Trajectory-based differential expression analysis for single-cell sequencing data

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8⁺ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8⁺ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Endocrine Pancreas Development and Dysfunction Through the Lens of Single-Cell RNA-Sequencing

A chronic inability to maintain blood glucose homeostasis leads to diabetes, which can damage multiple organs. The pancreatic islets regulate blood glucose levels through the coordinated action of islet cell-secreted hormones, with the insulin released by β-cells playing a crucial role in this process. Diabetes is caused by insufficient insulin secretion due to β-cell loss, or a pancreatic dysfunction. The restoration of a functional β-cell mass might, therefore, offer a cure. To this end, major efforts are underway to generate human β-cells de novo, in vitro, or in vivo. The efficient generation of functional β-cells requires a comprehensive knowledge of pancreas development, including the mechanisms driving cell fate decisions or endocrine cell maturation. Rapid progress in single-cell RNA sequencing (scRNA-Seq) technologies has brought a new dimension to pancreas development research. These methods can capture the transcriptomes of thousands of individual cells, including rare cell types, subtypes, and transient states. With such massive datasets, it is possible to infer the developmental trajectories of cell transitions and gene regulatory pathways. Here, we summarize recent advances in our understanding of endocrine pancreas development and function from scRNA-Seq studies on developing and adult pancreas and human endocrine differentiation models. We also discuss recent scRNA-Seq findings for the pathological pancreas in diabetes, and their implications for better treatment.

XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

Transcriptional heterogeneity and tightly regulated changes in gene expression during Plasmodium berghei sporozoite development

Despite the critical role of Plasmodium sporozoites in malaria transmission, we still know little about the mechanisms underlying their development in mosquitoes. Here, we use single-cell RNA sequencing to characterize the gene expression profiles of 16,038 Plasmodium berghei sporozoites isolated throughout their development from midgut oocysts to salivary glands, and from forced salivation experiments. Our results reveal a succession of tightly regulated changes in gene expression occurring during the maturation of sporozoites and highlight candidate genes that could play important roles in oocyst egress, sporozoite motility, and the mechanisms underlying the invasion of mosquito salivary glands and mammalian hepatocytes. In addition, the single-cell data reveal extensive transcriptional heterogeneity among parasites isolated from the same anatomical site, suggesting that Plasmodium development in mosquitoes is asynchronous and regulated by intrinsic as well as environmental factors. Finally, our analyses show a decrease in transcriptional activity preceding the translational repression observed in mature sporozoites and associated with their quiescent state in salivary glands, followed by a rapid reactivation of the transcriptional machinery immediately upon salivation.

Single-Cell RNA Sequencing of the Adult Mammalian Heart-State-of-the-Art and Future Perspectives

Purpose of the Review

Cardiovascular disease remains the leading cause of death worldwide, resulting in cardiac dysfunction and, subsequently, heart failure (HF). Single-cell RNA sequencing (scRNA-seq) is a rapidly developing tool for studying the transcriptional heterogeneity in both healthy and diseased hearts. Wide applications of techniques like scRNA-seq could significantly contribute to uncovering the molecular mechanisms involved in the onset and progression to HF and contribute to the development of new, improved therapies. This review discusses several studies that successfully applied scRNA-seq to the mouse and human heart using various methods of tissue processing and downstream analysis.

Recent Findings

The application of scRNA-seq in the cardiovascular field is continuously expanding, providing new detailed insights into cardiac pathophysiology.

Summary

Increased understanding of cardiac pathophysiology on the single-cell level will contribute to the development of novel, more effective therapeutic strategies. Here, we summarise the possible application of scRNA-seq to the adult mammalian heart.

Single-cell RNA sequencing reveals developmental heterogeneity among Plasmodium berghei sporozoites

In the malaria-causing parasite's life cycle, Plasmodium sporozoites must travel from the midgut of a mosquito to the salivary glands before they can infect a mammalian host. However, only a fraction of sporozoites complete the journey. Since salivary gland invasion is required for transmission of sporozoites, insights at the molecular level can contribute to strategies for malaria prevention. Recent advances in single-cell RNA sequencing provide an opportunity to assess sporozoite heterogeneity at a resolution unattainable by bulk RNA sequencing methods. In this study, we use a droplet-based single-cell RNA sequencing workflow to analyze the transcriptomes of over 8000 Plasmodium berghei sporozoites derived from the midguts and salivary glands of Anopheles stephensi mosquitoes. The detection of known marker genes confirms the successful capture and sequencing of samples composed of a mixed population of sporozoites. Using data integration, clustering, and trajectory analyses, we reveal differences in gene expression profiles of individual sporozoites, and identify both annotated and unannotated markers associated with sporozoite development. Our work highlights the utility of a high-throughput workflow for the transcriptomic profiling of Plasmodium sporozoites, and provides new insights into gene usage during the parasite's development in the mosquito.

Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution

Adipose tissues display a remarkable ability to adapt to the dietary status. Here, we have applied single-nucleus RNA-seq to map the plasticity of mouse epididymal white adipose tissue at single-nucleus resolution in response to high-fat-diet-induced obesity. The single-nucleus approach allowed us to recover all major cell types and to reveal distinct transcriptional stages along the entire adipogenic trajectory from preadipocyte commitment to mature adipocytes. We demonstrate the existence of different adipocyte subpopulations and show that obesity leads to disappearance of the lipogenic subpopulation and increased abundance of the stressed lipid-scavenging subpopulation. Moreover, obesity is associated with major changes in the abundance and gene expression of other cell populations, including a dramatic increase in lipid-handling genes in macrophages at the expense of macrophage-specific genes. The data provide a powerful resource for future hypothesis-driven investigations of the mechanisms of adipocyte differentiation and adipose tissue plasticity.

A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.

Compression of quantification uncertainty for scRNA-seq counts

Motivation

Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.

Results

We demonstrate that storing only the mean and variance from a set of inferential replicates (‘compression’) is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate ‘pseudo-inferential’ replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.

Availability and implementation

makeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper’s GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode.

Supplementary information

Supplementary data are available at Bioinformatics online.

Prior activation state shapes the microglia response to antihuman TREM2 in a mouse model of Alzheimer's disease

Triggering receptor expressed on myeloid cells 2 (TREM2) sustains microglia response to brain injury stimuli including apoptotic cells, myelin damage, and amyloid β (Aβ). Alzheimer's disease (AD) risk is associated with the TREM2R47H variant, which impairs ligand binding and consequently microglia responses to Aβ pathology. Here, we show that TREM2 engagement by the mAb hT2AB as surrogate ligand activates microglia in 5XFAD transgenic mice that accumulate Aβ and express either the common TREM2 variant (TREM2CV) or TREM2R47H scRNA-seq of microglia from TREM2CV-5XFAD mice treated once with control hIgG1 exposed four distinct trajectories of microglia activation leading to disease-associated (DAM), interferon-responsive (IFN-R), cycling (Cyc-M), and MHC-II expressing (MHC-II) microglia types. All of these were underrepresented in TREM2R47H-5XFAD mice, suggesting that TREM2 ligand engagement is required for microglia activation trajectories. Moreover, Cyc-M and IFN-R microglia were more abundant in female than male TREM2CV-5XFAD mice, likely due to greater Aβ load in female 5XFAD mice. A single systemic injection of hT2AB replenished Cyc-M, IFN-R, and MHC-II pools in TREM2R47H-5XFAD mice. In TREM2CV-5XFAD mice, however, hT2AB brought the representation of male Cyc-M and IFN-R microglia closer to that of females, in which these trajectories had already reached maximum capacity. Moreover, hT2AB induced shifts in gene expression patterns in all microglial pools without affecting representation. Repeated treatment with a murinized hT2AB version over 10 d increased chemokines brain content in TREM2R47H-5XFAD mice, consistent with microglia expansion. Thus, the impact of hT2AB on microglia is shaped by the extent of TREM2 endogenous ligand engagement and basal microglia activation.

Transcriptional Profiling During Neural Conversion

The processes that underlie neuronal conversion ultimately involve a reorganization of transcriptional networks to establish a neuronal cell fate. As such, transcriptional profiling is a key component toward understanding this process. In this chapter, we will discuss methods of elucidating transcriptional networks during neuronal reprogramming and considerations that should be incorporated in experimental design.

Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

Single-cell genomics technology has transformed our understanding of complex cellular systems. However, excessive cost and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high-content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all main hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the functional capacities of precisely mapped cell states to be measured at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

Investigating higher-order interactions in single-cell data with scHOT

Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered 'pseudotime' offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT-single-cell higher-order testing-which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.

Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis

The cell type specific sequences of transcriptional programs during lung regeneration have remained elusive. Using time-series single cell RNA-seq of the bleomycin lung injury model, we resolved transcriptional dynamics for 28 cell types. Trajectory modeling together with lineage tracing revealed that airway and alveolar stem cells converge on a unique Krt8 + transitional stem cell state during alveolar regeneration. These cells have squamous morphology, feature p53 and NFkB activation and display transcriptional features of cellular senescence. The Krt8+ state appears in several independent models of lung injury and persists in human lung fibrosis, creating a distinct cell-cell communication network with mesenchyme and macrophages during repair. We generated a model of gene regulatory programs leading to Krt8+ transitional cells and their terminal differentiation to alveolar type-1 cells. We propose that in lung fibrosis, perturbed molecular checkpoints on the way to terminal differentiation can cause aberrant persistence of regenerative intermediate stem cell states.

Single-Cell RNA-Seq Mapping of Human Thymopoiesis Reveals Lineage Specification Trajectories and a Commitment Spectrum in T Cell Development

The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34+ progenitor and the more differentiated CD34- fractions in the human postnatal thymus. CD34+ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7- and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34+ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

The Role of Single-Cell Technology in the Study and Control of Infectious Diseases

The advent of single-cell research in the recent decade has allowed biological studies at an unprecedented resolution and scale. In particular, single-cell analysis techniques such as Next-Generation Sequencing (NGS) and Fluorescence-Activated Cell Sorting (FACS) have helped show substantial links between cellular heterogeneity and infectious disease progression. The extensive characterization of genomic and phenotypic biomarkers, in addition to host-pathogen interactions at the single-cell level, has resulted in the discovery of previously unknown infection mechanisms as well as potential treatment options. In this article, we review the various single-cell technologies and their applications in the ongoing fight against infectious diseases, as well as discuss the potential opportunities for future development.

Neural and Self-Report Markers of Reassurance: A Generalized Additive Modelling Approach

Research has shown that engaging in self-reassurance, a compassionately motivated cognitive relating style, can down-regulate neural markers of threat and pain. Whilst important, the relationship between neural and self-report markers of reassurance are largely unknown. Here we analyzed previously published fMRI data which measured neural responses when participants engaged in self-reassurance toward a mistake, setback, or failure. Within the present paper, we identified correlations between regions of interest extracted during self-reassurance with fMRI and self-report data. Using generalized additive modelling, we show that participants with greater inadequate forms of self-criticism exhibited greater neural activation within the medial prefrontal cortex (MPFC) and anterior insula (AI). Furthermore, a relationship between greater fears of expressing compassion to the self and neural activation within the MPFC returned non-significant after correction for multiple comparisons. No significant relationships were observed between brain activation and hated and reassuring forms of self-criticism. Our results identify preliminary evidence for neural activity during self-reassurance as correlated with self-report markers, and we outline a method for modelling neural and self-report data which can be applied to future studies in compassion science, particularly with a clinical sample.