DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

Type I interferon alters invasive extravillous trophoblast function

Inappropriate type I interferon (IFN) signaling during embryo implantation and placentation is linked to poor pregnancy outcomes. Here, we evaluated the consequence of elevated type I IFN exposure on implantation using a biomimetic model of human implantation in an organ-on-a-chip device. We found that type I IFN reduced extravillous trophoblast (EVT) invasion capacity. Analyzing single-cell transcriptomes, we uncovered that IFN truncated endovascular EVT emergence in the implantation-on-a-chip device by stunting EVT epithelial-to-mesenchymal transition. Disruptions to the epithelial-to-mesenchymal transition is associated with the pathogenesis of preeclampsia, a life-threatening hypertensive disorder of pregnancy. Strikingly, unwarranted IFN stimulation induced genes associated with increased preeclampsia risk and a preeclamptic gene-like signature in EVTs. These dysregulated EVT phenotypes ultimately reduced EVT-mediated endothelial cell vascular remodeling in the implantation-on-a-chip device. Overall, our work indicates IFN signaling can alter EVT epithelial-to-mesenchymal transition progression which results in diminished EVT-mediated spiral artery remodeling and a preeclampsia gene signature upon sustained stimulation. Our work implicates unwarranted type I IFN as a maternal disturbance that can result in abnormal EVT function that could trigger preeclampsia.

Breast Cancer Macrophage Heterogeneity and Self-renewal are Determined by Spatial Localization

Tumor-infiltrating macrophages support critical steps in tumor progression, and their accumulation in the tumor microenvironment (TME) is associated with adverse outcomes and therapeutic resistance across human cancers. In the TME, macrophages adopt diverse phenotypic alterations, giving rise to heterogeneous immune activation states and induction of cell cycle. While the transcriptional profiles of these activation states are well-annotated across human cancers, the underlying signals that regulate macrophage heterogeneity and accumulation remain incompletely understood. Here, we leveraged a novel ex vivo organotypic TME (oTME) model of breast cancer, in vivo murine models, and human samples to map the determinants of functional heterogeneity of TME macrophages. We identified a subset of F4/80highSca-1+ self-renewing macrophages maintained by type-I interferon (IFN) signaling and requiring physical contact with cancer-associated fibroblasts. We discovered that the contact-dependent self-renewal of TME macrophages is mediated via Notch4, and its inhibition abrogated tumor growth of breast and ovarian carcinomas in vivo, as well as lung dissemination in a PDX model of triple-negative breast cancer (TNBC). Through spatial multi-omic profiling of protein markers and transcriptomes, we found that the localization of macrophages further dictates functionally distinct but reversible phenotypes, regardless of their ontogeny. Whereas immune-stimulatory macrophages (CD11C+CD86+) populated the tumor epithelial nests, the stroma-associated macrophages (SAMs) were proliferative, immunosuppressive (Sca-1+CD206+PD-L1+), resistant to CSF-1R depletion, and associated with worse patient outcomes. Notably, following cessation of CSF-1R depletion, macrophages rebounded primarily to the SAM phenotype, which was associated with accelerated growth of mammary tumors. Our work reveals the spatial determinants of macrophage heterogeneity in breast cancer and highlights the disruption of macrophage self-renewal as a potential new therapeutic strategy.

Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function

The clinical response to adoptive T cell therapies is strongly associated with transcriptional and epigenetic state. Thus, technologies to discover regulators of T cell gene networks and their corresponding phenotypes have great potential to improve the efficacy of T cell therapies. We developed pooled CRISPR screening approaches with compact epigenome editors to systematically profile the effects of activation and repression of 120 transcription factors and epigenetic modifiers on human CD8+ T cell state. These screens nominated known and novel regulators of T cell phenotypes with BATF3 emerging as a high confidence gene in both screens. We found that BATF3 overexpression promoted specific features of memory T cells such as increased IL7R expression and glycolytic capacity, while attenuating gene programs associated with cytotoxicity, regulatory T cell function, and T cell exhaustion. In the context of chronic antigen stimulation, BATF3 overexpression countered phenotypic and epigenetic signatures of T cell exhaustion. CAR T cells overexpressing BATF3 significantly outperformed control CAR T cells in both in vitro and in vivo tumor models. Moreover, we found that BATF3 programmed a transcriptional profile that correlated with positive clinical response to adoptive T cell therapy. Finally, we performed CRISPR knockout screens with and without BATF3 overexpression to define co-factors and downstream factors of BATF3, as well as other therapeutic targets. These screens pointed to a model where BATF3 interacts with JUNB and IRF4 to regulate gene expression and illuminated several other novel targets for further investigation.

Challenges in unsupervised clustering of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.

Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell's expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

Single-cell RNA sequencing of a European and an African lymphoblastoid cell line

In biomedical research, lymphoblastoid cell lines (LCLs), often established by in vitro infection of resting B cells with Epstein-Barr virus, are commonly used as surrogates for peripheral blood lymphocytes. Genomic and transcriptomic information on LCLs has been used to study the impact of genetic variation on gene expression in humans. Here we present single-cell RNA sequencing (scRNA-seq) data on GM12878 and GM18502—two LCLs derived from the blood of female donors of European and African ancestry, respectively. Cells from three samples (the two LCLs and a 1:1 mixture of the two) were prepared separately using a 10x Genomics Chromium Controller and deeply sequenced. The final dataset contained 7,045 cells from GM12878, 5,189 from GM18502, and 5,820 from the mixture, offering valuable information on single-cell gene expression in highly homogenous cell populations. This dataset is a suitable reference for population differentiation in gene expression at the single-cell level. Data from the mixture provide additional valuable information facilitating the development of statistical methods for data normalization and batch effect correction.

Design Type(s)	transcription profiling design • strain comparison design
Measurement Type(s)	transcription profiling assay
Technology Type(s)	RNA sequencing
Factor Type(s)	ancestry status • sex
Sample Characteristic(s)	GM12878 cell • GM18502 cell • immortal human peripheral vein-derived B cell line cell

Machine-accessible metadata file describing the reported data (ISA-Tab format)

MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell

The turnover of the intestinal epithelium is driven by multipotent LGR5⁺ crypt-base columnar cells (CBCs) located at the bottom of crypt zones¹. However, CBCs are lost following injury, such as irradiation², but the intestinal epithelium is nevertheless able to recover³. Thus, a second population of quiescent '+4' cells, or reserve stem cells (RSCs), has previously been proposed to regenerate the damaged intestine^4-7. Although CBCs and RSCs were thought to be mutually exclusive^4,8, subsequent studies have found that LGR5⁺ CBCs express RSC markers⁹ and that RSCs were dispensable-whereas LGR5⁺ cells were essential-for repair of the damaged intestine³. In addition, progenitors of absorptive enterocytes¹⁰, secretory cells^11-15 and slow cycling LGR5⁺ cells¹⁶ have been shown to contribute to regeneration whereas the transcriptional regulator YAP1, which is important for intestinal regeneration, was suggested to induce a pro-survival phenotype in LGR5⁺ cells¹⁷. Thus, whether cellular plasticity or distinct cell populations are critical for intestinal regeneration remains unknown. Here we applied single-cell RNA sequencing to profile the regenerating mouse intestine and identified a distinct, damage-induced quiescent cell type that we term the revival stem cell (revSC). revSCs are marked by high clusterin expression and are extremely rare under homoeostatic conditions, yet give rise-in a temporal hierarchy-to all the major cell types of the intestine, including LGR5⁺ CBCs. After intestinal damage by irradiation, targeted ablation of LGR5⁺ CBCs, or treatment with dextran sodium sulfate, revSCs undergo a YAP1-dependent transient expansion, reconstitute the LGR5⁺ CBC compartment and are required to regenerate a functional intestine. These studies thus define a unique stem cell that is mobilized by damage to revive the homoeostatic stem cell compartment and regenerate the intestinal epithelium.

Single Cell Gene Expression to Understand the Dynamic Architecture of the Heart

The recent development of single cell gene expression technologies, and especially single cell transcriptomics, have revolutionized the way biologists and clinicians investigate organs and organisms, allowing an unprecedented level of resolution to the description of cell demographics in both healthy and diseased states. Single cell transcriptomics provide information on prevalence, heterogeneity, and gene co-expression at the individual cell level. This enables a cell-centric outlook to define intracellular gene regulatory networks and to bridge toward the definition of intercellular pathways otherwise masked in bulk analysis. The technologies have developed at a fast pace producing a multitude of different approaches, with several alternatives to choose from at any step, including single cell isolation and capturing, lysis, RNA reverse transcription and cDNA amplification, library preparation, sequencing, and computational analyses. Here, we provide guidelines for the experimental design of single cell RNA sequencing experiments, exploring the current options for the crucial steps. Furthermore, we provide a complete overview of the typical data analysis workflow, from handling the raw sequencing data to making biological inferences. Significantly, advancements in single cell transcriptomics have already contributed to outstanding exploratory and functional studies of cardiac development and disease models, as summarized in this review. In conclusion, we discuss achievable outcomes of single cell transcriptomics' applications in addressing unanswered questions and influencing future cardiac clinical applications.

Discovery of rare cells from voluminous single cell expression data

Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage.