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Human pluripotent stem cell (hPSC)-derived progenies are immature versions of cells, presenting a potential limitation to the accurate modelling of disease 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 points in time for RPE cultures to reflect native counterparts, we characterised the transcriptomic profiles of hPSC-derived retinal pigment epithelium (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, and analysed the resulting data to assess the molecular changes of 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 maturing populations of RPE observed with time in culture. Assessment of gene ontology pathways revealed that as 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 confirmed a maturing transcriptional profile of RPE cells in culture. These results suggest that in vitro long-term culture of RPE cells allow the modelling of specific phenotypes observed in native mature tissue. Our work highlights the transcriptional landscape of hPSC-derived RPE as they age in culture, which provides a reference for native and patient-samples to be benchmarked against.

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.

Induced pluripotent stem cells generated from patients with geographic atrophy as well as healthy individuals were differentiated to retinal pigment epithelium (RPE) cells. By integrating transcriptional profiles of 127,659 RPE cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identified 439 expression Quantitative Trait (eQTL) loci in cis that were associated with disease status and specific to subpopulations of RPE cells. We identified loci linked to two genes with known associations with geographic atrophy - PILRB and PRPH2, in addition to 43 genes with significant genotype x disease interactions that are candidates for novel genetic associations for geographic atrophy. On a transcriptome-only level, we identified molecular pathways significantly upregulated in geographic atrophy-RPE including in extracellular cellular matrix reorganisation, neurodegeneration, and mitochondrial functions. We subsequently implemented a large-scale proteomics analysis, confirming modification in proteins associated with these pathways. We also identified six significant protein (p) QTL that regulate protein expression in the RPE cells and in geographic atrophy - two of which share variants with cis-eQTL. Transcriptome-wide association analysis identified genes at loci previously associated with age-related macular degeneration. Further analysis conditional on disease status, implicated statistically significant RPE-specific eQTL. This study uncovers important differences in RPE homeostasis associated with geographic atrophy.

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., Author summary The thickness of the inner retinal layers is one of the biomarkers for glaucoma, the leading cause of irreversible blindness globally. Here we utilised the large-scale of the UK Biobank and the images of the retina it contains to look for genetic variants that effect the thickness of the inner retina. We find many variants associated with this variable, but surprisingly only one that also affects glaucoma. Further analysis shows that glaucoma and genetically determined inner retinal thickness are not on the same genetic pathway and it is rather the change of thickness over time that is indicative of the disease. This is important as it invites the potential for the discovered variants to be used as a representation of baseline thickness in the clinic in the future. We also show that foveal hypoplasia, the lack of the normal valley-like shape of the central retina, is present at a population level in a mild form and is affected by three variants that also affect visual acuity. This is an interesting discovery as foveal hypoplasia was previously thought of as an outcome of rare Mendelian disease.

To determine if adrenocortical gene expression is associated with clinical outcomes or response to corticosteroid treatment in septic shock.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.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).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.

The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) - cells that can be differentiated into any cell type of the three germ layers - has provided a foundation forin vitrohuman disease modelling1,2, drug development1,2, and population genetics studies3,4. In the majority of instances, the expression levels of genes, plays a critical role in contributing to disease risk, or the ability to identify therapeutic targets. However, while the effect of the genetic background of cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells. Differences in the effect of genetic variation on the gene expression of different cell-types, would provide significant resolution for in vitro research using preprogramed cells. By bringing together single cell RNA sequencing15–21and population genetics, we now have a framework in which to evaluate the cell-types specific effects of genetic variation on gene expression. Here, we performed single cell RNA-sequencing on 64,018 fibroblasts from 79 donors and we mapped expression quantitative trait loci (eQTL) at the level of individual cell types. We demonstrate that the large majority of eQTL detected in fibroblasts are specific to an individual sub-type of cells. To address if the allelic effects on gene expression are dynamic across cell reprogramming, we generated scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type specific eQTL in iPSCs, and show that that the eQTL in fibroblasts are almost entirely disappear during reprogramming. This work provides an atlas of how genetic variation influences gene expression across cell subtypes, and provided evidence for patterns of genetic architecture that lead to cell-types specific eQTL effects.

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

Heterogeneity of cell states represented in pluripotent cultures have 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 comprised of 165 unique genes that denote the specific pluripotency states; and 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 3-fold) 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.

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, Muller 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 Muller 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.

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.

BackgroundGiant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies. GCA is a heterogenous disease, symptoms and signs are variable thereby making it challenging to diagnose and often delaying diagnosis. A temporal artery biopsy is the gold standard to test for GCA, and there are currently no specific biochemical markers to categorize or aid diagnosis of the disease. 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 and FindingsWe recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital (RVEEH), 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 (MACS). 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. 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, we identified two transcripts that remained differentially expressed after 12 months, namely SGTB, associated with neuronal apoptosis, and FCGR3A, which has been found in association with Takayasu arteritis (TA), another large vessel vasculitis. We detected genes that correlate with both symptoms and biochemical markers used in the acute setting 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: a history of Polymyalgia Rheumatica, both visual disturbance and raised neutrophils at the time of presentation, bilateral blindness and death within 12 months. Altered IL32 gene expression could provide risk evaluation of GCA diagnosis at the time of presentation and give an indication of prognosis, which may influence management.ConclusionsThis 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. These data significantly enhance the current knowledge of relevant biomarkers, their association with clinical prognostic markers, as well as potential candidates for detecting disease activity in whole blood samples. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide us in initiating appropriate patient management.

Cardiac differentiation of human pluripotent stem cells (hPSCs) requires orchestration of dynamic gene regulatory networks during stepwise fate transitions, but often generates immature cell types that do not fully recapitulate properties of their adult counterparts, suggesting incomplete activation of key transcriptional networks. We performed extensive single-cell transcriptomic analyses to map fate choices and gene expression programs during cardiac differentiation of hPSCs, and identified strategies to improve in vitro cardiomyocyte differentiation. Utilizing genetic gain- and loss-of-function approaches, we found that hypertrophic signaling is not effectively activated during monolayer-based cardiac differentiation, thereby preventing expression of HOPX and its activation of downstream genes that govern late stages of cardiomyocyte maturation. This study therefore provides a key transcriptional roadmap of in vitro cardiac differentiation at single-cell resolution, revealing fundamental mechanisms underlying heart development and differentiation of hPSC-derived cardiomyocytes.

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.