Single-cell genomics meets human genetics.

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

Transitioning single-cell genomics into the clinic.

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

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.

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

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

A Pilot Study Using Machine Learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery.

OBJECTIVE: To evaluate whether a machine learning algorithm (i.e. the "NightSignal" algorithm) can be used for the detection of postoperative complications prior to symptom onset after cardiothoracic surgery. SUMMARY BACKGROUND DATA: Methods that enable the early detection of postoperative complications after cardiothoracic surgery are needed. METHODS: This was a prospective observational cohort study conducted from July 2021 to February 2023 at a single academic tertiary care hospital. Patients aged 18 years or older scheduled to undergo cardiothoracic surgery were recruited. Study participants wore a Fitbit watch continuously for at least 1 week preoperatively and up to 90-days postoperatively. The ability of the NightSignal algorithm-which was previously developed for the early detection of Covid-19-to detect postoperative complications was evaluated. The primary outcomes were algorithm sensitivity and specificity for postoperative event detection. RESULTS: A total of 56 patients undergoing cardiothoracic surgery met inclusion criteria, of which 24 (42.9%) underwent thoracic operations and 32 (57.1%) underwent cardiac operations. The median age was 62 (IQR: 51-68) years and 30 (53.6%) patients were female. The NightSignal algorithm detected 17 of the 21 postoperative events a median of 2 (IQR: 1-3) days prior to symptom onset, representing a sensitivity of 81%. The specificity, negative predictive value, and positive predictive value of the algorithm for the detection of postoperative events were 75%, 97%, and 28%, respectively. CONCLUSIONS: Machine learning analysis of biometric data collected from wearable devices has the potential to detect postoperative complications-prior to symptom onset-after cardiothoracic surgery.

A single-cell tumor immune atlas for precision oncology.

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing published data sets of >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying colocalization patterns of immune, stromal, and cancer cells in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immunotherapy.

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.

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.].

Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19.

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV-2-specific naïve T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. A novel population of naïve interferon-activated T cells is expanded in acute COVID-19 and is recruited into the memory compartment during convalescence in adults but not children. This was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection.

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.

propeller: testing for differences in cell type proportions in single cell data.

MOTIVATION: Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. RESULTS: We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity. AVAILABILITY AND IMPLEMENTATION: The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nebulosa recovers single-cell gene expression signals by kernel density estimation.

SUMMARY: Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualized in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression. AVAILABILITY AND IMPLEMENTATION: Nebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

The Genetic Architecture of Gene Expression in Peripheral Blood.

We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h2) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (hCOJO2) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (hg2) accounted for on average 48% (0.093/0.192) of h2. Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.

Detection and replication of epistasis influencing transcription in humans.

Epistasis is the phenomenon whereby one polymorphism's effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits and contributes to their variation is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms, and some examples have been reported in other species, few examples exist for epistasis among natural polymorphisms in human traits. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues. Here we show, using advanced computation and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < 2.91 × 10(-16)). Replication of these interactions in two independent data sets showed both concordance of direction of epistatic effects (P = 5.56 × 10(-31)) and enrichment of interaction P values, with 30 being significant at a conservative threshold of P < 9.98 × 10(-5). Forty-four of the genetic interactions are located within 5 megabases of regions of known physical chromosome interactions (P = 1.8 × 10(-10)). Epistatic networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis-acting SNP is modulated by several trans-acting SNPs. For example, MBNL1 is influenced by an additive effect at rs13069559, which itself is masked by trans-SNPs on 14 different chromosomes, with nearly identical genotype-phenotype maps for each cis-trans interaction. This study presents the first evidence, to our knowledge, for many instances of segregating common polymorphisms interacting to influence human traits.

Septic Shock: A Genomewide Association Study and Polygenic Risk Score Analysis.

Previous genetic association studies have failed to identify loci robustly associated with sepsis, and there have been no published genetic association studies or polygenic risk score analyses of patients with septic shock, despite evidence suggesting genetic factors may be involved. We systematically collected genotype and clinical outcome data in the context of a randomized controlled trial from patients with septic shock to enrich the presence of disease-associated genetic variants. We performed genomewide association studies of susceptibility and mortality in septic shock using 493 patients with septic shock and 2442 population controls, and polygenic risk score analysis to assess genetic overlap between septic shock risk/mortality with clinically relevant traits. One variant, rs9489328, located in AL589740.1 noncoding RNA, was significantly associated with septic shock (p = 1.05 × 10-10); however, it is likely a false-positive. We were unable to replicate variants previously reported to be associated (p < 1.00 × 10-6 in previous scans) with susceptibility to and mortality from sepsis. Polygenic risk scores for hematocrit and granulocyte count were negatively associated with 28-day mortality (p = 3.04 × 10-3; p = 2.29 × 10-3), and scores for C-reactive protein levels were positively associated with susceptibility to septic shock (p = 1.44 × 10-3). Results suggest that common variants of large effect do not influence septic shock susceptibility, mortality and resolution; however, genetic predispositions to clinically relevant traits are significantly associated with increased susceptibility and mortality in septic individuals.

Transcriptomics and single-cell RNA-sequencing.

The past four decades have yielded advances in molecular biology allowing detailed characterization of the cellular genome and the transcriptome: the complete set of RNA species transcribed by a cell or tissue. Through transcriptomics and next-generation sequencing, we can now attain an unprecedented level of detail in understanding cellular phenotypes through examining the genes expressed in specific physiological and pathological states. In this review, we provide an overview of transcriptomics and RNA-sequencing in the analysis of whole tissue and single cells. We describe the techniques and pitfalls involved in the isolation and sequencing of single cells, and what additional benefits this application can provide. Finally, we look to how these technologies are being applied in pulmonary research, and how they may translate in the near future into clinical practice.