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The accumulation of senescent cells is a major cause of age-related inflammation and predisposes to a variety of age-related diseasessup1/sup. However, little is known about the molecular basis underlying this accumulation and its potential as a target to ameliorate the ageing process. Here we show that senescent cells heterogeneously express the immune checkpoint protein programmed death-ligand 1 (PD-L1) and that PD-L1sup+/supsenescent cells accumulate with age in vivo. PD-L1sup-/supcells are sensitive to T cell surveillance, whereas PD-L1sup+/supcells are resistant, even in the presence of senescence-associated secretory phenotypes (SASP). Single-cell analysis of p16sup+/supcells in vivo revealed that PD-L1 expression correlated with higher levels of SASP. Consistent with this, administration of programmed cell death protein 1 (PD-1) antibody to naturally ageing mice or a mouse model with normal livers or induced nonalcoholic steatohepatitis reduces the total number of p16sup+/supcells in vivo as well as the PD-L1sup+/suppopulation in an activated CD8sup+/supT cell-dependent manner, ameliorating various ageing-related phenotypes. These results suggest that the heterogeneous expression of PD-L1 has an important role in the accumulation of senescent cells and inflammation associated with ageing, and the elimination of PD-L1sup+/supsenescent cells by immune checkpoint blockade may be a promising strategy for anti-ageing therapy.

Single-cell RNA sequencing (scRNA-seq) is one of the most efficient technologies for human tumor research. However, data analysis is still faced with technical challenges, especially the difficulty in efficiently and accurately discriminating cancer/normal cells in the scRNA-seq expression matrix. If we can address these challenges, we can have a deeper understanding of the intratumoral and intertumoral heterogeneity. In this study, we developed a cancer/normal cell discrimination pipeline called pan-Cancer Seeker (CaSee) devoted to scRNA-seq expression matrix, which is based on the traditional high-quality pan-cancer bulk sequencing data using transfer learning. CaSee is the first tool directly used to discriminate cancer/normal cells in the scRNA-seq expression matrix, with much wider application fields and higher efficiency than copy number variation (CNV) method which requires corresponding reference cells. CaSee is user-friendly and can adapt to a variety of data sources, including but not limited to scRNA tissue sequencing data, scRNA cell line sequencing data, scRNA xenograft cell sequencing data and scRNA circulating tumor cell sequencing data. It is compatible with mainstream sequencing technology platforms, 10× Genomics Chromium, Smart-seq2, and Microwell-seq. Here, CaSee pipeline exhibited excellent performance in the multicenter data evaluation of 11 retrospective cohorts and one independent dataset, with an average discrimination accuracy of 96.69%. In general, the development of a deep-learning based, pan-cancer cell discrimination model, CaSee, to distinguish cancer cells from normal cells will be compelling to researchers working in the genomics, cancer, and single-cell fields.

Germinal centres are the engines of antibody evolution. Here, using human immunodeficiency virus (HIV) Env protein immunogen priming in rhesus monkeys followed by a long period without further immunization, we demonstrate germinal centre B (B

Severe SARS-CoV-2 infection1 has been associated with highly inflammatory immune activation since the earliest days of the COVID-19 pandemic2–5. More recently, these responses have been associated with the emergence of self-reactive antibodies with pathologic potential6–10, although their origins and resolution have remained unclear11. Previously, we and others have identified extrafollicular B cell activation, a pathway associated with the formation of new autoreactive antibodies in chronic autoimmunity12,13, as a dominant feature of severe and critical COVID-19 (refs. 14–18). Here, using single-cell B cell repertoire analysis of patients with mild and severe disease, we identify the expansion of a naive-derived, low-mutation IgG1 population of antibody-secreting cells (ASCs) reflecting features of low selective pressure. These features correlate with progressive, broad, clinically relevant autoreactivity, particularly directed against nuclear antigens and carbamylated proteins, emerging 10–15 days after the onset of symptoms. Detailed analysis of the low-selection compartment shows a high frequency of clonotypes specific for both SARS-CoV-2 and autoantigens, including pathogenic autoantibodies against the glomerular basement membrane. We further identify the contraction of this pathway on recovery, re-establishment of tolerance standards and concomitant loss of acute-derived ASCs irrespective of antigen specificity. However, serological autoreactivity persists in a subset of patients with postacute sequelae, raising important questions as to the contribution of emerging autoreactivity to continuing symptomology on recovery. In summary, this study demonstrates the origins, breadth and resolution of autoreactivity in severe COVID-19, with implications for early intervention and the treatment of patients with post-COVID sequelae.

Acquired resistance to inhibitors of anaplastic lymphoma kinase (ALK) is a major clinical challenge for ALK fusion-positive non-small-cell lung cancer (NSCLC). In the absence of secondary ALK mutations, epigenetic reprogramming is one of the main mechanisms of drug resistance, as it leads to phenotype switching that occurs during the epithelial-to-mesenchymal transition (EMT). Although drug-induced epigenetic reprogramming is believed to alter the sensitivity of cancer cells to anticancer treatments, there is still much to learn about overcoming drug resistance. In this study, we used an in vitro model of ceritinib-resistant NSCLC and employed genome-wide DNA methylation analysis in combination with single-cell (sc) RNA-seq to identify cytidine deaminase (CDA), a pyrimidine salvage pathway enzyme, as a candidate drug target. CDA was hypomethylated and upregulated in ceritinib-resistant cells. CDA-overexpressing cells were rarely but definitively detected in the naïve cell population by scRNA-seq, and their abundance was increased in the acquired-resistance population. Knockdown of CDA had antiproliferative effects on resistant cells and reversed the EMT phenotype. Treatment with epigenome-related nucleosides such as 5-formyl-2′-deoxycytidine selectively ablated CDA-overexpressing resistant cells via accumulation of DNA damage. Collectively, our data suggest that targeting CDA metabolism using epigenome-related nucleosides represents a potential new therapeutic strategy for overcoming ALK inhibitor resistance in NSCLC.

Rhabdomyosarcoma (RMS) is a common childhood cancer that shares features with developing skeletal muscle. Yet, the conservation of cellular hierarchy with human muscle development and the identification of molecularly defined tumor-propagating cells has not been reported. Using single-cell RNA-sequencing, DNA-barcode cell fate mapping and functional stem cell assays, we uncovered shared tumor cell hierarchies in RMS and human muscle development. We also identified common developmental stages at which tumor cells become arrested. Fusion-negative RMS cells resemble early myogenic cells found in embryonic and fetal development, while fusion-positive RMS cells express a highly specific gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. Fusion-positive RMS cells also have neural pathway-enriched states, suggesting less-rigid adherence to muscle-lineage hierarchies. Finally, we identified a molecularly defined tumor-propagating subpopulation in fusion-negative RMS that shares remarkable similarity to bi-potent, muscle mesenchyme progenitors that can make both muscle and osteogenic cells.

A surge in research has occurred because of current developments in single-cell technologies. Above all, single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) is a popular approach of analyzing chromatin accessibility differences at the level of single cell, either within or between groups. As a result, it is critical to examine cell heterogeneity at a previously unseen level and to identify both recognized and unknown cell types. However, with the ever-increasing number of cells engendered by technological development and the characteristics of the data, such as high noise, sparsity and dimension, challenges in distinguishing cell types have emerged. We propose scVAEBGM, which integrates a Variational Autoencoder (VAE) with a Bayesian Gaussian-mixture model (BGM) to process and analyze scATAC-seq data. This method combines and takes benefits of a Bayesian Gaussian mixture model to estimate the number of cell types without determining the cluster number in a beforehand. In other words, the size of the clusters is inferred from the data, thus avoiding biases introduced by subjective assessments when manually determining the size of the clusters. Additionally, the method is more robust to noise and can better represent single-cell data in lower dimensions. We also create a further clustering strategy. It is indicated by experiments that further clustering based on the already completed clustering can improve the clustering accuracy again. We test on six public datasets, and scVAEBGM outperforms various dimension reduction baselines. In downstream applications, scVAEBGM can reveal biological cell types.

The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.

Acknowledgements: This publication is part of the Human Cell Atlas. The authors thank the Sanger Cellular Generation and Phenotyping (CGaP) Core Facility and the Sanger Core Sequencing pipeline for support with sample processing and sequencing library preparation; A. Surani for supplying the TSC lines; H. Okae and T. Arima for sharing permission; R. Argelaguet, V. Kleshchevnikov, S. van Dongen, M. Prete and S. Murray for insightful comments and web portal support; T. Porter and the Cellular Genetics wet lab team for experimental support; A. Garcia for graphical images; and A. Maartens for editing. Placental material was provided by the Joint MRC–Human Cell Atlas (MR/S036350/1). The authors are grateful to patients for donating tissue for research. We thank D. Moore and M. Maquinana and staff at Addenbrooke’s Hospital, Cambridge, UK. Supported by Wellcome Sanger core funding (WT206194 and 220540/Z/20/A) and the Wellcome Trust grant ‘Wellcome Strategic Support Science award’ (grant no. 211276/Z/18/Z). M.Y.T. held the Royal Society Dorothy Hodgkin Fellowship (DH160216) and Research Grant for Research Fellows (RGF\R1\180028) during this study and is also supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (Grant agreement 853546). A.M. is in receipt of a Wellcome Trust Investigator Award (200841/Z/16/Z)., The relationship between the human placenta-the extraembryonic organ made by the fetus, and the decidua-the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) derived from placental villi infiltrate the decidua, transforming the maternal arteries into high-conductance vessels1. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia2. Here we have generated a spatially resolved multiomics single-cell atlas of the entire human maternal-fetal interface including the myometrium, which enables us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to infer the possible transcription factors mediating EVT invasion and show that they are preserved in in vitro models of EVT differentiation from primary trophoblast organoids3,4 and trophoblast stem cells5. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We predict the cell-cell communication events contributing to trophoblast invasion and placental bed giant cell formation, and model the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provide a comprehensive analysis of postimplantation trophoblast differentiation that can be used to inform the design of experimental models of the human placenta in early pregnancy.

Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.

Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)-combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level-from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to single-cell RNA sequencing data across individuals or regions, DIALOGUE uncovered MCPs marking Alzheimer's disease, ulcerative colitis and resistance to cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies. DIALOGUE enables the analysis of multicellular regulation in health and disease.

Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.

Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.

Although lung cancer risk among smokers is dependent on smoking dose, it remains unknown if this increased risk reflects an increased rate of somatic mutation accumulation in normal lung cells. Here, we applied single-cell whole-genome sequencing of proximal bronchial basal cells from 33 participants aged between 11 and 86 years with smoking histories varying from never-smoking to 116 pack-years. We found an increase in the frequency of single-nucleotide variants and small insertions and deletions with chronological age in never-smokers, with mutation frequencies significantly elevated among smokers. When plotted against smoking pack-years, mutations followed the linear increase in cancer risk until about 23 pack-years, after which no further increase in mutation frequency was observed, pointing toward individual selection for mutation avoidance. Known lung cancer-defined mutation signatures tracked with both age and smoking. No significant enrichment for somatic mutations in lung cancer driver genes was observed.

The ability to slow or reverse biological ageing would have major implications for mitigating disease risk and maintaining vitality1. Although an increasing number of interventions show promise for rejuvenation2, their effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. Here we performed single-cell RNA sequencing on 20 organs to reveal cell-type-specific responses to young and aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, haematopoietic stem cells and hepatocytes are among those cell types that are especially responsive. On the pathway level, young blood invokes new gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. We observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it in select cell types. Together, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity.

The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (scRNA-seq and snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate normal gene expression count matrices.

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-γ(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.

Genetic risk for autism spectrum disorder (ASD) is associated with hundreds of genes spanning a wide range of biological functions

Labs reaping information from single cells balance desirables with headaches such as data sparsity, cost and protocol surprises.

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

Single-cell multiomics data continues to grow at an unprecedented pace. Although several methods have demonstrated promising results in integrating several data modalities from the same tissue, the complexity and scale of data compositions present in cell atlases still pose a challenge. Here, we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.

Cell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and is a crucial component of single-cell clustering pipelines. However, most current single-cell feature selection methods are univariable filter methods ignoring gene dependency. Even the multivariable filter methods developed in recent years only consider "one-to-many" relationship between genes. In this paper, a novel single-cell feature selection method based on convex analysis of mixtures (FSCAM) is proposed, which takes into account "many-to-many" relationship. Compared to the previous "one-to-many" methods, FSCAM selects genes with a combination of relevancy, redundancy and completeness. Pertinent benchmarking is conducted on the real datasets to validate the superiority of FSCAM. Through plugging into the framework of partition around medoids (PAM) clustering, a single-cell clustering algorithm based on FSCAM method (SCC_FSCAM) is further developed. Comparing SCC_FSCAM with existing advanced clustering algorithms, the results show that our algorithm has advantages in both internal criteria (clustering number) and external criteria (adjusted Rand index) and has a good stability.

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

The skin serves as a physical barrier and an immunological interface that protects the body from the external environment

Local circuit architecture facilitates the emergence of feature selectivity in the cerebral cortex1. In the hippocampus, it remains unknown whether local computations supported by specific connectivity motifs2 regulate the spatial receptive fields of pyramidal cells3. Here we developed an in vivo electroporation method for monosynaptic retrograde tracing4 and optogenetics manipulation at single-cell resolution to interrogate the dynamic interaction of place cells with their microcircuitry during navigation. We found a local circuit mechanism in CA1 whereby the spatial tuning of an individual place cell can propagate to a functionally recurrent subnetwork5 to which it belongs. The emergence of place fields in individual neurons led to the development of inverse selectivity in a subset of their presynaptic interneurons, and recruited functionally coupled place cells at that location. Thus, the spatial selectivity of single CA1 neurons is amplified through local circuit plasticity to enable effective multi-neuronal representations that can flexibly scale environmental features locally without degrading the feedforward input structure. Single-cell tracing and optogenetics manipulation in mice are used to show how spatial tuning of individual pyramidal cells in CA1 can propagate to and be amplified by their local subnetwork of neurons.

Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84–93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division. SMALT, a base-editing lineage-tracing method, enables whole-organism phylogenetic analyses at single-cell resolution.

Gastrulation is the fundamental process in all multicellular animals through which the basic body plan is first laid down1–4. 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 specimens5–8, experimental models9–12 or, more recently, in vitro cultured samples13–16. 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.

Although critical for host defense, innate immune cells are also pathologic drivers of acute respiratory distress syndrome (ARDS). Innate immune dynamics during Coronavirus Disease 2019 (COVID-19) ARDS, compared to ARDS from other respiratory pathogens, is unclear. Moreover, mechanisms underlying the beneficial effects of dexamethasone during severe COVID-19 remain elusive. Using single-cell RNA sequencing and plasma proteomics, we discovered that, compared to bacterial ARDS, COVID-19 was associated with expansion of distinct neutrophil states characterized by interferon (IFN) and prostaglandin signaling. Dexamethasone during severe COVID-19 affected circulating neutrophils, altered IFNactive neutrophils, downregulated interferon-stimulated genes and activated IL-1R2+ neutrophils. Dexamethasone also expanded immunosuppressive immature neutrophils and remodeled cellular interactions by changing neutrophils from information receivers into information providers. Male patients had higher proportions of IFNactive neutrophils and preferential steroid-induced immature neutrophil expansion, potentially affecting outcomes. Our single-cell atlas (see ‘Data availability’ section) defines COVID-19-enriched neutrophil states and molecular mechanisms of dexamethasone action to develop targeted immunotherapies for severe COVID-19., New results shed light on the molecular mechanisms of dexamethasone action, underlying its therapeutic benefit in patients with severe COVID-19.

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body while at the same time raising the need to formalize this new knowledge. Here, we discuss current efforts to harmonize and integrate different sources of annotations of cell types and states into a reference cell ontology. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains.

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture., Multi-modal analysis is used to generate a 3D atlas of the upper limb area of the mouse primary motor cortex, providing a framework for future studies of motor control circuitry.

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa.

Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. As a result, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

Cervical cancer (CC) is the most common gynecological malignancy, whose cellular heterogeneity has not been fully understood. Here, we performed single-cell RNA sequencing (scRNA-seq) to survey the transcriptomes of 57,669 cells derived from three CC tumors with paired normal adjacent non-tumor (NAT) samples. Single-cell transcriptomics analysis revealed extensive heterogeneity in malignant cells of human CCs, wherein epithelial subpopulation exhibited different genomic and transcriptomic signatures. We also identified cancer-associated fibroblasts (CAFs) that may promote tumor progression of CC, and further distinguished inflammatory CAF (iCAF) and myofibroblastic CAF (myCAF). CD8+ T cell diversity revealed both proliferative (MKI67+) and non-cycling exhausted (PDCD1+) subpopulations at the end of the trajectory path. We used the epithelial signature genes derived from scRNA-seq to deconvolute bulk RNA-seq data of CC, identifying four different CC subtypes, namely hypoxia (S-H subtype), proliferation (S-P subtype), differentiation (S-D subtype), and immunoactive (S-I subtype) subtype. The S-H subtype showed the worst prognosis, while CC patients of the S-I subtype had the longest overall survival time. Our results lay the foundation for precision prognostic and therapeutic stratification of CC.

Background Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird’s eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. Results A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard maximum mean discrepancy measure. Conclusion The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect.

Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.

BackgroundThe rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.ResultsscGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample sizeN≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.ConclusionsWe showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Global alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory trees, revealing various gene expression dynamics including gene–gene correlation conservation between different species.

Transposable Elements (TEs) contribute to the repetitive fraction in almost every eukaryotic genome known to date, and their transcriptional activation can influence the expression of neighboring genes in healthy and disease states. Single cell RNA-Seq (scRNA-Seq) is a technical advance that allows the study of gene expression on a cell-by-cell basis. Although a current computational approach is available for the single cell analysis of TE expression, it omits their genomic location. Here we show SoloTE, a pipeline that outperforms the previous approach in terms of computational resources and by allowing the inclusion of locus-specific TE activity in scRNA-Seq expression matrixes. We then apply SoloTE to several datasets to reveal the repertoire of TEs that become transcriptionally active in different cell groups, and based on their genomic location, we predict their potential impact on gene expression. As our tool takes as input the resulting files from standard scRNA-Seq processing pipelines, we expect it to be widely adopted in single cell studies to help researchers discover patterns of cellular diversity associated with TE expression.

Developmental thymic waves of innate-like and adaptive-like γδ T cells have been described, but the current understanding of γδ T cell development is mainly limited to mouse models. Here, we combine single cell (sc) RNA gene expression and sc γδ T cell receptor (TCR) sequencing on fetal and pediatric γδ thymocytes in order to understand the ontogeny of human γδ T cells. Mature fetal γδ thymocytes (both the Vγ9Vδ2 and nonVγ9Vδ2 subsets) are committed to either a type 1, a type 3 or a type 2-like effector fate displaying a wave-like pattern depending on gestation age, and are enriched for public CDR3 features upon maturation. Strikingly, these effector modules express different CDR3 sequences and follow distinct developmental trajectories. In contrast, the pediatric thymus generates only a small effector subset that is highly biased towards Vγ9Vδ2 TCR usage and shows a mixed type 1/type 3 effector profile. Thus, our combined dataset of gene expression and detailed TCR information at the single-cell level identifies distinct functional thymic programming of γδ T cell immunity in human.

Organoids are biomimetic tissue models comprising multiple cell types and cell states. Post-translational modification (PTM) signaling networks control cellular phenotypes and are frequently dysregulated in diseases such as cancer. Although signaling networks vary across cell types, there are limited techniques to study cell type-specific PTMs in heterocellular organoids. Here, we present a multiplexed mass cytometry (MC) protocol for single-cell analysis of PTM signaling and cell states in organoids and organoids co-cultured with fibroblasts and leukocytes. We describe how thiol-reactive organoid barcoding in situ (TOBis) enables 35-plex and 126-plex single-cell comparison of organoid cultures and provide a cytometry by time of flight (CyTOF) signaling analysis pipeline (CyGNAL) for computing cell type-specific PTM signaling networks. The TOBis MC protocol takes ~3 d from organoid fixation to data acquisition and can generate single-cell data for >40 antibodies from millions of cells across 126 organoid cultures in a single MC run.

The aorta contains numerous cell types that contribute to vascular inflammation and thus the progression of aortic diseases. However, the heterogeneity and cellular composition of the ascending aorta in the setting of a high-fat diet (HFD) have not been fully assessed. We performed single-cell RNA sequencing on ascending aortas from mice fed a normal diet and mice fed a HFD. Unsupervised cluster analysis of the transcriptional profiles from 24,001 aortic cells identified 27 clusters representing 10 cell types: endothelial cells (ECs), fibroblasts, vascular smooth muscle cells (SMCs), immune cells (B cells, T cells, macrophages, and dendritic cells), mesothelial cells, pericytes, and neural cells. After HFD intake, subpopulations of endothelial cells with lipid transport and angiogenesis capacity and extensive expression of contractile genes were defined. In the HFD group, three major SMC subpopulations showed increased expression of extracellular matrix-degradation genes, and a synthetic SMC subcluster was proportionally increased. This increase was accompanied by upregulation of proinflammatory genes. Under HFD conditions, aortic-resident macrophage numbers were increased, and blood-derived macrophages showed the strongest expression of proinflammatory cytokines. Our study elucidates the nature and range of the cellular composition of the ascending aorta and increases understanding of the development and progression of aortic inflammatory disease., Cardiovascular disease: High-fat diets cause changes to aorta cells RNA sequencing of aorta cells reveals how key changes in cell populations and gene expression caused by a high-fat diet can increase risk of cardiovascular diseases. Xin Ma and co-workers at Jiangnan University in Wuxi, China, performed single-cell sequencing on over 24,000 cells from the ascending aorta of mice fed either high-fat or normal diets. The resulting dataset of around 1,700 genes and 4,000 unique molecular identifiers reveals numerous fat-induced differences across the ten main aortic cell types. The high-fat diet extended specific sub-populations of endothelial cells that enhance lipid transport, while muscle cells showed increased expression of genes that degrade the extracellular matrix. Other fat-induced differences, including increased populations of immune cells known as macrophages and a sub-cluster of muscle cells that up-regulate pro-inflammatory genes, may act to promote excessive immune responses in obese individuals.

An explosion in single cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single cell techniques have been extended to multi-omics approaches, and now enable the simultaneous measurement of data modalities and cellular spatial context. Data are now available for millions of cells, for whole-genome measurements, and for multiple modalities. Although analyses of such multimodal datasets have potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single cell data set. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell type classification, regulatory network modeling, and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multiome data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell–cell interactions and intrinsic genetic programs that result in probably more than a thousand distinct cell types. A complete understanding of this process requires a systematic characterization of cell states over the entire spatiotemporal range of brain development. The ability of single-cell RNA sequencing and spatial transcriptomics to reveal the molecular heterogeneity of complex tissues has therefore been particularly powerful in the nervous system. Previous studies have explored development in specific brain regions1–8, the whole adult brain9 and even entire embryos10. Here we report a comprehensive single-cell transcriptomic atlas of the embryonic mouse brain between gastrulation and birth. We identified almost eight hundred cellular states that describe a developmental program for the functional elements of the brain and its enclosing membranes, including the early neuroepithelium, region-specific secondary organizers, and both neurogenic and gliogenic progenitors. We also used in situ mRNA sequencing to map the spatial expression patterns of key developmental genes. Integrating the in situ data with our single-cell clusters revealed the precise spatial organization of neural progenitors during the patterning of the nervous system. A comprehensive single-cell transcriptomic atlas of the mouse brain between gastrulation and birth identifies hundreds of cellular states and reveals the spatiotemporal organization of brain development.

The kidney glomerulus is essential for proper kidney function. Until recently, technical challenges associated with glomerular isolation and subsequent dissolution into single cells have limited the detailed characterization of cells in the glomerulus. Previous techniques of kidney dissociation result in low glomerular cell yield, which limits high-throughput analysis. The ability to efficiently purify glomeruli and digest the tissue into single cells is especially important for single-cell characterization methods. Here, we present a detailed and comprehensive technique for the extraction and preparation of mouse glomerular cells, with high yield and viability. The method includes direct renal perfusion of Dynabeads via the renal artery followed by kidney dissociation and isolation of glomeruli by magnet; these steps provide a high number and purity of isolated glomeruli, which are further dissociated into single cells. The balanced representation of podocytes, mesangial and endothelial cells in single-cell suspensions of mouse glomeruli, and the high cell viability observed, confirm the efficiency of our method. With some practice, the procedure can be done in