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The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic, receptor-ligand interactions and adaptive-like responses to viral infections. In the present study, we generated a single-cell transcriptional reference map of healthy human blood- and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene-regulatory networks defining NK cell differentiation. Transfer learning facilitated incorporation of tumor-infiltrating NK cell transcriptomes (39 datasets, 7 solid tumors, 427 patients) into the reference map to analyze tumor microenvironment (TME)-induced perturbations. Of the six functionally distinct NK cell states identified, a dysfunctional stressed CD56 bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant effector CD56 dim state were commonly enriched across tumor types, the ratio of which was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of new NK cell therapies and can be extended through transfer learning to interrogate new datasets from experimental perturbations or disease conditions., (© 2024. The Author(s).)

Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses., (© 2024. The Author(s).)

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues., Competing Interests: Declaration of interests S.A.T. is a remunerated member of the Scientific Advisory Board, Foresite Labs, and Qiagen. She is co-founder and equity holder of Transition Bio., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets.

The development of single-cell and spatial transcriptomics methods was instrumental in the conception of the Human Cell Atlas initiative, which aims to generate an integrated map of all cells across the human body. These technology advances are bringing increasing depth and resolution to maps of human organs and tissues, as well as our understanding of individual human cell types. Commonalities as well as tissue-specific features of primary and supportive cell types across human organs are beginning to emerge from these human tissue maps. In this Review, we highlight key biological insights obtained from cross-tissue studies into epithelial, fibroblast, vascular and immune cells based on single-cell gene expression data in humans and contrast it with mechanisms reported in mice., (© 2022. Springer Nature Limited.)

Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.

KMT2A-rearranged infant ALL is an aggressive childhood leukemia with poor prognosis. Here, we investigated the developmental state of KMT2A-rearranged infant B-cell acute lymphoblastic leukemia (B-ALL) using bulk messenger RNA (mRNA) meta-analysis and examination of single lymphoblast transcriptomes against a developing bone marrow reference. KMT2A-rearranged infant B-ALL was uniquely dominated by an early lymphocyte precursor (ELP) state, whereas less adverse NUTM1-rearranged infant ALL demonstrated signals of later developing B cells, in line with most other childhood B-ALLs. We compared infant lymphoblasts with ELP cells and revealed that the cancer harbored hybrid myeloid-lymphoid features, including nonphysiological antigen combinations potentially targetable to achieve cancer specificity. We validated surface coexpression of exemplar combinations by flow cytometry. Through analysis of shared mutations in separate leukemias from a child with infant KMT2A-rearranged B-ALL relapsing as AML, we established that KMT2A rearrangement occurred in very early development, before hematopoietic specification, emphasizing that cell of origin cannot be inferred from the transcriptional state., (© 2022. The Author(s).)

The intestinal immune system represents the largest collection of immune cells in the body and is continually exposed to antigens from food and the microbiota. Here we discuss the contribution of single-cell transcriptomics in shaping our understanding of this complex system. We consider the impact on resolving early intestine development, engagement with the neighbouring microbiota, diversity of intestinal immune cells, compartmentalisation within the intestines and interactions with non-immune cells. Finally, we offer a perspective on open questions about gut immunity that evolving single-cell technologies are well placed to address., (© 2021. The Author(s).)

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics., (© 2021. The Author(s).)

Tumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference "cellular signals" in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of "fetalness" with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16 + C1QA/B/C + ) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34 + hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8 + T cells and an increased ratio of CD8 + effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

Human gut development requires the orchestrated interaction of differentiating cell types. Here, we generate an in-depth single-cell map of the developing human intestine at 6-10 weeks post-conception. Our analysis reveals the transcriptional profile of cycling epithelial precursor cells; distinct from LGR5-expressing cells. We propose that these cells may contribute to differentiated cell subsets via the generation of LGR5-expressing stem cells and receive signals from surrounding mesenchymal cells. Furthermore, we draw parallels between the transcriptomes of ex vivo tissues and in vitro fetal organoids, revealing the maturation of organoid cultures in a dish. Lastly, we compare scRNA-seq profiles from pediatric Crohn's disease epithelium alongside matched healthy controls to reveal disease-associated changes in the epithelial composition. Contrasting these with the fetal profiles reveals the re-activation of fetal transcription factors in Crohn's disease. Our study provides a resource available at www.gutcellatlas.org, and underscores the importance of unraveling fetal development in understanding disease., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The dynamics of CD4 + T cell memory development remain to be examined at genome scale. In malaria-endemic regions, antimalarial chemoprevention protects long after its cessation and associates with effects on CD4 + T cells. We applied single-cell RNA sequencing and computational modelling to track memory development during Plasmodium infection and treatment. In the absence of central memory precursors, two trajectories developed as T helper 1 (T H 1) and follicular helper T (T FH ) transcriptomes contracted and partially coalesced over three weeks. Progeny of single clones populated T H 1 and T FH trajectories, and fate-mapping suggested that there was minimal lineage plasticity. Relationships between T FH and central memory were revealed, with antimalarials modulating these responses and boosting T H 1 recall. Finally, single-cell epigenomics confirmed that heterogeneity among effectors was partially reset in memory. Thus, the effector-to-memory transition in CD4 + T cells is gradual during malaria and is modulated by antiparasitic drugs. Graphical user interfaces are presented for examining gene-expression dynamics and gene-gene correlations ( http://haquelab.mdhs.unimelb.edu.au/cd4_memory/ ).

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require a deeper understanding of the molecular processes involved in the healthy heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavour. Here, using state-of-the-art analyses of large-scale single-cell and single-nucleus transcriptomes, we characterize six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, and reveal distinct atrial and ventricular subsets of cells with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment, we identify cardiac-resident macrophages with inflammatory and protective transcriptional signatures. Furthermore, analyses of cell-to-cell interactions highlight different networks of macrophages, fibroblasts and cardiomyocytes between atria and ventricles that are distinct from those of skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a valuable reference for future studies.

Acute gastrointestinal (GI) graft-versus-host disease (GVHD) is a primary determinant of mortality after allogeneic hematopoietic stem cell transplantation (alloSCT). The condition is mediated by alloreactive donor CD4+ T cells that differentiate into pathogenic subsets expressing IFN-γ, IL-17A, or GM-CSF and is regulated by subsets expressing IL-10 and/or Foxp3. Developmental relationships between Th cell states during priming in mesenteric lymph nodes (mLNs) and effector function in the GI tract remain undefined at genome scale. We applied scRNA-Seq and computational modeling to a mouse model of donor DC-mediated GVHD exacerbation, creating an atlas of putative CD4+ T cell differentiation pathways in vivo. Computational trajectory inference suggested emergence of pathogenic and regulatory states along a single developmental trajectory in mLNs. Importantly, we inferred an unexpected second trajectory, categorized by little proliferation or cytokine expression, reduced glycolysis, and high tcf7 expression. TCF1hi cells upregulated α4β7 before gut migration and failed to express cytokines. These cells exhibited recall potential and plasticity following secondary transplantation, including cytokine or Foxp3 expression, but reduced T cell factor 1 (TCF1). Thus, scRNA-Seq suggested divergence of alloreactive CD4+ T cells into quiescent and effector states during gut GVHD exacerbation by donor DC, reflecting putative heterogeneous priming in vivo. These findings, which are potentially the first at a single-cell level during GVHD over time, may assist in examination of T cell differentiation in patients undergoing alloSCT.

Cell-cell communication mediated by ligand-receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions. In contrast to other repositories, our database takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, provide procedures for inferring cell-cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads.

Motivation: Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods., Availability and Implementation: BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development.

Iterative liver injury results in progressive fibrosis disrupting hepatic architecture, regeneration potential, and liver function. Hepatic stellate cells (HSCs) are a major source of pathological matrix during fibrosis and are thought to be a functionally homogeneous population. Here, we use single-cell RNA sequencing to deconvolve the hepatic mesenchyme in healthy and fibrotic mouse liver, revealing spatial zonation of HSCs across the hepatic lobule. Furthermore, we show that HSCs partition into topographically diametric lobule regions, designated portal vein-associated HSCs (PaHSCs) and central vein-associated HSCs (CaHSCs). Importantly we uncover functional zonation, identifying CaHSCs as the dominant pathogenic collagen-producing cells in a mouse model of centrilobular fibrosis. Finally, we identify LPAR1 as a therapeutic target on collagen-producing CaHSCs, demonstrating that blockade of LPAR1 inhibits liver fibrosis in a rodent NASH model. Taken together, our work illustrates the power of single-cell transcriptomics to resolve the key collagen-producing cells driving liver fibrosis with high precision., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Non-lymphoid tissues (NLTs) harbor a pool of adaptive immune cells with largely unexplored phenotype and development. We used single-cell RNA-seq to characterize 35,000 CD4 + regulatory (Treg) and memory (Tmem) T cells in mouse skin and colon, their respective draining lymph nodes (LNs) and spleen. In these tissues, we identified Treg cell subpopulations with distinct degrees of NLT phenotype. Subpopulation pseudotime ordering and gene kinetics were consistent in recruitment to skin and colon, yet the initial NLT-priming in LNs and the final stages of NLT functional adaptation reflected tissue-specific differences. Predicted kinetics were recapitulated using an in vivo melanoma-induction model, validating key regulators and receptors. Finally, we profiled human blood and NLT Treg and Tmem cells, and identified cross-mammalian conserved tissue signatures. In summary, we describe the relationship between Treg cell heterogeneity and recruitment to NLTs through the combined use of computational prediction and in vivo validation., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Messenger RNA encodes cellular function and phenotype. In the context of human cancer, it defines the identities of malignant cells and the diversity of tumor tissue. We studied 72,501 single-cell transcriptomes of human renal tumors and normal tissue from fetal, pediatric, and adult kidneys. We matched childhood Wilms tumor with specific fetal cell types, thus providing evidence for the hypothesis that Wilms tumor cells are aberrant fetal cells. In adult renal cell carcinoma, we identified a canonical cancer transcriptome that matched a little-known subtype of proximal convoluted tubular cell. Analyses of the tumor composition defined cancer-associated normal cells and delineated a complex vascular endothelial growth factor (VEGF) signaling circuit. Our findings reveal the precise cellular identities and compositions of human kidney tumors., (Copyright © 2018, American Association for the Advancement of Science.)

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology. Here we provide an overview of the state of single-cell genomics methods and an outlook on the use of single-cell techniques to decipher the adaptive and innate components of immunity., (Copyright © 2017, American Association for the Advancement of Science.)

Aging is characterized by progressive loss of physiological and cellular functions, but the molecular basis of this decline remains unclear. We explored how aging affects transcriptional dynamics using single-cell RNA sequencing of unstimulated and stimulated naïve and effector memory CD4 + T cells from young and old mice from two divergent species. In young animals, immunological activation drives a conserved transcriptomic switch, resulting in tightly controlled gene expression characterized by a strong up-regulation of a core activation program, coupled with a decrease in cell-to-cell variability. Aging perturbed the activation of this core program and increased expression heterogeneity across populations of cells in both species. These discoveries suggest that increased cell-to-cell transcriptional variability will be a hallmark feature of aging across most, if not all, mammalian tissues., (Copyright © 2017, American Association for the Advancement of Science.)

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans -membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA sequencing of lck:GFP cells in zebrafish and obtained the first transcriptome of specific immune cell types in a nonmammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T cells, a novel type of NK-like cells, and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune-cell-specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed the higher gene turnover of trans -membrane proteins in NK cells compared with T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates., (© 2017 Carmona et al.; Published by Cold Spring Harbor Laboratory Press.)

Mouse embryonic stem cells are dynamic and heterogeneous. For example, rare cells cycle through a state characterized by decondensed chromatin and expression of transcripts, including the Zscan4 cluster and MERVL endogenous retrovirus, which are usually restricted to preimplantation embryos. Here, we further characterize the dynamics and consequences of this transient cell state. Single-cell transcriptomics identified the earliest upregulated transcripts as cells enter the MERVL/Zscan4 state. The MERVL/Zscan4 transcriptional network was also upregulated during induced pluripotent stem cell reprogramming. Genome-wide DNA methylation and chromatin analyses revealed global DNA hypomethylation accompanying increased chromatin accessibility. This transient DNA demethylation was driven by a loss of DNA methyltransferase proteins in the cells and occurred genome-wide. While methylation levels were restored once cells exit this state, genomic imprints remained hypomethylated, demonstrating a potential global and enduring influence of endogenous retroviral activation on the epigenome., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

We developed TraCeR, a computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. We found that T cell clonotypes in a mouse Salmonella infection model span early activated CD4(+) T cells as well as mature effector and memory cells.

The transcriptional programs that govern hematopoiesis have been investigated primarily by population-level analysis of hematopoietic stem and progenitor cells, which cannot reveal the continuous nature of the differentiation process. Here we applied single-cell RNA-sequencing to a population of hematopoietic cells in zebrafish as they undergo thrombocyte lineage commitment. By reconstructing their developmental chronology computationally, we were able to place each cell along a continuum from stem cell to mature cell, refining the traditional lineage tree. The progression of cells along this continuum is characterized by a highly coordinated transcriptional program, displaying simultaneous suppression of genes involved in cell proliferation and ribosomal biogenesis as the expression of lineage specific genes increases. Within this program, there is substantial heterogeneity in the expression of the key lineage regulators. Overall, the total number of genes expressed, as well as the total mRNA content of the cell, decreases as the cells undergo lineage commitment., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Embryonic stem cell (ESC) culture conditions are important for maintaining long-term self-renewal, and they influence cellular pluripotency state. Here, we report single cell RNA-sequencing of mESCs cultured in three different conditions: serum, 2i, and the alternative ground state a2i. We find that the cellular transcriptomes of cells grown in these conditions are distinct, with 2i being the most similar to blastocyst cells and including a subpopulation resembling the two-cell embryo state. Overall levels of intercellular gene expression heterogeneity are comparable across the three conditions. However, this masks variable expression of pluripotency genes in serum cells and homogeneous expression in 2i and a2i cells. Additionally, genes related to the cell cycle are more variably expressed in the 2i and a2i conditions. Mining of our dataset for correlations in gene expression allowed us to identify additional components of the pluripotency network, including Ptma and Zfp640, illustrating its value as a resource for future discovery., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Hematopoietic stem cells (HSCs) are a rare cell type with the ability of long-term self-renewal and multipotency to reconstitute all blood lineages. HSCs are typically purified from the bone marrow using cell surface markers. Recent studies have identified significant cellular heterogeneities in the HSC compartment with subsets of HSCs displaying lineage bias. We previously discovered that the transcription factor Bcl11a has critical functions in the lymphoid development of the HSC compartment., Results: In this report, we employ single-cell transcriptomic analysis to dissect the molecular heterogeneities in HSCs. We profile the transcriptomes of 180 highly purified HSCs (Bcl11a (+/+) and Bcl11a (-/-)). Detailed analysis of the RNA-seq data identifies cell cycle activity as the major source of transcriptomic variation in the HSC compartment, which allows reconstruction of HSC cell cycle progression in silico. Single-cell RNA-seq profiling of Bcl11a (-/-) HSCs reveals abnormal proliferative phenotypes. Analysis of lineage gene expression suggests that the Bcl11a (-/-) HSCs are constituted of two distinct myeloerythroid-restricted subpopulations. Remarkably, similar myeloid-restricted cells could also be detected in the wild-type HSC compartment, suggesting selective elimination of lymphoid-competent HSCs after Bcl11a deletion. These defects are experimentally validated in serial transplantation experiments where Bcl11a (-/-) HSCs are myeloerythroid-restricted and defective in self-renewal., Conclusions: Our study demonstrates the power of single-cell transcriptomics in dissecting cellular process and lineage heterogeneities in stem cell compartments, and further reveals the molecular and cellular defects in the Bcl11a-deficient HSC compartment.

The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: CD4(+) T cells are key regulators of the adaptive immune system and can be divided into T helper (Th) cells and regulatory T (Treg) cells. During an immune response Th cells mature from a naive state into one of several effector subtypes that exhibit distinct functions. The transcriptional mechanisms that underlie the specific functional identity of CD4(+) T cells are not fully understood., Results: To assist investigations into the transcriptional identity and regulatory processes of these cells we performed mRNA-sequencing on three murine T helper subtypes (Th1, Th2 and Th17) as well as on splenic Treg cells and induced Treg (iTreg) cells. Our integrated analysis of this dataset revealed the gene expression changes associated with these related but distinct cellular identities. Each cell subtype differentially expresses a wealth of 'subtype upregulated' genes, some of which are well known whilst others promise new insights into signalling processes and transcriptional regulation. We show that hundreds of genes are regulated purely by alternative splicing to extend our knowledge of the role of post-transcriptional regulation in cell differentiation., Conclusions: This CD4(+) transcriptome atlas provides a valuable resource for the study of CD4(+) T cell populations. To facilitate its use by others, we have made the data available in an easily accessible online resource at www.th-express.org.

The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.