Single-cell mRNA quantification and differential analysis with Census

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.

Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

T cell subsets and functions in atherosclerosis

Atherosclerosis is a chronic inflammatory disease of the arterial wall and the primary underlying cause of cardiovascular disease. Data from in vivo imaging, cell-lineage tracing and knockout studies in mice, as well as clinical interventional studies and advanced mRNA sequencing techniques, have drawn attention to the role of T cells as critical drivers and modifiers of the pathogenesis of atherosclerosis. CD4+ T cells are commonly found in atherosclerotic plaques. A large body of evidence indicates that T helper 1 (TH1) cells have pro-atherogenic roles and regulatory T (Treg) cells have anti-atherogenic roles. However, Treg cells can become pro-atherogenic. The roles in atherosclerosis of other TH cell subsets such as TH2, TH9, TH17, TH22, follicular helper T cells and CD28null T cells, as well as other T cell subsets including CD8+ T cells and γδ T cells, are less well understood. Moreover, some T cells seem to have both pro-atherogenic and anti-atherogenic functions. In this Review, we summarize the knowledge on T cell subsets, their functions in atherosclerosis and the process of T cell homing to atherosclerotic plaques. Much of our understanding of the roles of T cells in atherosclerosis is based on findings from experimental models. Translating these findings into human disease is challenging but much needed. T cells and their specific cytokines are attractive targets for developing new preventive and therapeutic approaches including potential T cell-related therapies for atherosclerosis.

Continuous-state HMMs for modeling time-series single-cell RNA-Seq data

MOTIVATION

Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.

RESULTS

We developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single-cell datasets, we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.

AVAILABILITY AND IMPLEMENTATION

Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Coverage-dependent bias creates the appearance of binary splicing in single cells

Single-cell RNA sequencing provides powerful insight into the factors that determine each cell’s unique identity. Previous studies led to the surprising observation that alternative splicing among single cells is highly variable and follows a bimodal pattern: a given cell consistently produces either one or the other isoform for a particular splicing choice, with few cells producing both isoforms. Here, we show that this pattern arises almost entirely from technical limitations. We analyze alternative splicing in human and mouse single-cell RNA-seq datasets, and model them with a probabilistic simulator. Our simulations show that low gene expression and low capture efficiency distort the observed distribution of isoforms. This gives the appearance of binary splicing outcomes, even when the underlying reality is consistent with more than one isoform per cell. We show that accounting for the true amount of information recovered can produce biologically meaningful measurements of splicing in single cells.

Dynamics of peripheral T cell clones during PD-1 blockade in non-small cell lung cancer

Understanding of the functional states and clonal dynamics of T cells after immune checkpoint blockade (ICB) is valuable for improving these therapeutic strategies. Here we performed Smart-seq2 single-cell RNA sequencing (scRNA-seq) analysis on 3,110 peripheral T cells of non-small cell lung cancer (NSCLC) patients before and after the initiation of programmed cell death protein 1 (PD-1) blockade. We identified individual peripheral T cell clones based on the full-length T cell receptor (TCR) sequences and monitored their dynamics during immunotherapy. We found a higher cytotoxic activity in the tumor-related CD4⁺ T cell clones than in the CD8⁺ T cell clones. Based on a large tumor-related CD4⁺ T cell clone, we observed a dramatically decreased abundance after progression, as well as a reduction in the percentage of PD-1⁺ T cells. We also detected 25 genes, such as CXCR4, DUSP2 and ZFP36, that were noticeably upregulated or downregulated following progression. In addition, the pseudotime trajectory of CD8⁺ T cell clones corresponded to the treatment time points, showing a decreased activity in the "cytokine and cytokine receptor interaction" pathway. These analyses provided an insight into the dynamics of peripheral T cell clones during PD-1 blockade in NSCLC.

Heterogeneity of human pancreatic β-cells

Background

Human pancreatic β-cells are heterogeneous. This has been known for a long time and is based on various functional and morphological readouts. β-Cell heterogeneity could reflect fixed subpopulations with distinct functions. However, recent pseudotime analysis of large-scale RNA sequencing data suggest that human β-cell subpopulations may rather reflect dynamic interchangeable states characterized by low expression of genes involved in the unfolded protein response (UPR) and low insulin gene expression, low UPR and high insulin expression or high UPR and low insulin expression.

Scope of review

This review discusses findings obtained by single-cell RNA sequencing combined with pseudotime analysis that human β-cell heterogeneity represents dynamic interchangeable functional states. The physiological significance and potential implications of β-cell heterogeneity in the development and progression of diabetes is highlighted.

Major conclusions

The existence of dynamic functional states allow β-cells to transition between periods of high insulin production and UPR-mediated stress recovery. The recovery state is important since proinsulin is a misfolding-prone protein, making its biosynthesis in the endoplasmic reticulum a stressful event. The transition of β-cells between dynamic states is likely controlled at multiple levels and influenced by the microenvironment within the pancreatic islets. Disturbances in the ability of the β-cells to transition between periods of high insulin biosynthesis and UPR-mediated stress recovery may contribute to diabetes development. Diabetes medications that restore the ability of the β-cells to transition between the functional states should be considered.

Trajectory mapping of human embryonic stem cell cardiogenesis reveals lineage branch points and an ISL1 progenitor-derived cardiac fibroblast lineage

A family of multipotent heart progenitors plays a central role in the generation of diverse myogenic and nonmyogenic lineages in the heart. Cardiac progenitors in particular play a significant role in lineages involved in disease, and have also emerged to be a strong therapeutic candidate. Based on this premise, we aimed to deeply characterize the progenitor stage of cardiac differentiation at a single-cell resolution. Integrated comparison with an embryonic 5-week human heart transcriptomic dataset validated lineage identities with their late stage in vitro counterparts, highlighting the relevance of an in vitro differentiation for progenitors that are developmentally too early to be accessed in vivo. We utilized trajectory mapping to elucidate progenitor lineage branching points, which are supported by RNA velocity. Nonmyogenic populations, including cardiac fibroblast-like cells and endoderm, were found, and we identified TGFBI as a candidate marker for human cardiac fibroblasts in vivo and in vitro. Both myogenic and nonmyogenic populations express ISL1, and its loss redirected myogenic progenitors into a neural-like fate. Our study provides important insights into processes during early heart development.

Unifying developmental programs for embryonic and postembryonic neurogenesis in the zebrafish retina

The zebrafish retina grows for a lifetime. Whether embryonic and postembryonic retinogenesis conform to the same developmental program is an outstanding question that remains under debate. Using single-cell RNA sequencing of ∼20,000 cells of the developing zebrafish retina at four different stages, we identified seven distinct developmental states. Each state explicitly expresses a gene set. Disruption of individual state-specific marker genes results in various defects ranging from small eyes to the loss of distinct retinal cell types. Using a similar approach, we further characterized the developmental states of postembryonic retinal stem cells (RSCs) and their progeny in the ciliary marginal zone. Expression pattern analysis of state-specific marker genes showed that the developmental states of postembryonic RSCs largely recapitulated those of their embryonic counterparts, except for some differences in rod photoreceptor genesis. Thus, our findings reveal the unifying developmental program used by the embryonic and postembryonic retinogenesis in zebrafish.

Unsupervised generative and graph representation learning for modelling cell differentiation

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies.

Temporal specificity and heterogeneity of Drosophila immune cells

Immune cells provide defense against non-self and have recently been shown to also play key roles in diverse processes such as development, metabolism, and tumor progression. The heterogeneity of Drosophila immune cells (hemocytes) remains an open question. Using bulk RNA sequencing, we find that the hemocytes display distinct features in the embryo, a closed and rapidly developing system, compared to the larva, which is exposed to environmental and metabolic challenges. Through single-cell RNA sequencing, we identify fourteen hemocyte clusters present in unchallenged larvae and associated with distinct processes, e.g., proliferation, phagocytosis, metabolic homeostasis, and humoral response. Finally, we characterize the changes occurring in the hemocyte clusters upon wasp infestation, which triggers the differentiation of a novel hemocyte type, the lamellocyte. This first molecular atlas of hemocytes provides insights and paves the way to study the biology of the Drosophila immune cells in physiological and pathological conditions.

Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers

The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group “exons” that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.

Alternative splicing is a major mechanism for generating transcriptome diversity. However, few published scRNA-seq studies have investigated alternative splicing, and even when studied, methods developed for bulk RNA-seq were utilized. Compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Methods developed for bulk RNA-seq may not be optimal when analyzing data generated from scRNA-seq experiments. To fill in this gap, we developed SCATS, an open-source software package, which allows analysis of scRNA-seq data with or without Unique Molecular Identifiers (UMIs). SCATS is able to detect splicing events even when sequencing depth is low. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle differences across cortical cell types than Census and DEXSeq. Additionally, SCATS accurately characterized splicing heterogeneity across cortical cell types, which was further confirmed by qRT-PCR measurements. Our study highlights the benefit of SCATS for elucidating splicing heterogeneity across cells in scRNA-seq data.

Effect of intra- and inter-tumoral heterogeneity on molecular characteristics of primary IDH-wild type glioblastoma revealed by single-cell analysis

Aims

To reveal the effects of intra‐ and inter‐tumoral heterogeneity on characteristics of primary IDH‐wild type glioblastoma cells.

Methods

Single‐cell RNA‐seq data were acquired from the GEO database, and bulk sample transcriptome data were downloaded from the TCGA database with clinical information. Neoplastic subtype and glioma stem‐like cells (GSCs) were identified by matching 5000 random virtual samples based on ssGSEA. CNV was inferred to compare the heterogeneity among patients and subtypes by infercnv. Transition direction was inferred by RNA velocity, and lineage trajectory was inferred by monocle. Regulon network of cells was analyzed by SCENIC, and cell communication was identified by CellPhoneDB.

Results

Glioblastoma (GBM) cells could be divided into four subtypes by Verhaak classifier. However, classification of three subtypes (except NE subtype) was more suitable for GBM cells, and Verhaak classifier has difficulty in distinguishing GSCs. GBM heterogeneity and GBM cells’ regulon network were mainly influenced by inter‐tumoral heterogeneity. Within the same patient, different subclones exist in the same subtype of cells whose transition direction could be predicted by regulon similarity. Apart from inter‐tumoral heterogeneity, different subtype of cells share common subtype‐specific cell‐cell communications.

Conclusions

Inter‐tumoral heterogeneity contributes mainly to GBM heterogeneity and cell molecular characteristics. However, the same subtype of cells shared cell communication similarities.

High-throughput, microscope-based sorting to dissect cellular heterogeneity

Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.

Differences in Tumor Microenvironment Dictate T Helper Lineage Polarization and Response to Immune Checkpoint Therapy

Immune checkpoint therapy (ICT) shows encouraging results in a subset of patients with metastatic castration-resistant prostate cancer (mCRPC) but still elicits a sub-optimal response among those with bone metastases. Analysis of patients' bone marrow samples revealed increased T_h17 instead of T_h1 subsets after ICT. To further evaluate the different tumor microenvironments, we injected mice with prostate tumor cells either subcutaneously or intraosseously. ICT in the subcutaneous CRPC model significantly increases intra-tumoral T_h1 subsets and improves survival. However, ICT fails to elicit an anti-tumor response in the bone CRPC model despite an increase in the intra-tumoral CD4 T cells, which are polarized to T_h17 rather than T_h1 lineage. Mechanistically, tumors in the bone promote osteoclast-mediated bone resorption that releases TGF-β, which restrains T_h1 lineage development. Blocking TGF-β along with ICT increases T_h1 subsets and promotes clonal expansion of CD8 T cells and subsequent regression of bone CRPC and improves survival.

Data-based RNA-seq simulations by binomial thinning

BACKGROUND

With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance.

RESULTS

Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets.

CONCLUSIONS

Using data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.

Redundant and additive functions of the four Lef/Tcf transcription factors in lung epithelial progenitors

In multicellular organisms, paralogs from gene duplication survive purifying selection by evolving tissue-specific expression and function. Whether this genetic redundancy is also selected for within a single cell type is unclear for multimember paralogs, as exemplified by the four obligatory Lef/Tcf transcription factors of canonical Wnt signaling, mainly due to the complex genetics involved. Using the developing mouse lung as a model system, we generate two quadruple conditional knockouts, four triple mutants, and various combinations of double mutants, showing that the four Lef/Tcf genes function redundantly in the presence of at least two Lef/Tcf paralogs, but additively upon losing additional paralogs to specify and maintain lung epithelial progenitors. Prelung-specification, pan-epithelial double knockouts have no lung phenotype; triple knockouts have varying phenotypes, including defective branching and tracheoesophageal fistulas; and the quadruple knockout barely forms a lung, resembling the Ctnnb1 mutant. Postlung-specification deletion of all four Lef/Tcf genes leads to branching defects, down-regulation of progenitor genes, premature alveolar differentiation, and derepression of gastrointestinal genes, again phenocopying the corresponding Ctnnb1 mutant. Our study supports a monotonic, positive signaling relationship between CTNNB1 and Lef/Tcf in lung epithelial progenitors as opposed to reported repressor functions of Lef/Tcf, and represents a thorough in vivo analysis of cell-type-specific genetic redundancy among the four Lef/Tcf paralogs.

Vascularized human cortical organoids (vOrganoids) model cortical development in vivo

Modeling the processes of neuronal progenitor proliferation and differentiation to produce mature cortical neuron subtypes is essential for the study of human brain development and the search for potential cell therapies. We demonstrated a novel paradigm for the generation of vascularized organoids (vOrganoids) consisting of typical human cortical cell types and a vascular structure for over 200 days as a vascularized and functional brain organoid model. The observation of spontaneous excitatory postsynaptic currents (sEPSCs), spontaneous inhibitory postsynaptic currents (sIPSCs), and bidirectional electrical transmission indicated the presence of chemical and electrical synapses in vOrganoids. More importantly, single-cell RNA-sequencing analysis illustrated that vOrganoids exhibited robust neurogenesis and that cells of vOrganoids differentially expressed genes (DEGs) related to blood vessel morphogenesis. The transplantation of vOrganoids into the mouse S1 cortex resulted in the construction of functional human-mouse blood vessels in the grafts that promoted cell survival in the grafts. This vOrganoid culture method could not only serve as a model to study human cortical development and explore brain disease pathology but also provide potential prospects for new cell therapies for nervous system disorders and injury.

This study establishes a method to generate vascularized cortical organoids. This shows that in addition to reducing hypoxia and cell death, the vascular system promotes neural development in organoids. When transplanting these organoids into host mice, a graft-host vascular system could be reconstructed.

APEC: an accesson-based method for single-cell chromatin accessibility analysis

The development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each cell by groups of accessible regions with synergistic signal patterns termed “accessons”. This python-based package greatly improves the accuracy of unsupervised single-cell clustering for many public datasets. It also predicts gene expression, identifies enriched motifs, discovers super-enhancers, and projects pseudotime trajectories. APEC is available at https://github.com/QuKunLab/APEC.

Zonation of Ribosomal DNA Transcription Defines a Stem Cell Hierarchy in Colorectal Cancer

Colorectal cancers (CRCs) are composed of an amalgam of cells with distinct genotypes and phenotypes. Here, we reveal a previously unappreciated heterogeneity in the biosynthetic capacities of CRC cells. We discover that the majority of ribosomal DNA transcription and protein synthesis in CRCs occurs in a limited subset of tumor cells that localize in defined niches. The rest of the tumor cells undergo an irreversible loss of their biosynthetic capacities as a consequence of differentiation. Cancer cells within the biosynthetic domains are characterized by elevated levels of the RNA polymerase I subunit A (POLR1A). Genetic ablation of POLR1A-high cell population imposes an irreversible growth arrest on CRCs. We show that elevated biosynthesis defines stemness in both LGR5⁺ and LGR5^- tumor cells. Therefore, a common architecture in CRCs is a simple cell hierarchy based on the differential capacity to transcribe ribosomal DNA and synthesize proteins.

PIRs mediate innate myeloid cell memory to nonself MHC molecules

Immunological memory specific to previously encountered antigens is a cardinal feature of adaptive lymphoid cells. However, it is unknown whether innate myeloid cells retain memory of prior antigenic stimulation and respond to it more vigorously on subsequent encounters. In this work, we show that murine monocytes and macrophages acquire memory specific to major histocompatibility complex I (MHC-I) antigens, and we identify A-type paired immunoglobulin-like receptors (PIR-As) as the MHC-I receptors necessary for the memory response. We demonstrate that deleting PIR-A in the recipient or blocking PIR-A binding to donor MHC-I molecules blocks memory and attenuates kidney and heart allograft rejection. Thus, innate myeloid cells acquire alloantigen-specific memory that can be targeted to improve transplant outcomes.

A single-cell transcriptomic landscape of primate arterial aging

Our understanding of how aging affects the cellular and molecular components of the vasculature and contributes to cardiovascular diseases is still limited. Here we report a single-cell transcriptomic survey of aortas and coronary arteries in young and old cynomolgus monkeys. Our data define the molecular signatures of specialized arteries and identify eight markers discriminating aortic and coronary vasculatures. Gene network analyses characterize transcriptional landmarks that regulate vascular senility and position FOXO3A, a longevity-associated transcription factor, as a master regulator gene that is downregulated in six subtypes of monkey vascular cells during aging. Targeted inactivation of FOXO3A in human vascular endothelial cells recapitulates the major phenotypic defects observed in aged monkey arteries, verifying FOXO3A loss as a key driver for arterial endothelial aging. Our study provides a critical resource for understanding the principles underlying primate arterial aging and contributes important clues to future treatment of age-associated vascular disorders.

Prognostic Molecular Classification of Appendiceal Mucinous Neoplasms Treated with Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy

BACKGROUND

Appendiceal mucinous neoplasm (AMN) with peritoneal metastasis is a rare but deadly disease with few prognostic or therapy-predictive biomarkers to guide treatment decisions. Here, we investigated the prognostic and biological attributes of gene expression-based AMN molecular subtypes.

METHODS

AMN specimens (n = 138) derived from a population-based subseries of patients treated at our institution with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) between 05/2000 and 05/2013 were analyzed for gene expression using a custom-designed NanoString 148-gene panel. Signed non-negative matrix factorization (sNMF) was used to define a gene signature capable of delineating robustly-classified AMN molecular subtypes. The sNMF class assignments were evaluated by topology learning, reverse-graph embedding and cross-cohort performance analysis.

RESULTS

Three molecular subtypes of AMN were discerned by the expression patterns of 17 genes with roles in cancer progression or anti-tumor immunity. Tumor subtype assignments were confirmed by topology learning. AMN subtypes were termed immune-enriched (IE), oncogene-enriched (OE) and mixed (M) as evidenced by their gene expression patterns, and exhibited significantly different post-treatment survival outcomes. Genes with specialized immune functions, including markers of T-cells, natural killer cells, B-cells, and cytolytic activity showed increased expression in the low-risk IE subtype, while genes implicated in the promotion of cancer growth and progression were more highly expressed in the high-risk OE subtype. In multivariate analysis, the subtypes demonstrated independent prediction power for post-treatment survival.

CONCLUSIONS

Our findings suggest a greater role for the immune system in AMN than previously recognized. AMN subtypes may have clinical utility for predicting CRS/HIPEC treatment outcomes.

Dissecting Cell Lineage Specification and Sex Fate Determination in Gonadal Somatic Cells Using Single-Cell Transcriptomics

Sex determination is a unique process that allows the study of multipotent progenitors and their acquisition of sex-specific fates during differentiation of the gonad into a testis or an ovary. Using time series single-cell RNA sequencing (scRNA-seq) on ovarian Nr5a1-GFP⁺ somatic cells during sex determination, we identified a single population of early progenitors giving rise to both pre-granulosa cells and potential steroidogenic precursor cells. By comparing time series single-cell RNA sequencing of XX and XY somatic cells, we provide evidence that gonadal supporting cells are specified from these early progenitors by a non-sex-specific transcriptomic program before pre-granulosa and Sertoli cells acquire their sex-specific identity. In XX and XY steroidogenic precursors, similar transcriptomic profiles underlie the acquisition of cell fate but with XX cells exhibiting a relative delay. Our data provide an important resource, at single-cell resolution, for further interrogation of the molecular and cellular basis of mammalian sex determination.

Novel specialized cell state and spatial compartments within the germinal center

Within germinal centers (GCs), complex and highly orchestrated molecular programs must balance proliferation, somatic hypermutation (SHM) and selection to both provide effective humoral immunity and to protect against genomic instability and neoplastic transformation. In contrast to this complexity, GC B cells are canonically divided into two principal populations, dark zone (DZ) and light zone (LZ) cells. We now demonstrate that following selection in the LZ, B cells migrated to specialized sites within the canonical DZ that contained tingible body macrophages (TBMs) and were sites of ongoing cell division. Proliferating DZ (DZp) cells then transited into the larger DZ to become differentiating DZ (DZd) cells before re-entering the LZ. Multidimensional analysis revealed distinct molecular programs in each population commensurate with observed compartmentization of non-compatable functions. These data provide a new three-cell population model that both orders critical GC functions and reveals essential molecular programs of humoral adaptive immunity.

Single Cell RNA Sequencing in Atherosclerosis Research

Technological advances in characterizing molecular heterogeneity at the single cell level have ushered in a deeper understanding of the biological diversity of cells present in tissues including atherosclerotic plaques. New subsets of cells have been discovered among cell types previously considered homogenous. The commercial availability of systems to obtain transcriptomes and matching surface phenotypes from thousands of single cells is rapidly changing our understanding of cell types and lineage identity. Emerging methods to infer cellular functions are beginning to shed new light on the interplay of components involved in multifaceted disease responses, like atherosclerosis. Here, we provide a technical guide for design, implementation, assembly, and interpretations of current single cell transcriptomics approaches from the perspective of employing these tools for advancing cardiovascular disease research.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Beta Cell Dedifferentiation Induced by IRE1α Deletion Prevents Type 1 Diabetes

Immune-mediated destruction of insulin-producing β cells causes type 1 diabetes (T1D). However, how β cells participate in their own destruction during the disease process is poorly understood. Here, we report that modulating the unfolded protein response (UPR) in β cells of non-obese diabetic (NOD) mice by deleting the UPR sensor IRE1α prior to insulitis induced a transient dedifferentiation of β cells, resulting in substantially reduced islet immune cell infiltration and β cell apoptosis. Single-cell and whole-islet transcriptomics analyses of immature β cells revealed remarkably diminished expression of β cell autoantigens and MHC class I components, and upregulation of immune inhibitory markers. IRE1α-deficient mice exhibited significantly fewer cytotoxic CD8⁺ T cells in their pancreata, and adoptive transfer of their total T cells did not induce diabetes in Rag1^-/- mice. Our results indicate that inducing β cell dedifferentiation, prior to insulitis, allows these cells to escape immune-mediated destruction and may be used as a novel preventive strategy for T1D in high-risk individuals.

Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis

In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

Functionally heterogeneous human satellite cells identified by single cell RNA sequencing

Although heterogeneity is recognized within the murine satellite cell pool, a comprehensive understanding of distinct subpopulations and their functional relevance in human satellite cells is lacking. We used a combination of single cell RNA sequencing and flow cytometry to identify, distinguish, and physically separate novel subpopulations of human PAX7+ satellite cells (Hu-MuSCs) from normal muscles. We found that, although relatively homogeneous compared to activated satellite cells and committed progenitors, the Hu-MuSC pool contains clusters of transcriptionally distinct cells with consistency across human individuals. New surface marker combinations were enriched in transcriptional subclusters, including a subpopulation of Hu-MuSCs marked by CXCR4/CD29/CD56/CAV1 (CAV1+). In vitro, CAV1+ Hu-MuSCs are morphologically distinct, and characterized by resistance to activation compared to CAV1- Hu-MuSCs. In vivo, CAV1+ Hu-MuSCs demonstrated increased engraftment after transplantation. Our findings provide a comprehensive transcriptional view of normal Hu-MuSCs and describe new heterogeneity, enabling separation of functionally distinct human satellite cell subpopulations.

Single Cell Analysis in Vascular Biology

The ability to quantify DNA, RNA, and protein variations at the single cell level has revolutionized our understanding of cellular heterogeneity within tissues. Via such analyses, individual cells within populations previously thought to be homogeneous can now be delineated into specific subpopulations expressing unique sets of genes, enabling specialized functions. In vascular biology, studies using single cell RNA sequencing have revealed extensive heterogeneity among endothelial and mural cells even within the same vessel, key intermediate cell types that arise during blood and lymphatic vessel development, and cell-type specific responses to disease. Thus, emerging new single cell analysis techniques are enabling vascular biologists to elucidate mechanisms of vascular development, homeostasis, and disease that were previously not possible. In this review, we will provide an overview of single cell analysis methods and highlight recent advances in vascular biology made possible through single cell RNA sequencing.

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

A single cell transcriptome atlas of the developing zebrafish hindbrain

Segmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at boundaries and segment centres, in part mediated by Fgf20 signalling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

Single-Cell Transcriptomic Analysis

Single-cell sequencing measures the sequence information from individual cells using optimized single-cell isolation protocols and next-generation sequencing technologies. Recent advancement in single-cell sequencing has transformed biomedical research, providing insights into diverse biological processes such as mammalian development, immune system function, cellular diversity and heterogeneity, and disease pathogenesis. In this article, we introduce and describe popular commercial platforms for single-cell RNA sequencing, general workflow for data analysis, repositories and databases, and applications for these approaches in biomedical research. © 2020 American Physiological Society. Compr Physiol 10:767-783, 2020.

Histone variant dictates fate biasing of neural crest cells to melanocyte lineage

In the neural crest lineage, progressive fate restriction and stem cell assignment are crucial for both development and regeneration. Whereas fate commitment events have distinct transcriptional footprints, fate biasing is often transitory and metastable, and is thought to be moulded by epigenetic programmes. Therefore, the molecular basis of specification is difficult to define. In this study, we established a role for a histone variant, H2a.z.2, in specification of the melanocyte lineage from multipotent neural crest cells. H2a.z.2 silencing reduces the number of melanocyte precursors in developing zebrafish embryos and from mouse embryonic stem cells in vitro We demonstrate that this histone variant occupies nucleosomes in the promoter of the key melanocyte determinant mitf, and enhances its induction. CRISPR/Cas9-based targeted mutagenesis of this gene in zebrafish drastically reduces adult melanocytes, as well as their regeneration. Thereby, our study establishes the role of a histone variant upstream of the core gene regulatory network in the neural crest lineage. This epigenetic mark is a key determinant of cell fate and facilitates gene activation by external instructive signals, thereby establishing melanocyte fate identity.

Time-lapse single-cell transcriptomics reveals modulation of histone H3 for dormancy breaking in fission yeast

How quiescent cells break dormancy is a key issue in eukaryotic cells including cancer. Fungal spores, for example, remain quiescent for long periods until nourished, although the mechanisms by which dormancy is broken remain enigmatic. Transcriptome analysis could provide a clue, but methods to synchronously germinate large numbers of spores are lacking, and thus it remains a challenge to analyse gene expression upon germination. Hence, we develop methods to assemble transcriptomes from individual, asynchronous spore cells of fission yeast undergoing germination to assess transcriptomic changes over time. The virtual time-lapse analyses highlights one of three copies of histone H3 genes whose transcription fluctuates during the initial stage of germination. Disruption of this temporal fluctuation causes defects in spore germination despite no visible defects in other stages of the life cycle. We conclude that modulation of histone H3 expression is a crucial 'wake-up' trigger at dormancy breaking.

Single-cell RNA sequencing identifies novel cell types in Drosophila blood

Drosophila has been extensively used to model the human blood-immune system, as both systems share many developmental and immune response mechanisms. However, while many human blood cell types have been identified, only three were found in flies: plasmatocytes, crystal cells and lamellocytes. To better understand the complexity of fly blood system, we used single-cell RNA sequencing technology to generate comprehensive gene expression profiles for Drosophila circulating blood cells. In addition to the known cell types, we identified two new Drosophila blood cell types: thanacytes and primocytes. Thanacytes, which express many stimulus response genes, are involved in distinct responses to different types of bacteria. Primocytes, which express cell fate commitment and signaling genes, appear to be involved in keeping stem cells in the circulating blood. Furthermore, our data revealed four novel plasmatocyte subtypes (Ppn⁺, CAH7⁺, Lsp⁺ and reservoir plasmatocytes), each with unique molecular identities and distinct predicted functions. We also identified cross-species markers from Drosophila hemocytes to human blood cells. Our analysis unveiled a more complex Drosophila blood system and broadened the scope of using Drosophila to model human blood system in development and disease.

CD8+ T-cell plasticity regulates vascular regeneration in type-2 diabetes

In this study, we observe that the ischemic tissues of type-2 diabetic (T2D) patients and mice have significantly more CD8⁺ T-cells than that of their normoglycemic counterparts, respectively. However, the role of CD8⁺ T-cells in the pathogenesis of diabetic vascular complication has been less studied. Methods: We employed loss-of-function studies in mouse models using the non-lytic anti-CD8 antibody that blocks tissue infiltration of CD8⁺ T-cells into the injured tissue. We also performed genome-wide, single-cell RNA-sequencing of CD8⁺ T-cells to uncover their role in the pathogenesis of diabetic vascular diseases. Results: The vascular density is negatively correlated with the number of CD8⁺ T-cells in the ischemic tissues of patients and mice after injury. CD8⁺ T-cells or their supernatant can directly impair human and murine angiogenesis. Compared to normoglycemic mice that can regenerate their blood vessels after injury, T2D mice fail in this regeneration. Treatment with the CD8 checkpoint blocking antibody increases the proliferation and function of endothelial cells in both Lepr^db/db mice and diet-induced diabetic Cdh5-Cre;Rosa-YFP lineage-tracing mice after ischemic injury. Furthermore, single-cell transcriptomic profiling reveals that CD8⁺ T-cells of T2D mice showed a de novo cell fate change from the angiogenic, tissue-resident memory cells towards the effector and effector memory cells after injury. Functional revascularization by CD8 checkpoint blockade is mediated through unleashing such a poised lineage commitment of CD8⁺ T-cells from T2D mice. Conclusion: Our results reveal that CD8⁺ T-cell plasticity regulates vascular regeneration; and give clinically relevant insights into the potential development of immunotherapy targeting vascular diseases associated with obesity and diabetes.

Pbx4 limits heart size and fosters arch artery formation by partitioning second heart field progenitors and restricting proliferation

Vertebrate heart development requires the integration of temporally distinct differentiating progenitors. However, few signals are understood that restrict the size of the later-differentiating outflow tract (OFT). We show that improper specification and proliferation of second heart field (SHF) progenitors in zebrafish lazarus (lzr) mutants, which lack the transcription factor Pbx4, produces enlarged hearts owing to an increase in ventricular and smooth muscle cells. Specifically, Pbx4 initially promotes the partitioning of the SHF into anterior progenitors, which contribute to the OFT, and adjacent endothelial cell progenitors, which contribute to posterior pharyngeal arches. Subsequently, Pbx4 limits SHF progenitor (SHFP) proliferation. Single cell RNA sequencing of nkx2.5+ cells revealed previously unappreciated distinct differentiation states and progenitor subpopulations that normally reside within the SHF and arterial pole of the heart. Specifically, the transcriptional profiles of Pbx4-deficient nkx2.5+ SHFPs are less distinct and display characteristics of normally discrete proliferative progenitor and anterior, differentiated cardiomyocyte populations. Therefore, our data indicate that the generation of proper OFT size and arch arteries requires Pbx-dependent stratification of unique differentiation states to facilitate both homeotic-like transformations and limit progenitor production within the SHF.