Single-cell analysis of lymphatic endothelial cell fate specification and differentiation during zebrafish development.

During development, the lymphatic vasculature forms as a second network derived chiefly from blood vessels. The transdifferentiation of embryonic venous endothelial cells (VECs) into lymphatic endothelial cells (LECs) is a key step in this process. Specification, differentiation and maintenance of LEC fate are all driven by the transcription factor Prox1, yet the downstream mechanisms remain to be elucidated. We here present a single-cell transcriptomic atlas of lymphangiogenesis in zebrafish, revealing new markers and hallmarks of LEC differentiation over four developmental stages. We further profile single-cell transcriptomic and chromatin accessibility changes in zygotic prox1a mutants that are undergoing a LEC-VEC fate shift. Using maternal and zygotic prox1a/prox1b mutants, we determine the earliest transcriptomic changes directed by Prox1 during LEC specification. This work altogether reveals new downstream targets and regulatory regions of the genome controlled by Prox1 and presents evidence that Prox1 specifies LEC fate primarily by limiting blood vascular and haematopoietic fate. This extensive single-cell resource provides new mechanistic insights into the enigmatic role of Prox1 and the control of LEC differentiation in development.

High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma.

INTRODUCTION: Primary open angle glaucoma (POAG) is a leading cause of blindness globally. Characterized by progressive retinal ganglion cell degeneration, the precise pathogenesis remains unknown. Genome-wide association studies (GWAS) have uncovered many genetic variants associated with elevated intraocular pressure (IOP), one of the key risk factors for POAG. We aimed to identify genetic and morphological variation that can be attributed to trabecular meshwork cell (TMC) dysfunction and raised IOP in POAG. METHODS: 62 genes across 55 loci were knocked-out in a primary human TMC line. Each knockout group, including five non-targeting control groups, underwent single-cell RNA-sequencing (scRNA-seq) for differentially-expressed gene (DEG) analysis. Multiplexed fluorescence coupled with CellProfiler image analysis allowed for single-cell morphological profiling. RESULTS: Many gene knockouts invoked DEGs relating to matrix metalloproteinases and interferon-induced proteins. We have prioritized genes at four loci of interest to identify gene knockouts that may contribute to the pathogenesis of POAG, including ANGPTL2, LMX1B, CAV1, and KREMEN1. Three genetic networks of gene knockouts with similar transcriptomic profiles were identified, suggesting a synergistic function in trabecular meshwork cell physiology. TEK knockout caused significant upregulation of nuclear granularity on morphological analysis, while knockout of TRIOBP, TMCO1 and PLEKHA7 increased granularity and intensity of actin and the cell-membrane. CONCLUSION: High-throughput analysis of cellular structure and function through multiplex fluorescent single-cell analysis and scRNA-seq assays enabled the direct study of genetic perturbations at the single-cell resolution. This work provides a framework for investigating the role of genes in the pathogenesis of glaucoma and heterogenous diseases with a strong genetic basis.

Correction: Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images.

[This corrects the article DOI: 10.1371/journal.pgen.1009497.].

Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images.

Optical Coherence Tomography (OCT) enables non-invasive imaging of the retina and is used to diagnose and manage ophthalmic diseases including glaucoma. We present the first large-scale genome-wide association study of inner retinal morphology using phenotypes derived from OCT images of 31,434 UK Biobank participants. We identify 46 loci associated with thickness of the retinal nerve fibre layer or ganglion cell inner plexiform layer. Only one of these loci has been associated with glaucoma, and despite its clear role as a biomarker for the disease, Mendelian randomisation does not support inner retinal thickness being on the same genetic causal pathway as glaucoma. We extracted overall retinal thickness at the fovea, representative of foveal hypoplasia, with which three of the 46 SNPs were associated. We additionally associate these three loci with visual acuity. In contrast to the Mendelian causes of severe foveal hypoplasia, our results suggest a spectrum of foveal hypoplasia, in part genetically determined, with consequences on visual function.

A single-cell transcriptome atlas of the adult human retina.

The retina is a specialized neural tissue that senses light and initiates image processing. Although the functional organization of specific retina cells has been well studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile the human retina, we performed single-cell RNA sequencing on 20,009 cells from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct cell populations representing all known neural retinal cells: rod photoreceptors, cone photoreceptors, Müller glia, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, astrocytes, and microglia. Our data captured molecular profiles for healthy and putative early degenerating rod photoreceptors, and revealed the loss of MALAT1 expression with longer post-mortem time, which potentially suggested a novel role of MALAT1 in rod photoreceptor degeneration. We have demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to understanding retinal biology and disease.

Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations.

Heterogeneity of cell states represented in pluripotent cultures has not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC-CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method and, through this, identified four subpopulations distinguishable on the basis of their pluripotent state, including a core pluripotent population (48.3%), proliferative (47.8%), early primed for differentiation (2.8%), and late primed for differentiation (1.1%). For each subpopulation, we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four transcriptionally distinct predictor gene sets composed of 165 unique genes that denote the specific pluripotency states; using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to threefold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations and support our conclusions with results from two orthogonal pseudotime trajectory methods.

Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods.

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets-droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease.

The human immune system displays substantial variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single-cell RNA sequencing (scRNA-seq) data from 1,267,758 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTLs, with most showing cell type-specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells that are transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to interindividual variation in immune function. Finally, using a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of interindividual variation in the immune system.

Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration.

There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.

Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma.

To assess the transcriptomic profile of disease-specific cell populations, fibroblasts from patients with primary open-angle glaucoma (POAG) were reprogrammed into induced pluripotent stem cells (iPSCs) before being differentiated into retinal organoids and compared with those from healthy individuals. We performed single-cell RNA sequencing of a total of 247,520 cells and identified cluster-specific molecular signatures. Comparing the gene expression profile between cases and controls, we identified novel genetic associations for this blinding disease. Expression quantitative trait mapping identified a total of 4,443 significant loci across all cell types, 312 of which are specific to the retinal ganglion cell subpopulations, which ultimately degenerate in POAG. Transcriptome-wide association analysis identified genes at loci previously associated with POAG, and analysis, conditional on disease status, implicated 97 statistically significant retinal ganglion cell-specific expression quantitative trait loci. This work highlights the power of large-scale iPSC studies to uncover context-specific profiles for a genetically complex disease.

scGPS: Determining Cell States and Global Fate Potential of Subpopulations.

Finding cell states and their transcriptional relatedness is a main outcome from analysing single-cell data. In developmental biology, determining whether cells are related in a differentiation lineage remains a major challenge. A seamless analysis pipeline from cell clustering to estimating the probability of transitions between cell clusters is lacking. Here, we present Single Cell Global fate Potential of Subpopulations (scGPS) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more samples into clusters (SCORE algorithm) and estimates pairwise transitioning potential (scGPS algorithm) of any pair of clusters. SCORE allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. scGPS implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. scGPS also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied scGPS in diverse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. scGPS is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in R with optimised functions using C++ and is publicly available in Bioconductor.

The relationship between adrenocortical candidate gene expression and clinical response to hydrocortisone in patients with septic shock.

PURPOSE: To determine if adrenocortical gene expression is associated with clinical outcomes or response to corticosteroid treatment in septic shock. METHODS: A pre-specified nested cohort study of a randomised controlled trial of hydrocortisone compared to placebo in septic shock. Blood was collected for RNAseq analysis prior to treatment with hydrocortisone or placebo. The expression of adrenocortical candidate genes related to pituitary releasing hormones, mineralocorticoid and glucocorticoid receptors, intracellular glucocorticoid metabolism and transport proteins was measured. RESULTS: From May 2014 to April 2017, 671 patients were enrolled in the nested cohort study, from which 494 samples were available for analysis. We found no evidence of an association between candidate gene expression levels and either 90-day mortality, 28-day mortality or time to shock reversal. We observed evidence of a significant interaction between expression and treatment group for time to shock reversal in two genes; GLCCI1 (HR 3.81, 95%CI 0.57-25.47 vs. HR 0.64, 95%CI 0.13-3.07 for hydrocortisone and placebo respectively, p for interaction 0.008) and BHSD1 (HR 0.55, 95%CI 0.28-1.09 vs. HR 1.32 95%CI 0.67-2.60, p for interaction 0.01). CONCLUSIONS: In patients with septic shock, there is no association between adrenocortical candidate gene expression and mortality. Patients with higher expression of GLCCI1 who received hydrocortisone achieved shock resolution faster than those receiving placebo; conversely, patients who had higher expression of BHSD1 who received hydrocortisone achieved shock resolution slower than those who received placebo. Variation in gene expression may be a mechanism for heterogeneity of treatment response to corticosteroids in septic shock.

Transcriptomic Profiling of Human Pluripotent Stem Cell-derived Retinal Pigment Epithelium over Time.

Human pluripotent stem cell (hPSC)-derived progenies are immature versions of cells, presenting a potential limitation to the accurate modelling of diseases associated with maturity or age. Hence, it is important to characterise how closely cells used in culture resemble their native counterparts. In order to select appropriate time points of retinal pigment epithelium (RPE) cultures that reflect native counterparts, we characterised the transcriptomic profiles of the hPSC-derived RPE cells from 1- and 12-month cultures. We differentiated the human embryonic stem cell line H9 into RPE cells, performed single-cell RNA-sequencing of a total of 16,576 cells to assess the molecular changes of the RPE cells across these two culture time points. Our results indicate the stability of the RPE transcriptomic signature, with no evidence of an epithelial-mesenchymal transition, and with the maturing populations of the RPE observed with time in culture. Assessment of Gene Ontology pathways revealed that as the cultures age, RPE cells upregulate expression of genes involved in metal binding and antioxidant functions. This might reflect an increased ability to handle oxidative stress as cells mature. Comparison with native human RPE data confirms a maturing transcriptional profile of RPE cells in culture. These results suggest that long-term in vitro culture of RPE cells allows the modelling of specific phenotypes observed in native mature tissues. Our work highlights the transcriptional landscape of hPSC-derived RPE cells as they age in culture, which provides a reference for native and patient samples to be benchmarked against.

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells.

BACKGROUND: The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. RESULTS: Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. CONCLUSIONS: This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing.

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3' libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

ascend: R package for analysis of single-cell RNA-seq data.

BACKGROUND: Recent developments in single-cell RNA sequencing (scRNA-seq) platforms have vastly increased the number of cells typically assayed in an experiment. Analysis of scRNA-seq data is multidisciplinary in nature, requiring careful consideration of the application of statistical methods with respect to the underlying biology. Few analysis packages exist that are at once robust, are computationally fast, and allow flexible integration with other bioinformatics tools and methods. FINDINGS: ascend is an R package comprising tools designed to simplify and streamline the preliminary analysis of scRNA-seq data, while addressing the statistical challenges of scRNA-seq analysis and enabling flexible integration with genomics packages and native R functions, including fast parallel computation and efficient memory management. The package incorporates both novel and established methods to provide a framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and a wide range of visualization functions. CONCLUSIONS: ascend is designed to work with scRNA-seq data generated by any high-throughput platform and includes functions to convert data objects between software packages. The ascend workflow is simple and interactive, as well as suitable for implementation by a broad range of users, including those with little programming experience.

Genotype-free demultiplexing of pooled single-cell RNA-seq.

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit.

Detection of HPV E7 Transcription at Single-Cell Resolution in Epidermis.

Persistent human papillomavirus (HPV) infection is responsible for at least 5% of human malignancies. Most HPV-associated cancers are initiated by the HPV16 genotype, as confirmed by detection of integrated HPV DNA in cells of oral and anogenital epithelial cancers. However, single-cell RNA sequencing may enable prediction of HPV involvement in carcinogenesis at other sites. We conducted single-cell RNA sequencing on keratinocytes from a mouse transgenic for the E7 gene of HPV16 and showed sensitive and specific detection of HPV16-E7 mRNA, predominantly in basal keratinocytes. We showed that increased E7 mRNA copy number per cell was associated with increased expression of E7 induced genes. This technique enhances detection of active viral transcription in solid tissue and may clarify possible linkage of HPV infection to development of squamous cell carcinoma.

Single cell RNA sequencing of stem cell-derived retinal ganglion cells.

We used single cell sequencing technology to characterize the transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs) at the single cell level. The human embryonic stem cell line BRN3B-mCherry (A81-H7), was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells showed increased expression of genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature RGC phenotype.

Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent giant cell arteritis.

BACKGROUND: Giant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies but symptoms and signs are variable thereby making it a challenging disease to diagnose. A temporal artery biopsy is the gold standard to confirm GCA, but there are currently no specific biochemical markers to aid diagnosis. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA. METHODS: We recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital, Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2-3 weeks, 6-8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting. RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls. RESULTS: Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, two transcripts remained differentially expressed after 12 months; SGTB, associated with neuronal apoptosis, and FCGR3A, associatied with Takayasu arteritis. We detected genes that correlate with both symptoms and biochemical markers used for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes including Polymyalgia Rheumatica, bilateral blindness and death within 12 months. CONCLUSIONS: This is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide in initiating appropriate patient management.