Single-cell mRNA quantification and differential analysis with Census

Exploring the role of RALYL in Alzheimer's disease reserve by network-based approaches

BACKGROUND

Alzheimer's disease (AD) reserve theory is based on specific individual characteristics that are associated with a higher resilience against neurodegeneration and its symptoms. A given degree of AD pathology may contribute to varying cognitive decline levels in different individuals. Although this phenomenon is attributed to reserve, the biological mechanisms that underpin it remain elusive, which restricts translational medicine research and treatment strategy development.

METHODS

Network-based approaches were integrated to identify AD reserve related genes. Then, AD brain transcriptomics data were clustered into co-expression modules, and a Bayesian network was developed using these modules plus AD reserve related phenotypes. The directed acyclic graph suggested that the module was strongly associated with AD reserve. The hub gene of the module of interest was filtered using the topological method. Validation was performed in the multi-AD brain transcriptomic dataset.

RESULTS

We revealed that the RALYL (RALY RNA Binding Protein-like) is the hub gene of the module which was highly associated with AD reserve related phenotypes. Pseudo-time projections of RALYL revealed the changes in relative expression drivers in the AD and control subjects over pseudo-time had distinct transcriptional states. Notably, the expression of RALYL decreased with the gradual progression of AD, and this corresponded to MMSE decline. Subjects with AD reserve exhibited significantly higher RALYL expression than those without AD reserve.

CONCLUSION

The present study suggests that RALYL may be associated with AD reserve, and it provides novel insights into the mechanisms of AD reserve and highlights the potential role of RALYL in this process.

Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

BACKGROUND

As core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing.

RESULTS

We perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors, and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues.

CONCLUSIONS

The adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

An atlas of immune cell exhaustion in HIV-infected individuals revealed by single-cell transcriptomics

Chronic infection with human immunodeficiency virus (HIV) can cause progressive loss of immune cell function, or exhaustion, which impairs control of virus replication. However, little is known about the development and maintenance, as well as heterogeneity of immune cell exhaustion. Here, we investigated the effects of HIV infection on immune cell exhaustion at the transcriptomic level by analyzing single-cell RNA sequencing of peripheral blood mononuclear cells from four healthy subjects (37,847 cells) and six HIV-infected donors (28,610 cells). We identified nine immune cell clusters and eight T cell subclusters, and three of these (exhausted CD4+ and CD8+ T cells and interferon-responsive CD8+ T cells) were detected only in samples from HIV-infected donors. An inhibitory receptor KLRG1 was identified in a HIV-1 specific exhausted CD8+ T cell population expressing KLRG1, TIGIT, and T-betdimEomeshi markers. Ex-vivo antibody blockade of KLRG1 restored the function of HIV-specific exhausted CD8+ T cells demonstrating the contribution of KLRG1+ population to T cell exhaustion and providing an immunotherapy target to treat HIV chronic infection. These data provide a comprehensive analysis of gene signatures associated with immune cell exhaustion during HIV infection, which could be useful in understanding exhaustion mechanisms and developing new cure therapies.

A novel cardiomyogenic role for Isl1+ neural crest cells in the inflow tract

The degree to which populations of cardiac progenitors (CPCs) persist in the postnatal heart remains a controversial issue in cardiobiology. To address this question, we conducted a spatiotemporally resolved analysis of CPC deployment dynamics, tracking cells expressing the pan-CPC gene Isl1 Most CPCs undergo programmed silencing during early cardiogenesis through proteasome-mediated and PRC2 (Polycomb group repressive complex 2)-mediated Isl1 repression, selectively in the outflow tract. A notable exception is a domain of cardiac neural crest cells (CNCs) in the inflow tract. These "dorsal CNCs" are regulated through a Wnt/β-catenin/Isl1 feedback loop and generate a limited number of trabecular cardiomyocytes that undergo multiple clonal divisions during compaction, to eventually produce ~10% of the biventricular myocardium. After birth, CNCs continue to generate cardiomyocytes that, however, exhibit diminished clonal amplification dynamics. Thus, although the postnatal heart sustains cardiomyocyte-producing CNCs, their regenerative potential is likely diminished by the loss of trabeculation-like proliferative properties.

Spatiotemporal cellular movement and fate decisions during first pharyngeal arch morphogenesis

Cranial neural crest (CNC) cells contribute to different cell types during embryonic development. It is unknown whether postmigratory CNC cells undergo dynamic cellular movement and how the process of cell fate decision occurs within the first pharyngeal arch (FPA). Our investigations demonstrate notable heterogeneity within the CNC cells, refine the patterning domains, and identify progenitor cells within the FPA. These progenitor cells undergo fate bifurcation that separates them into common progenitors and mesenchymal cells, which are characterized by Cdk1 and Spry2/Notch2 expression, respectively. The common progenitors undergo further bifurcations to restrict them into osteogenic/odontogenic and chondrogenic/fibroblast lineages. Disruption of a patterning domain leads to specific mandible and tooth defects, validating the binary cell fate restriction process. Different from the compartment model of mandibular morphogenesis, our data redefine heterogeneous cellular domains within the FPA, reveal dynamic cellular movement in time, and describe a sequential series of binary cell fate decision-making process.

What has single-cell RNA sequencing revealed about microglial neuroimmunology?

The use of single-cell RNA sequencing (scRNA-seq) in microglial research is increasing rapidly. The basic workflow of this approach consists of isolating single cells, followed by sequencing. scRNA-seq is capable of examining microglial heterogeneity on a cellular level. However, the results gained from applying this technique suffer from discrepancies due to differences between applied methods characteristics such as the number of cells sequenced and the depth of sequencing. This review aims to shed more light on the recent developments that happened in this field and how they are related to the methods used. To do that, we track the progress and limitations of various scRNA-seq methods currently available. The review then summarizes the current knowledge gained using scRNA-seq in the field of microglia, including novel subpopulations associated with function and development under homeostasis as well during several pathological conditions such as Alzheimer, lipopolysaccharide response, and HIV in relation to the methods employed. Our review points out that despite major developments found using this technique, current scRNA-seq methods suffer from high cost, low yields, and nonstandardization of generated data. Additional development of scRNA-seq methods will raise our awareness of microglia's heterogeneity and plasticity under healthy and pathological conditions.

Histone H3.3G34-Mutant Interneuron Progenitors Co-opt PDGFRA for Gliomagenesis

Histone H3.3 glycine 34 to arginine/valine (G34R/V) mutations drive deadly gliomas and show exquisite regional and temporal specificity, suggesting a developmental context permissive to their effects. Here we show that 50% of G34R/V tumors (n = 95) bear activating PDGFRA mutations that display strong selection pressure at recurrence. Although considered gliomas, G34R/V tumors actually arise in GSX2/DLX-expressing interneuron progenitors, where G34R/V mutations impair neuronal differentiation. The lineage of origin may facilitate PDGFRA co-option through a chromatin loop connecting PDGFRA to GSX2 regulatory elements, promoting PDGFRA overexpression and mutation. At the single-cell level, G34R/V tumors harbor dual neuronal/astroglial identity and lack oligodendroglial programs, actively repressed by GSX2/DLX-mediated cell fate specification. G34R/V may become dispensable for tumor maintenance, whereas mutant-PDGFRA is potently oncogenic. Collectively, our results open novel research avenues in deadly tumors. G34R/V gliomas are neuronal malignancies where interneuron progenitors are stalled in differentiation by G34R/V mutations and malignant gliogenesis is promoted by co-option of a potentially targetable pathway, PDGFRA signaling.

Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

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SARS-CoV2 infection elicits dynamic changes of circulating cells in the blood

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Severe COVID-19 is characterized by increased metabolically active plasmablasts

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Elevation of IFN-activated megakaryocytes and erythroid cells in severe COVID-19

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Cell-type-specific expression signatures are associated with a fatal COVID-19 outcome

Parallel Social Information Processing Circuits Are Differentially Impacted in Autism

Parallel processing circuits are thought to dramatically expand the network capabilities of the nervous system. Magnocellular and parvocellular oxytocin neurons have been proposed to subserve two parallel streams of social information processing, which allow a single molecule to encode a diverse array of ethologically distinct behaviors. Here we provide the first comprehensive characterization of magnocellular and parvocellular oxytocin neurons in male mice, validated across anatomical, projection target, electrophysiological, and transcriptional criteria. We next use novel multiple feature selection tools in Fmr1-KO mice to provide direct evidence that normal functioning of the parvocellular but not magnocellular oxytocin pathway is required for autism-relevant social reward behavior. Finally, we demonstrate that autism risk genes are enriched in parvocellular compared with magnocellular oxytocin neurons. Taken together, these results provide the first evidence that oxytocin-pathway-specific pathogenic mechanisms account for social impairments across a broad range of autism etiologies.

Distinct regional ontogeny and activation of tumor associated macrophages in human glioblastoma

Tumor-associated macrophages (TAMs) constitute up to 50% of tumor bulk in glioblastoma (GBM) and play an important role in tumor maintenance and progression. The recently discovered differences between invading tumour periphery and hypoxic tumor core implies that macrophage biology is also distinct by location. This may provide further insight into the observed treatment resistance to immune modulation. We hypothesize that macrophage activation occurs through processes that are distinct in tumor periphery versus core. We therefore investigated regional differences in TAM recruitment and evolution in GBM by combining open source single cell and bulk gene expression data. We used single cell gene expression data from 4 glioblastomas (total of 3589 cells) and 122 total bulk samples obtained from 10 different patients. Cell identity, ontogeny (bone-marrow derived macrophages-BMDM vs microglia), and macrophage activation state were inferred using verified gene expression signatures. We captured the spectrum of immune states using cell trajectory analysis with pseudotime ordering. In keeping with previous studies, TAMs carrying BMDM identity were more abundant in tumor bulk while microglia-derived TAMs dominated the tumor periphery across all macrophage activation states including pre-activation. We note that core TAMs evolve towards a pro-inflammatory state and identify a subpopulation of cells based on a gene program exhibiting strong, opposing correlation with Programmed cell Death-1 (PD-1) signaling, which may correlate to their response to PD-1 inhibition. By contrast, peripheral TAMs evolve towards anti-inflammatory phenotype and contains a population of cells strongly associated with NFkB signaling. Our preliminary analysis suggests important regional differences in TAMs with regard to recruitment and evolution. We identify regionally distinct and potentially actionable cell subpopulations and advocate the need for a multi-targeted approach to GBM therapeutics.

Reconstructing the Developmental Trajectories of Multiple Subtypes in Pulmonary Parenchymal Epithelial Cells by Single-Cell RNA-seq

Background

Some lung diseases are cell type-specific. It is essential to study the cellular anatomy of the normal human lung for exploring the cellular origin of lung disease and the cell development trajectory.

Methods

We used the Seurat R package for data quality control. The principal component analysis (PCA) was used for linear dimensionality reduction. UMAP and tSNE were used for dimensionality reduction. Muonocle2 was used to extract lung epithelial cells to analyze the subtypes of epithelial cells further and to study the development of these cell subtypes.

Results

We showed a total of 20154 high quality of cells from human normal lung tissue. They were initially divided into 17 clusters cells and then identified as seven types of cells, namely macrophages, monocytes, CD8 + T cells, epithelial cells, endothelial cells, adipocytes, and NK cells. 4240 epithelial cells were extracted for further analysis and they were divided into seven clusters. The seven cell clusters include alveolar cell, alveolar endothelial progenitor, ciliated cell, secretory cell, ionocyte cell, and a group of cells that are not clear at present. We show the development track of these subtypes of epithelial cells, in which we speculate that alveolar epithelial progenitor (AEP) is a kind of progenitor cells that can develop into alveolar cells, and find six essential genes that determine the cell fate, including AGER, RPL10, RPL9, RPS18, RPS27, and SFTPB.

Conclusion

We provide a transcription map of human lung cells, especially the in-depth study on the development of epithelial cell subtypes, which will help us to study lung cell biology and lung diseases.

Statistical significance of cluster membership for unsupervised evaluation of cell identities

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries.

RESULTS

We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in an unsupervised manner. Diverse simulation studies demonstrate accuracy of the jackstraw test for cluster membership. We propose a posterior probability that a cell should be included in that clustering-based subpopulation. Posterior inclusion probabilities (PIPs) for cluster memberships can be used to select and visualize samples relevant to subpopulations. The proposed methods are applied on three scRNA-seq datasets. First, a mixture of Jurkat and 293T cell lines provides two distinct cellular populations. Second, Cell Hashing yields cell identities corresponding to eight donors which are independently analyzed by the jackstraw. Third, peripheral blood mononuclear cells are used to explore heterogeneous immune populations. The proposed P-values and PIPs lead to probabilistic feature selection of single cells that can be visualized using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and others. By learning uncertainty in clustering high-dimensional data, the proposed methods enable unsupervised evaluation of cluster membership.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/package=jackstraw.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Global Absence and Targeting of Protective Immune States in Severe COVID-19

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense.

One Sentence Summary

In severe COVID-19 patients, the immune system fails to generate cells that define mild disease; antibodies in their serum actively prevents the successful production of those cells.

Limiting Self-Renewal of the Basal Compartment by PKA Activation Induces Differentiation and Alters the Evolution of Mammary Tumors

Differentiation therapy utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift toward terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have met with limited success. Mammary-specific activation of PKA in mouse models leads to aberrant differentiation and diminished self-renewing potential of the basal compartment, which harbors mammary repopulating cells. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential, and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single-cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. Acting through the repression of SOX4, PKA activation promotes tumor differentiation and represents a possible adjuvant to chemotherapy for certain breast cancers.

Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy

A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.

Developmental, cellular, and behavioral phenotypes in a mouse model of congenital hypoplasia of the dentate gyrus

In the hippocampus, a widely accepted model posits that the dentate gyrus improves learning and memory by enhancing discrimination between inputs. To test this model, we studied conditional knockout mice in which the vast majority of dentate granule cells (DGCs) fail to develop - including nearly all DGCs in the dorsal hippocampus - secondary to eliminating Wntless (Wls) in a subset of cortical progenitors with Gfap-Cre. Other cells in the Wlsfl/-;Gfap-Cre hippocampus were minimally affected, as determined by single nucleus RNA sequencing. CA3 pyramidal cells, the targets of DGC-derived mossy fibers, exhibited normal morphologies with a small reduction in the numbers of synaptic spines. Wlsfl/-;Gfap-Cre mice have a modest performance decrement in several complex spatial tasks, including active place avoidance. They were also modestly impaired in one simpler spatial task, finding a visible platform in the Morris water maze. These experiments support a role for DGCs in enhancing spatial learning and memory.

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

Single-cell RNA sequencing of Tocilizumab-treated peripheral blood mononuclear cells as an in vitro model of inflammation

COVID-19 has posed a significant threat to global health. Early data has revealed that IL-6, a key regulatory cytokine, plays an important role in the cytokine storm of COVID-19. Multiple trials are therefore looking at the effects of Tocilizumab, an IL-6 receptor antibody that inhibits IL-6 activity, on treatment of COVID-19, with promising findings. As part of a clinical trial looking at the effects of Tocilizumab treatment on kidney transplant recipients with subclinical rejection, we performed single-cell RNA sequencing of comparing stimulated PBMCs before and after Tocilizumab treatment. We leveraged this data to create an in vitro cytokine storm model, to better understand the effects of Tocilizumab in the presence of inflammation. Tocilizumab-treated cells had reduced expression of inflammatory-mediated genes and biologic pathways, particularly amongst monocytes. These results support the hypothesis that Tocilizumab may hinder the cytokine storm of COVID-19, through a demonstration of biologic impact at the single-cell level.

Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

BACKGROUND

Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues.

METHODS

We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies.

RESULTS

We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene(ERG) exerts an important role in maintaining normal aortic wall function.

CONCLUSIONS

Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors

Biomaterial systems have enabled the in vitro production of complex, emergent tissue behaviors that were not possible with conventional two-dimensional culture systems, allowing for analysis of both normal development and disease processes. We propose that the path towards developing the design parameters for biomaterial systems lies with identifying the molecular drivers of emergent behavior through leveraging technological advances in systems biology, including single cell omics, genetic engineering, and high content imaging. This growing research opportunity at the intersection of the fields of tissue engineering and systems biology - systems tissue engineering - can uniquely interrogate the mechanisms by which complex tissue behaviors emerge with the potential to capture the contribution of i) dynamic regulation of tissue development and dysregulation, ii) single cell heterogeneity and the function of rare cell types, and iii) the spatial distribution and structure of individual cells and cell types within a tissue. By leveraging advances in both biological and materials data science, systems tissue engineering can facilitate the identification of biomaterial design parameters that will accelerate basic science discovery and translation.

The Msi1-mTOR pathway drives the pathogenesis of mammary and extramammary Paget's disease

Mammary and extramammary Paget's Diseases (PD) are a malignant skin cancer characterized by the appearance of Paget cells. Although easily diagnosed, its pathogenesis remains unknown. Here, single-cell RNA-sequencing identified distinct cellular states, novel biomarkers, and signaling pathways - including mTOR, associated with extramammary PD. Interestingly, we identified MSI1 ectopic overexpression in basal epithelial cells of human PD skin, and show that Msi1 overexpression in the epidermal basal layer of mice phenocopies human PD at histopathological, single-cell and molecular levels. Using this mouse model, we identified novel biomarkers of Paget-like cells that translated to human Paget cells. Furthermore, single-cell trajectory, RNA velocity and lineage-tracing analyses revealed a putative keratinocyte-to-Paget-like cell conversion, supporting the in situ transformation theory of disease pathogenesis. Mechanistically, the Msi1-mTOR pathway drives keratinocyte-Paget-like cell conversion, and suppression of mTOR signaling with Rapamycin significantly rescued the Paget-like phenotype in Msi1-overexpressing transgenic mice. Topical Rapamycin treatment improved extramammary PD-associated symptoms in humans, suggesting mTOR inhibition as a novel therapeutic treatment in PD.

APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer's disease

Beta-amyloid deposition is a defining feature of Alzheimer's disease (AD). How genetic risk factors, like APOE and TREM2, intersect with cellular responses to beta-amyloid in human tissues is not fully understood. Using single-nucleus RNA sequencing of postmortem human brain with varied APOE and TREM2 genotypes and neuropathology, we identified distinct microglia subpopulations, including a subpopulation of CD163-positive amyloid-responsive microglia (ARM) that are depleted in cases with APOE and TREM2 risk variants. We validated our single-nucleus RNA sequencing findings in an expanded cohort of AD cases, demonstrating that APOE and TREM2 risk variants are associated with a significant reduction in CD163-positive amyloid-responsive microglia. Our results showcase the diverse microglial response in AD and underscore how genetic risk factors influence cellular responses to underlying pathologies.

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

What has single-cell RNA-seq taught us about mammalian spermatogenesis?

Mammalian spermatogenesis is a complex developmental program that transforms mitotic testicular germ cells (spermatogonia) into mature male gametes (sperm) for production of offspring. For decades, it has been known that this several-weeks-long process involves a series of highly ordered and morphologically recognizable cellular changes as spermatogonia proliferate, spermatocytes undertake meiosis, and spermatids develop condensed nuclei, acrosomes, and flagella. Yet, much of the underlying molecular logic driving these processes has remained opaque because conventional characterization strategies often aggregated groups of cells to meet technical requirements or due to limited capability for cell selection. Recently, a cornucopia of single-cell transcriptome studies has begun to lift the veil on the full compendium of gene expression phenotypes and changes underlying spermatogenic development. These datasets have revealed the previously obscured molecular heterogeneity among and between varied spermatogenic cell types and are reinvigorating investigation of testicular biology. This review describes the extent of available single-cell RNA-seq profiles of spermatogenic and testicular somatic cells, how those data were produced and evaluated, their present value for advancing knowledge of spermatogenesis, and their potential future utility at both the benchtop and bedside.

The myeloid type I interferon response to myocardial infarction begins in bone marrow and is regulated by Nrf2-activated macrophages

Sterile tissue injury is thought to locally activate innate immune responses via damage-associated molecular patterns (DAMPs). Whether innate immune pathways are remotely activated remains relatively unexplored. Here, by analyzing ~145,000 single-cell transcriptomes at steady state and after myocardial infarction (MI) in mice and humans, we show that the type I interferon (IFN) response, characterized by expression of IFN-stimulated genes (ISGs), begins far from the site of injury, in neutrophil and monocyte progenitors within the bone marrow. In the peripheral blood of patients, we observed defined subsets of ISG-expressing neutrophils and monocytes. In the bone marrow and blood of mice, ISG expression was detected in neutrophils and monocytes and their progenitors, intensified with maturation at steady-state and after MI, and was controlled by Tet2 and Irf3 transcriptional regulators. Within the infarcted heart, ISG-expressing cells were negatively regulated by Nrf2 activation in Ccr2^- steady-state cardiac macrophages. Our results show that IFN signaling begins in the bone marrow, implicate multiple transcriptional regulators (Tet2, Irf3, and Nrf2) in governing ISG expression, and provide a clinical biomarker (ISG score) for studying IFN signaling in patients.

Cell Development Deficiency and Gene Expression Dysregulation of Trisomy 21 Retina Revealed by Single-Nucleus RNA Sequencing

Retina is a crucial tissue for capturing and processing light stimulus. It is critical to describe the characteristics of retina at the single-cell level for understanding its biological functions. A variety of abnormalities in terms of morphology and function are present in the trisomy 21 (T21) retina. To evaluate the consequences of chromosome aneuploidy on retina development, we identified the single-cell transcriptional profiles of a T21 fetus and performed comprehensive bioinformatic analyses. Our data revealed the diversity and heterogeneity of cellular compositions in T21 retina, as well as the abnormal constitution of T21 retina compared to disomic retina. In total, we identified seven major cell types and several subtypes within each cell type, followed by the detection of corresponding molecular markers, including previously reported ones and a series of novel markers. Through the analysis of the retinal differentiation process, subtypes of retinal progenitor cells (RPCs) exhibiting the potential of different retinal cell-type commitments and certain Müller glial cells (MGs) with differentiating potency were identified. Moreover, the extensive communication networks between cellular types were confirmed, among which a few ligand–receptor interactions were related to the formation and function of retina and immunoregulatory interactions. Taken together, our data provides the first ever single-cell transcriptome profiles for human T21 retina, which facilitates the understanding on the dosage effects of chromosome 21 on the development of retina.

Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring

Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.

Hic1 Defines Quiescent Mesenchymal Progenitor Subpopulations with Distinct Functions and Fates in Skeletal Muscle Regeneration

Many adult tissues contain resident stem cells, such as the Pax7⁺ satellite cells within skeletal muscle, that regenerate parenchymal elements following damage. Tissue-resident mesenchymal progenitors (MPs) also participate in regeneration, although their function and fate in this process are unclear. Here, we identify Hypermethylated in cancer 1 (Hic1) as a marker of MPs in skeletal muscle and further show that Hic1 deletion leads to MP hyperplasia. Single-cell RNA-seq and ATAC-seq analysis of Hic1⁺ MPs in skeletal muscle shows multiple subpopulations, which we further show have distinct functions and lineage potential. Hic1⁺ MPs orchestrate multiple aspects of skeletal muscle regeneration by providing stage-specific immunomodulation and trophic and mechanical support. During muscle regeneration, Hic1⁺ derivatives directly contribute to several mesenchymal compartments including Col22a1-expressing cells within the myotendinous junction. Collectively, these findings demonstrate that HIC1 regulates MP quiescence and identifies MP subpopulations with transient and enduring roles in muscle regeneration.

Spectacle: An interactive resource for ocular single-cell RNA sequencing data analysis

Single-cell RNA sequencing has revolutionized ocular gene expression studies. This technology has enabled researchers to identify expression signatures for rare cell types and characterize how gene expression changes across biological conditions, such as topographic region or disease status. However, sharing single-cell RNA sequencing results remains a major obstacle, particular for individuals without a computational background. To address these limitations, we developed Spectacle, an interactive web-based resource for exploring previously published single-cell RNA sequencing data from ocular studies. Spectacle is powered by a locally developed R package, cellcuratoR, which utilizes the Shiny framework in R to generate interactive visualizations for single-cell expression data. Spectacle contains five pre-processed ocular single-cell RNA sequencing data sets and is accessible via the web at OcularGeneExpression.org/singlecell. With Spectacle, users can interactively identify which cell types express a gene of interest, detect transcriptomic subpopulations within a cell type, and perform highly flexible differential expression analyses. The freely-available Spectacle system reduces the bioinformatic barrier for interacting with rich single-cell RNA sequencing studies from ocular tissues, making it easy to quickly identify cell types that express a gene of interest.

Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering

In recent years, single-cell RNA sequencing reveals diverse cell genetics at unprecedented resolutions. Such technological advances enable researchers to uncover the functionally distinct cell subtypes such as hematopoietic stem cell subpopulation identification. However, most of the related algorithms have been hindered by the high-dimensionality and sparse nature of single-cell RNA sequencing (RNA-seq) data. To address those problems, we propose a multiobjective evolutionary clustering based on adaptive non-negative matrix factorization (MCANMF) for multiobjective single-cell RNA-seq data clustering. First, adaptive non-negative matrix factorization is proposed to decompose data for feature extraction. After that, a multiobjective clustering algorithm based on learning vector quantization is proposed to analyze single-cell RNA-seq data. To validate the effectiveness of MCANMF, we benchmark MCANMF against 15 state-of-the-art methods including seven feature extraction methods, seven clustering methods, and the kernel-based similarity learning method on six published single-cell RNA sequencing datasets comprehensively. When compared with those 15 state-of-the-art methods, MCANMF performs better than the others on those single-cell RNA sequencing datasets according to multiple evaluation metrics. Moreover, the MCANMF component analysis, time complexity analysis, and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

The cellular and molecular landscape of hypothalamic patterning and differentiation from embryonic to late postnatal development

The hypothalamus is a central regulator of many innate behaviors essential for survival, but the molecular mechanisms controlling hypothalamic patterning and cell fate specification are poorly understood. To identify genes that control hypothalamic development, we have used single-cell RNA sequencing (scRNA-Seq) to profile mouse hypothalamic gene expression across 12 developmental time points between embryonic day 10 and postnatal day 45. This identified genes that delineated clear developmental trajectories for all major hypothalamic cell types, and readily distinguished major regional subdivisions of the developing hypothalamus. By using our developmental dataset, we were able to rapidly annotate previously unidentified clusters from existing scRNA-Seq datasets collected during development and to identify the developmental origins of major neuronal populations of the ventromedial hypothalamus. We further show that our approach can rapidly and comprehensively characterize mutants that have altered hypothalamic patterning, identifying Nkx2.1 as a negative regulator of prethalamic identity. These data serve as a resource for further studies of hypothalamic development, physiology, and dysfunction.

A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data

A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficulty, we present singleCellHaystack, a method that enables the prediction of DEGs without relying on explicit clustering of cells. Our method uses Kullback–Leibler divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a multidimensional space. Comparisons with existing DEG prediction approaches on artificial datasets show that singleCellHaystack has higher accuracy. We illustrate the usage of singleCellHaystack through applications on 136 real transcriptome datasets and a spatial transcriptomics dataset. We demonstrate that our method is a fast and accurate approach for DEG prediction in single-cell data. singleCellHaystack is implemented as an R package and is available from CRAN and GitHub.

How cell clusters are defined in single-cell sequencing data has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. Here, the authors present a new approach that enables the prediction of differentially expressed genes without relying on explicit clustering of cells.

A systematic evaluation of single-cell RNA-sequencing imputation methods

BACKGROUND

The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other.

RESULTS

Here, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms.

CONCLUSIONS

We found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.

m6A RNA methylation impacts fate choices during skin morphogenesis

N6-methyladenosine is the most prominent RNA modification in mammals. Here, we study mouse skin embryogenesis to tackle m6A's functions and physiological importance. We first landscape the m6A modifications on skin epithelial progenitor mRNAs. Contrasting with in vivo ribosomal profiling, we unearth a correlation between m6A modification in coding sequences and enhanced translation, particularly of key morphogenetic signaling pathways. Tapping physiological relevance, we show that m6A loss profoundly alters these cues and perturbs cellular fate choices and tissue architecture in all skin lineages. By single-cell transcriptomics and bioinformatics, both signaling and canonical translation pathways show significant downregulation after m6A loss. Interestingly, however, many highly m6A-modified mRNAs are markedly upregulated upon m6A loss, and they encode RNA-methylation, RNA-processing and RNA-metabolism factors. Together, our findings suggest that m6A functions to enhance translation of key morphogenetic regulators, while also destabilizing sentinel mRNAs that are primed to activate rescue pathways when m6A levels drop.

Single-Cell Transcriptome Profiling Reveals β Cell Maturation in Stem Cell-Derived Islets after Transplantation

Human pluripotent stem cells differentiated to insulin-secreting β cells (SC-β cells) in islet organoids could provide an unlimited cell source for diabetes cell replacement therapy. However, current SC-β cells generated in vitro are transcriptionally and functionally immature compared to native adult β cells. Here, we use single-cell transcriptomic profiling to catalog changes that occur in transplanted SC-β cells. We find that transplanted SC-β cells exhibit drastic transcriptional changes and mature to more closely resemble adult β cells. Insulin and IAPP protein secretions increase upon transplantation, along with expression of maturation genes lacking with differentiation in vitro, including INS, MAFA, CHGB, and G6PC2. Other differentiated cell types, such as SC-α and SC-enterochromaffin (SC-EC) cells, also exhibit large transcriptional changes. This study provides a comprehensive resource for understanding human islet cell maturation and provides important insights into maturation of SC-β cells and other SC-islet cell types to enable future differentiation strategy improvements.

A single-cell transcriptome atlas of marsupial embryogenesis and X inactivation

Single-cell RNA sequencing of embryos can resolve the transcriptional landscape of development at unprecedented resolution. To date, single-cell RNA-sequencing studies of mammalian embryos have focused exclusively on eutherian species. Analysis of mammalian outgroups has the potential to identify deeply conserved lineage specification and pluripotency factors, and can extend our understanding of X dosage compensation. Metatherian (marsupial) mammals diverged from eutherians around 160 million years ago. They exhibit distinctive developmental features, including late implantation¹ and imprinted X chromosome inactivation², which is associated with expression of the XIST-like noncoding RNA RSX³. Here we perform a single-cell RNA-sequencing analysis of embryogenesis and X chromosome inactivation in a marsupial, the grey short-tailed opossum (Monodelphis domestica). We resolve the developmental trajectory and transcriptional signatures of the epiblast, primitive endoderm and trophectoderm, and identify deeply conserved lineage-specific markers that pre-date the eutherian-marsupial divergence. RSX coating and inactivation of the X chromosome occurs early and rapidly. This observation supports the hypothesis that-in organisms with early X chromosome inactivation-imprinted X chromosome inactivation prevents biallelic X silencing. We identify XSR, an RSX antisense transcript expressed from the active X chromosome, as a candidate for the regulator of imprinted X chromosome inactivation. Our datasets provide insights into the evolution of mammalian embryogenesis and X dosage compensation.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Stromal cell diversity associated with immune evasion in human triple-negative breast cancer

The tumour stroma regulates nearly all stages of carcinogenesis. Stromal heterogeneity in human triple-negative breast cancers (TNBCs) remains poorly understood, limiting the development of stromal-targeted therapies. Single-cell RNA sequencing of five TNBCs revealed two cancer-associated fibroblast (CAF) and two perivascular-like (PVL) subpopulations. CAFs clustered into two states: the first with features of myofibroblasts and the second characterised by high expression of growth factors and immunomodulatory molecules. PVL cells clustered into two states consistent with a differentiated and immature phenotype. We showed that these stromal states have distinct morphologies, spatial relationships and functional properties in regulating the extracellular matrix. Using cell signalling predictions, we provide evidence that stromal-immune crosstalk acts via a diverse array of immunoregulatory molecules. Importantly, the investigation of gene signatures from inflammatory-CAFs and differentiated-PVL cells in independent TNBC patient cohorts revealed strong associations with cytotoxic T-cell dysfunction and exclusion, respectively. Such insights present promising candidates to further investigate for new therapeutic strategies in the treatment of TNBCs.

The P-glycoprotein repertoire of the equine parasitic nematode Parascaris univalens

P-glycoproteins (Pgp) have been proposed as contributors to the widespread macrocyclic lactone (ML) resistance in several nematode species including a major pathogen of foals, Parascaris univalens. Using new and available RNA-seq data, ten different genomic loci encoding Pgps were identified and characterized by transcriptome-guided RT-PCRs and Sanger sequencing. Phylogenetic analysis revealed an ascarid-specific Pgp lineage, Pgp-18, as well as two paralogues of Pgp-11 and Pgp-16. Comparative gene expression analyses in P. univalens and Caenorhabditis elegans show that the intestine is the major site of expression but individual gene expression patterns were not conserved between the two nematodes. In P. univalens, PunPgp-9, PunPgp-11.1 and PunPgp-16.2 consistently exhibited the highest expression level in two independent transcriptome data sets. Using RNA-Seq, no significant upregulation of any Pgp was detected following in vitro incubation of adult P. univalens with ivermectin suggesting that drug-induced upregulation is not the mechanism of Pgp-mediated ML resistance. Expression and functional analyses of PunPgp-2 and PunPgp-9 in Saccharomyces cerevisiae provide evidence for an interaction with ketoconazole and ivermectin, but not thiabendazole. Overall, this study established reliable reference gene models with significantly improved annotation for the P. univalens Pgp repertoire and provides a foundation for a better understanding of Pgp-mediated anthelmintic resistance.

Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19

Although most SARS-CoV-2-infected individuals experience mild coronavirus disease 2019 (COVID-19), some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA-seq using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyper-inflammatory signatures across all types of cells among PBMCs, particularly up-regulation of the TNF/IL-1β-driven inflammatory response as compared to severe influenza. In classical monocytes from patients with severe COVID-19, type I IFN response co-existed with the TNF/IL-1β-driven inflammation, and this was not seen in patients with milder COVID-19. Interestingly, we documented type I IFN-driven inflammatory features in patients with severe influenza as well. Based on this, we propose that the type I IFN response plays a pivotal role in exacerbating inflammation in severe COVID-19.

Computational Methods for Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures in tumor genomes. Determining the active signatures from a full repertoire of potential ones helps elucidate mechanisms of cancer development. This involves optimally decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known signatures. Here, we develop sigLASSO (a software tool at github.com/gersteinlab/siglasso) to carry out this optimization efficiently. sigLASSO has four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly factoring multinomial sampling into the objective function. This is particularly important when mutation counts are low and sampling variance is high (e.g., in exome sequencing). (2) sigLASSO uses L1 regularization to parsimoniously assign signatures, leading to sparse and interpretable solutions. (3) It fine-tunes model complexity, informed by data scale and biological priors. (4) Consequently, sigLASSO can assess model uncertainty and abstain from making assignments in low-confidence contexts.

Wnt Signaling Separates the Progenitor and Endocrine Compartments during Pancreas Development

In vitro differentiation of pluripotent cells into β cells is a promising alternative to cadaveric-islet transplantation as a cure for type 1 diabetes (T1D). During the directed differentiation of human embryonic stem cells (hESCS) by exogenous factors, numerous genes that affect the differentiation process are turned on and off autonomously. Manipulating these reactions could increase the efficiency of differentiation and provide a more complete control over the final composition of cell populations. To uncover in vitro autonomous responses, we performed single-cell RNA sequencing on hESCs as they differentiate in spherical clusters. We observed that endocrine cells and their progenitors exist beside one another in separate compartments that activate distinct genetic pathways. WNT pathway inhibition in the endocrine domain of the differentiating clusters reveals a necessary role for the WNT inhibitor APC during islet formation in vivo. Accordingly, WNT inhibition in vitro causes an increase in the proportion of differentiated endocrine cells.

Single-cell analyses identify distinct and intermediate states of zebrafish pancreatic islet development

Pancreatic endocrine islets are vital for glucose homeostasis. However, the islet developmental trajectory and its regulatory network are not well understood. To define the features of these specification and differentiation processes, we isolated individual islet cells from TgBAC(neurod1:EGFP) transgenic zebrafish and analyzed islet developmental dynamics across four different embryonic stages using a single-cell RNA-seq strategy. We identified proliferative endocrine progenitors, which could be further categorized by different cell cycle phases with the G1/S subpopulation displaying a distinct differentiation potential. We identified endocrine precursors, a heterogeneous intermediate-state population consisting of lineage-primed alpha, beta and delta cells that were characterized by the expression of lineage-specific transcription factors and relatively low expression of terminally differentiation markers. The terminally differentiated alpha, beta, and delta cells displayed stage-dependent differentiation states, which were related to their functional maturation. Our data unveiled distinct states, events and molecular features during the islet developmental transition, and provided resources to comprehensively understand the lineage hierarchy of islet development at the single-cell level.

MYC Drives Temporal Evolution of Small Cell Lung Cancer Subtypes by Reprogramming Neuroendocrine Fate

Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based on expression of ASCL1, NEUROD1, POU2F3, or YAP1. Here, we use mouse and human models with a time-series single-cell transcriptome analysis to reveal that MYC drives dynamic evolution of SCLC subtypes. In neuroendocrine cells, MYC activates Notch to dedifferentiate tumor cells, promoting a temporal shift in SCLC from ASCL1+ to NEUROD1+ to YAP1+ states. MYC alternatively promotes POU2F3+ tumors from a distinct cell type. Human SCLC exhibits intratumoral subtype heterogeneity, suggesting that this dynamic evolution occurs in patient tumors. These findings suggest that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype.

Comparison of high-throughput single-cell RNA sequencing data processing pipelines

With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

Breathing fresh air into respiratory research with single-cell RNA sequencing

The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research.