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Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord. Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries.

Targeting critical epigenetic regulators reverses aberrant transcription in cancer, thereby restoring normal tissue function 1 – 3 . The interaction of menin with lysine methyltransferase 2A (KMT2A), an epigenetic regulator, is a dependence in acute leukaemia caused by either rearrangement of KMT2A or mutation of the nucleophosmin 1 gene ( NPM1 ) 4 – 6 . KMT2A rearrangements occur in up to 10% of acute leukaemias and have an adverse prognosis, whereas NPM1 mutations occur in up to 30%, forming the most common genetic alteration in acute myeloid leukaemia 7 , 8 . Here, we describe the results of the first-in-human phase 1 clinical trial investigating revumenib (SNDX-5613), a potent and selective oral inhibitor of the menin–KMT2A interaction, in patients with relapsed or refractory acute leukaemia (ClinicalTrials.gov, NCT04065399). We show that therapy with revumenib was associated with a low frequency of grade 3 or higher treatment-related adverse events and a 30% rate of complete remission or complete remission with partial haematologic recovery (CR/CRh) in the efficacy analysis population. Asymptomatic prolongation of the QT interval on electrocardiography was identified as the only dose-limiting toxicity. Remissions occurred in leukaemias refractory to multiple previous lines of therapy. We demonstrate clearance of residual disease using sensitive clinical assays and identify hallmarks of differentiation into normal haematopoietic cells, including differentiation syndrome. These data establish menin inhibition as a therapeutic strategy for susceptible acute leukaemia subtypes. Revumenib, a potent and selective oral inhibitor of the menin–KMT2A interaction, is associated with a low frequency of treatment-related adverse events and promising clinical activity in patients with relapsed or refractory acute leukaemia.

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases 1 – 4 . Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types 5 , 6 . We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome. Combining CRISPRi-FlowFISH to perturb enhancers with an activity-by-contact model to predict complex connections allows systematic mapping of enhancer–gene connections in a given cell type, on the basis of chromatin-state measurements.

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data. A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

Astrocytes, a major cell type found throughout the central nervous system, have general roles in the modulation of synapse formation and synaptic transmission, blood–brain barrier formation, and regulation of blood flow, as well as metabolic support of other brain resident cells. Crucially, emerging evidence shows specific adaptations and astrocyte-encoded functions in regions, such as the spinal cord and cerebellum. To investigate the true extent of astrocyte molecular diversity across forebrain regions, we used single-cell RNA sequencing. Our analysis identifies five transcriptomically distinct astrocyte subtypes in adult mouse cortex and hippocampus. Validation of our data in situ reveals distinct spatial positioning of defined subtypes, reflecting the distribution of morphologically and physiologically distinct astrocyte populations. Our findings are evidence for specialized astrocyte subtypes between and within brain regions. The data are available through an online database ( https://holt-sc.glialab.org/ ), providing a resource on which to base explorations of local astrocyte diversity and function in the brain. Astrocytes are a major cell type in the central nervous system. Using single cell transcriptome sequencing, the authors identify multiple astrocyte subtypes in the adult mouse CNS, which map to distinct spatial locations and show correlations to cell morphology and physiology.

Although single-cell RNA sequencing studies have begun to provide compendia of cell expression profiles 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 – 9 , it has been difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here, using droplet- and plate-based single-cell RNA sequencing of approximately 75,000 human cells across all lung tissue compartments and circulating blood, combined with a multi-pronged cell annotation approach, we create an extensive cell atlas of the human lung. We define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 out of 45 previously known cell types and 14 previously unknown ones. This comprehensive molecular atlas identifies the biochemical functions of lung cells and the transcription factors and markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signalling interactions and immune cell homing; and identifies cell types that are directly affected by lung disease genes and respiratory viruses. By comparing human and mouse data, we identified 17 molecular cell types that have been gained or lost during lung evolution and others with substantially altered expression profiles, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions and interactions are achieved in development and tissue engineering and altered in disease and evolution. Expression profiling on 75,000 single cells creates a comprehensive cell atlas of the human lung that includes 41 out of 45 previously known cell types and 14 new ones.

Gastrulation is the fundamental process in all multicellular animals through which the basic body plan is first laid down 1 – 4 . It is pivotal in generating cellular diversity coordinated with spatial patterning. In humans, gastrulation occurs in the third week after fertilization. Our understanding of this process in humans is relatively limited and based primarily on historical specimens 5 – 8 , experimental models 9 – 12 or, more recently, in vitro cultured samples 13 – 16 . Here we characterize in a spatially resolved manner the single-cell transcriptional profile of an entire gastrulating human embryo, staged to be between 16 and 19 days after fertilization. We use these data to analyse the cell types present and to make comparisons with other model systems. In addition to pluripotent epiblast, we identified primordial germ cells, red blood cells and various mesodermal and endodermal cell types. This dataset offers a unique glimpse into a central but inaccessible stage of our development. This characterization provides new context for interpreting experiments in other model systems and represents a valuable resource for guiding directed differentiation of human cells in vitro. The single-cell transcriptional profile of a human embryo between 16 and 19 days after fertilization reveals parallels and differences in gastrulation in humans as compared with mouse and non-human primate models.

Itch triggers scratching, a behavioural defence mechanism that aids in the removal of harmful irritants and parasites 1 . Chemical itch is triggered by many endogenous and exogenous cues, such as pro-inflammatory histamine, which is released during an allergic reaction 1 . Mechanical itch can be triggered by light sensations such as wool fibres or a crawling insect 2 . In contrast to chemical itch pathways, which have been extensively studied, the mechanisms that underlie the transduction of mechanical itch are largely unknown. Here we show that the mechanically activated ion channel PIEZO1 (ref. 3 ) is selectively expressed by itch-specific sensory neurons and is required for their mechanically activated currents. Loss of PIEZO1 function in peripheral neurons greatly reduces mechanically evoked scratching behaviours and both acute and chronic itch-evoked sensitization. Finally, mice expressing a gain-of-function Piezo1 allele 4 exhibit enhanced mechanical itch behaviours. Our studies reveal the polymodal nature of itch sensory neurons and identify a role for PIEZO1 in the sensation of itch. Experiments in mice show that the mechanically activated ion channel PIEZO1 is expressed in itch-specific sensory neurons and has a role in transducing mechanical itch.

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens. Profiling of 53,193 individual epithelial cells from the mouse small intestine identifies previously unknown cell subtypes and corresponding gene markers, providing insight into gut homeostasis and response to pathogens. Surveying the stomach wall Intestinal epithelial cells can sense and respond to microbial stimuli to support their barrier function and coordinate appropriate immune responses, which range from tolerance to active immunity in cases of pathogen infection. In this study, Aviv Regev, Ramnik Xavier and colleagues used single-cell expression profiling to provide a comprehensive analysis of the epithelial cell composition of mouse small intestines when healthy and after infection, providing new markers, transcriptional programs and organizational principles of gut homeostasis and physiology.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV)-associated malignancy with a complex tumor ecosystem. How the interplay between tumor cells, EBV, and the microenvironment contributes to NPC progression and immune evasion remains unclear. Here we performed single-cell RNA sequencing on ~104,000 cells from 19 EBV + NPCs and 7 nonmalignant nasopharyngeal biopsies, simultaneously profiling the transcriptomes of malignant cells, EBV, stromal and immune cells. Overall, we identified global upregulation of interferon responses in the multicellular ecosystem of NPC. Notably, an epithelial–immune dual feature of malignant cells was discovered and associated with poor prognosis. Functional experiments revealed that tumor cells with this dual feature exhibited a higher capacity for tumorigenesis. Further characterization of the cellular components of the tumor microenvironment (TME) and their interactions with tumor cells revealed that the dual feature of tumor cells was positively correlated with the expression of co-inhibitory receptors on CD8 + tumor-infiltrating T cells. In addition, tumor cells with the dual feature were found to repress IFN-γ production by T cells, demonstrating their capacity for immune suppression. Our results provide new insights into the multicellular ecosystem of NPC and offer important clinical implications.