrotic lung diseases such as idiopathic pulmonary fibrosis (IPF), this regenerative process is abnormal but the underlying mechanisms remain unclear. Here, using human lung tissue that represents different stages of disease and a 33-plex single-cell imaging mass cytometry (IMC), we present a high-resolution, temporo-spatial cell atlas of the regenerating alveolar niche. With unbiased mathematical methods which quantify statistically enriched interactions, CD206 hi macrophage subtype and an alveolar basal intermediate epithelial cell emerge as the most statistically robust spatial association in the epithelial and immune cell interactome, found across all stages of disease. Spatially resolved receptor–ligand analysis further offers an in silico mechanism by which these macrophages may influence epithelial regeneration. These findings provide a foundational step toward understanding immune–epithelial dynamics in aberrant alveolar regeneration in IPF. The cause of abnormal alveolar regeneration in idiopathic pulmonary fibrosis is unknown. Here, the authors generated a temporo-spatial cellular map of the regenerating niche and, using unbiased mathematical methods, identified a spatial interaction between a subset of macrophages and aberrant alveolar epithelial cells across all stages of the disease.

Mononuclear phagocytes (MPs) including monocytes, macrophages and dendritic cells (DCs) are critical innate immune effectors and initiators of the adaptive immune response. MPs are present in the alveolar airspace at steady state, however little is known about DC recruitment in acute pulmonary inflammation. Here we use lipopolysaccharide inhalation to induce acute inflammation in healthy volunteers and examine the impact on bronchoalveolar lavage fluid and blood MP repertoire. Classical monocytes and two DC subsets (DC2/3 and DC5) are expanded in bronchoalveolar lavage fluid 8 h after lipopolysaccharide inhalation. Surface phenotyping, gene expression profiling and parallel analysis of blood indicate recruited DCs are blood-derived. Recruited monocytes and DCs rapidly adopt typical airspace-resident MP gene expression profiles. Following lipopolysaccharide inhalation, alveolar macrophages strongly up-regulate cytokines for MP recruitment. Our study defines the characteristics of human DCs and monocytes recruited into bronchoalveolar space immediately following localised acute inflammatory stimulus in vivo. The diversity of human mononuclear phagocyte subsets remains to be characterized in many tissue-specific and functional contexts, including pulmonary inflammation. Here the authors characterize dendritic cell and monocyte subset recruitment to the bronchoalveolar space in a human LPS inhalation model.

Innate lymphoid cells (ILCs) play a key role in tissue-mediated immunity and can be controlled by coreceptor signaling. Here, we define a subset of ILCs that are Tbet⁺NK1.1⁻ and are present within the tumor microenvironment (TME). We show programmed death-1 receptor (PD-1) expression on ILCs within TME is found in Tbet⁺NK1.1⁻ ILCs. PD-1 significantly controlled the proliferation and function of Tbet⁺NK1.1⁻ ILCs in multiple murine and human tumors. We found tumor-derived lactate enhanced PD-1 expression on Tbet⁺NK1.1⁻ ILCs within the TME, which resulted in dampened the mammalian target of rapamycin (mTOR) signaling along with increased fatty acid uptake. In line with these metabolic changes, PD-1-deficient Tbet⁺NK1.1⁻ ILCs expressed significantly increased IFNγ and granzyme B and K. Furthermore, PD-1-deficient Tbet⁺NK1.1⁻ ILCs contributed toward diminished tumor growth in an experimental murine model of melanoma. These data demonstrate that PD-1 can regulate antitumor responses of Tbet⁺NK1.1⁻ ILCs within the TME.

The most common cause of death due to COVID-19 remains respiratory failure. Yet, our understanding of the precise cellular and molecular changes underlying lung alveolar damage is limited. Here, we integrate single cell transcriptomic data of COVID-19 and donor lung tissue with spatial transcriptomic data stratifying histopathological stages of diffuse alveolar damage. We identify changes in cellular composition across progressive damage, including waves of molecularly distinct macrophages and depletion of epithelial and endothelial populations. Predicted markers of pathological states identify immunoregulatory signatures, including IFN-alpha and metallothionein signatures in early damage, and fibrosis-related collagens in late damage. Furthermore, we predict a fibrinolytic shutdown via endothelial upregulation of SERPINE1 /PAI-1. Cell-cell interaction analysis revealed macrophage-derived SPP1 /osteopontin signalling as a key regulator during early steps of alveolar damage. These results provide a comprehensive, spatially resolved atlas of alveolar damage progression in COVID-19, highlighting the cellular mechanisms underlying pro-inflammatory and pro-fibrotic pathways in severe disease. Here the authors characterise the cellular and molecular progression of lung alveolar damage in severe COVID-19 patients using integrated histopathology and cell atlassing, pinpointing a role for macrophage SPP1 signalling to vasculature in this process.

During the activation of humoral immune responses, B cells acquire antigen for subsequent presentation to cognate T cells. Here we show that after mouse B cells accumulate antigen, it is maintained in a polarized distribution for extended periods in vivo. Using high-throughput imaging flow cytometry, we observed that this polarization is preserved during B cell division, promoting asymmetric antigen segregation among progeny. Antigen inheritance correlates with the ability of progeny to activate T cells: Daughter cells receiving larger antigen stores exhibit a prolonged capacity to present antigen, which renders them more effective in competing for T cell help. The generation of progeny with differential capacities for antigen presentation may have implications for somatic hypermutation and class switching during affinity maturation and as B cells commit to effector cell fates.