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EOMES and T-BET are related T-box transcription factors that control natural killer (NK) cell development. Here we demonstrate that EOMES and T-BET regulate largely distinct gene sets during this process. EOMES is dominantly expressed in immature NK cells and drives early lineage specification by inducing hallmark receptors and functions. By contrast, T-BET is dominant in mature NK cells, where it induces responsiveness to IL-12 and represses the cell cycle, likely through transcriptional repressors. Regardless, many genes with distinct functions are co-regulated by the two transcription factors. By generating two gene-modified mice facilitating chromatin immunoprecipitation of endogenous EOMES and T-BET, we show a strong overlap in their DNA binding targets, as well as extensive epigenetic changes during NK cell differentiation. Our data thus suggest that EOMES and T-BET may distinctly govern, via differential expression and co-factors recruitment, NK cell maturation by inserting partially overlapping epigenetic regulations.

Recent evidence has elucidated how multipotent blood progenitors transform their identities in the thymus and undergo commitment to become T cells. Together with environmental signals, a core group of transcription factors have essential roles in this process by directly activating and repressing specific genes. Many of these transcription factors also function in later T cell development, but control different genes. Here, we review how these transcription factors work to change the activities of specific genomic loci during early intrathymic development to establish T cell lineage identity. We introduce the key regulators and highlight newly emergent insights into the rules that govern their actions. Whole-genome deep sequencing-based analysis has revealed unexpectedly rich relationships between inherited epigenetic states, transcription factor–DNA binding affinity thresholds and influences of given transcription factors on the activities of other factors in the same cells. Together, these mechanisms determine T cell identity and make the lineage choice irreversible.A transcription factor network triggered by Notch signalling in the thymus guides proliferating, multipotent progenitor cells into the T cell pathway. This Review describes how these factors work to establish regulatory target specificity, epigenomic impact and irreversibility for T cell identity.

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression. Cell type deconvolution in tumor spatial transcriptomics (ST) data remains challenging. Here, the authors develop Spatial Cellular Estimator for Tumors (SpaCET) to infer cell types and intercellular interactions from ST data in cancer across different platforms, with improved performance over similar methods.

Blood formation is believed to occur through stepwise progression of haematopoietic stem cells (HSCs) following a tree-like hierarchy of oligo-, bi- and unipotent progenitors. However, this model is based on the analysis of predefined flow-sorted cell populations. Here we integrated flow cytometric, transcriptomic and functional data at single-cell resolution to quantitatively map early differentiation of human HSCs towards lineage commitment. During homeostasis, individual HSCs gradually acquire lineage biases along multiple directions without passing through discrete hierarchically organized progenitor populations. Instead, unilineage-restricted cells emerge directly from a ‘continuum of low-primed undifferentiated haematopoietic stem and progenitor cells’ (CLOUD-HSPCs). Distinct gene expression modules operate in a combinatorial manner to control stemness, early lineage priming and the subsequent progression into all major branches of haematopoiesis. These data reveal a continuous landscape of human steady-state haematopoiesis downstream of HSCs and provide a basis for the understanding of haematopoietic malignancies. Velten et al.  use single-cell transcriptomics and functional data to map the early lineage commitment of human haematopoietic stem cells as a continuous process of cells passing through transitory states rather than demarcating discrete progenitors.

Hydra continually renews all cells in its body using three stem cell populations. This feature of Hydra allowed Siebert et al. to identify the transcriptional signatures of stem cells, progenitors, and terminally differentiated cells using single-cell RNA sequencing of adult Hydra (see the Perspective by Reddien). From these data, they built differentiation trajectories for all cell lineages, identified gene modules expressed along these trajectories, and identified putative regulators of genes within these modules. In addition, they identified candidate markers for elusive cell populations (such as multipotent stem cells and germline stem cells) and built a molecular map of the nervous system. Science , this issue p. eaav9314 ; see also p. 314 Single-cell sequencing of Hydra , a small freshwater cnidarian, uncovers gene expression underlying ongoing tissue homeostasis. The adult Hydra polyp continually renews all of its cells using three separate stem cell populations, but the genetic pathways enabling this homeostatic tissue maintenance are not well understood. We sequenced 24,985 Hydra single-cell transcriptomes and identified the molecular signatures of a broad spectrum of cell states, from stem cells to terminally differentiated cells. We constructed differentiation trajectories for each cell lineage and identified gene modules and putative regulators expressed along these trajectories, thus creating a comprehensive molecular map of all developmental lineages in the adult animal. In addition, we built a gene expression map of the Hydra nervous system. Our work constitutes a resource for addressing questions regarding the evolution of metazoan developmental processes and nervous system function.

A cell type's transcriptome defines the active genes that control its biology. Two groups used single-cell RNA sequencing to define the transcriptomes for essentially all cell types of a complete animal, the regenerative planarian Schmidtea mediterranea. Because pluripotent stem cells constantly differentiate to rejuvenate any part of the body of this species, all developmental lineages are active in adult animals. Fincher et al. determined the transcriptomes for most, if not all, planarian cell types, including some that were previously unknown. They also identified transition states and genes governing positional information. Plass et al. used single-cell transcriptomics and computational algorithms to reconstruct a lineage tree capturing the developmental progressions from stem to differentiated cells. They could then predict gene programs that are specifically turned on and off along the tree, and they used this approach to study how the cell types behaved during regeneration. These whole-animal transcriptome “atlases” are a powerful way to study metazoan biology. Science , this issue p. eaaq1736 , p. eaaq1723 Single-cell analysis reveals major planarian cell types, a differentiation tree, and genes for specific differentiation events. Flatworms of the species Schmidtea mediterranea are immortal—adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.

In adults, more than 300 billion blood cells are replenished daily. This output arises from a cellular hierarchy where stem cells differentiate into a series of multilineage progenitors, culminating in unilineage progenitors that generate over 10 different mature blood cell types. Notta et al. mapped the lineage potential of nearly 3000 single cells from 33 different cell populations of stem and progenitor cells from fetal liver, cord blood, and adult bone marrow (see the Perspective by Cabezas-Wallscheid and Trumpp). Prenatally, stem cell and progenitor populations were multilineage with few unilineage progenitors. In adults, multilineage cell potential was only seen in stem cell populations. Science , this issue p. 10.1126/science.aab2116 ; see also p. 126 As humans age, progenitor cells take over from stem cells the task of producing a steady supply of blood cells. [Also see Perspective by Cabezas-Wallscheid and Trumpp ] In a classical view of hematopoiesis, the various blood cell lineages arise via a hierarchical scheme starting with multipotent stem cells that become increasingly restricted in their differentiation potential through oligopotent and then unipotent progenitors. We developed a cell-sorting scheme to resolve myeloid (My), erythroid (Er), and megakaryocytic (Mk) fates from single CD34 + cells and then mapped the progenitor hierarchy across human development. Fetal liver contained large numbers of distinct oligopotent progenitors with intermingled My, Er, and Mk fates. However, few oligopotent progenitor intermediates were present in the adult bone marrow. Instead, only two progenitor classes predominate, multipotent and unipotent, with Er-Mk lineages emerging from multipotent cells. The developmental shift to an adult “two-tier” hierarchy challenges current dogma and provides a revised framework to understand normal and disease states of human hematopoiesis.

The derivation of induced pluripotent stem cells (iPSCs) over a decade ago sparked widespread enthusiasm for the development of new models of human disease, enhanced platforms for drug discovery and more widespread use of autologous cell-based therapy. Early studies using directed differentiation of iPSCs frequently uncovered cell-level phenotypes in monogenic diseases, but translation to tissue-level and organ-level diseases has required development of more complex, 3D, multicellular systems. Organoids and human–rodent chimaeras more accurately mirror the diverse cellular ecosystems of complex tissues and are being applied to iPSC disease models to recapitulate the pathobiology of a broad spectrum of human maladies, including infectious diseases, genetic disorders and cancer.Enthusiasm for patient-specific therapies based on induced pluripotent stem cells (iPSCs) has risen in parallel with rapid advances in genome editing. This Review summarizes the progress in iPSC-based disease modelling over the past decade, with a focus on 3D organoid systems and chimeric models being exploited for new therapeutic approaches.

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Cancers are not composed merely of cancer cells alone; instead, they are complex ‘ecosystems’ comprising many different cell types and noncellular factors. The tumour stroma is a critical component of the tumour microenvironment, where it has crucial roles in tumour initiation, progression, and metastasis. Most anticancer therapies target cancer cells specifically, but the tumour stroma can promote the resistance of cancer cells to such therapies, eventually resulting in fatal disease. Therefore, novel treatment strategies should combine anticancer and antistromal agents. Herein, we provide an overview of the advances in understanding the complex cancer cell–tumour stroma interactions and discuss how this knowledge can result in more effective therapeutic strategies, which might ultimately improve patient outcomes.