Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Single-cell data combined with phenotypes improves variant interpretation

BACKGROUND

Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can be complemented by approaches that incorporate phenotype specificity and provide contextual biological information, such as tissue or cell-type specificity. We hypothesised that integrating single-cell gene expression data into phenotype-specific models would improve the accuracy and interpretability of pathogenic variant prioritisation.

METHODS

To test this hypothesis, we developed IMPPROVE, a new tool that constructs phenotype-specific ensemble models integrating CADD scores with bulk and single-cell gene expression data. We constructed a total of 1,866 Random Forest models for individual HPO terms, incorporating both bulk and single cell expression data.

RESULTS

Our phenotype-specific models utilising expression data can better predict pathogenic variants in 90% of the phenotypes (HPO terms) considered. Using single-cell expression data instead of bulk benefited the models, significantly shifting the proportion of pathogenic variants that were correctly identified at a fixed false positive rate ( p < 10 - 30 , using an approximate Wilcoxon signed rank test). We found 57 phenotypes' models exhibited a large performance difference, depending on the dataset used. Further analysis revealed biological links between the pathology and the tissues or cell-types used by these 57 models.

CONCLUSIONS

Phenotype-specific models that integrate gene expression data with CADD scores show great promise in improving variant prioritisation. In addition to improving diagnostic accuracy, these models offer insights into the underlying biological mechanisms of rare diseases. Enriching existing pathogenicity-related scores with gene expression datasets has the potential to advance personalised medicine through more accurate and interpretable variant prioritisation.

Single-cell RNA-seq offer new insights into the cell fate decision of the primordial germ cells

The faithful production of primordial germ cells (PGCs) in vitro opens a wide range of novel applications in reproductive biology and medicine. However, the reproducibility of PGCs culture conditions across different laboratories or breeds remains a challenge. Therefore, it is necessary to research the molecular dynamics that lead to the gradual establishment of cultured PGCs lines network. Here, the results of single-cell RNA-seq indicated that the cell cycle drove cellular heterogeneity. The active populations engaged in PGC self-renewal and the characteristics of the aging cell fate have been identified. The active self-renewal populations presented a rising expression of DNA repair genes, couple with a high proportion of cells in G1/S phase and a low frequency of cells in G2 phase. Notably, Hippo, FoxO, AMPK and MAPK pathways are active within these populations. The combination of six activator or inhibitors, targeting these active pathways, resulted in a significantly higher proliferation rate of PGCs and an increased number of cells entering the G1 and S phases. Importantly, they greatly reduced the establishment time to a minimum of 26 days and increased the efficiency of male PGC line establishment to 59 % in FS medium. Our results provided several new insights into the PGCs.

Deterministic patterns in single-cell transcriptomic data

We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms.

Lung Immune Cell Niches and the Discovery of New Cell Subtypes

Immune cells in the lungs are important for maintaining lung function. The importance of immune cells in defending against lung diseases and infections is increasingly recognized. However, a primary knowledge gaps in current studies of lung immune cells is the understanding of their subtypes and functional heterogeneity. Increasing evidence supports the existence of novel immune cell subtypes that engage in the complex crosstalk between lung-resident immune cells, recruited immune cells, and epithelial cells. Therefore, further studies on how immune cells respond to perturbations in the pulmonary microenvironment are warranted. This review explores the processes behind the formation of the immune cell niche during lung development, and the characteristics and cell interaction modes of several major lung-resident immune cells. It indicates that distinct lung microenvironments or inflammatory niches can mediate the formation of different cell subtypes. These findings summarize and clarify paths to identify new cell subtypes that originate from resident progenitor cells and recruited peripheral cells, which are remodeled by the pulmonary microenvironment. The development of new techniques combining transcriptome analysis and location information is essential for identifying new immune cell subtypes and their relative immune niches, as well as for uncovering the molecular mechanisms of immune cell-mediated lung homeostasis.

Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues

Single-cell transcriptomics has emerged as the preferred tool to define cell identity through the analysis of gene expression signatures. However, there are limited studies that have comprehensively compared the performance of different scRNAseq systems in complex tissues. Here, we present a systematic comparison of two well-established high throughput 3'-scRNAseq platforms: 10× Chromium and BD Rhapsody, using tumours that present high cell diversity. Our experimental design includes both fresh and artificially damaged samples from the same tumours, which also provides a comparable dataset to examine their performance under challenging conditions. The performance metrics used in this study consist of gene sensitivity, mitochondrial content, reproducibility, clustering capabilities, cell type representation and ambient RNA contamination. These analyses showed that BD Rhapsody and 10× Chromium have similar gene sensitivity, while BD Rhapsody has the highest mitochondrial content. Interestingly, we found cell type detection biases between platforms, including a lower proportion of endothelial and myofibroblast cells in BD Rhapsody and lower gene sensitivity in granulocytes for 10× Chromium. Moreover, the source of the ambient noise was different between plate-based and droplet-based platforms. In conclusion, our reported platform differential performance should be considered for the selection of the scRNAseq method during the study experimental designs.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

BACKGROUND

Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY

The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis.

CONCLUSIONS

According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism

MOTIVATION

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.

RESULTS

Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.

scQTLbase: an integrated human single-cell eQTL database

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.

The potential clinical impact of cell type-specific genetic regulation: Crohn's disease

Complex diseases are heterogenous due to variation in their genetic and environmental underpinnings, leading to varied treatment responses. Genome-wide association studies (GWAS) integrated with single-cell expression quantitative trait loci analyses (eQTL) can pinpoint cell-type specific candidate disease-relevant genes and pathways. This knowledge can be applied to patient stratification and novel therapeutic target identification. Here, we describe the translational potential of cell-type specific genetic regulation, using Crohn's disease as an example.

Single-cell RNA sequencing of murine hearts for studying the development of the cardiac conduction system

The development of the cardiac conduction system (CCS) is essential for correct heart function. However, critical details on the cell types populating the CCS in the mammalian heart during the development remain to be resolved. Using single-cell RNA sequencing, we generated a large dataset of transcriptomes of ~0.5 million individual cells isolated from murine hearts at six successive developmental corresponding to the early, middle and late stages of heart development. The dataset provides a powerful library for studying the development of the heart's CCS and other cardiac components. Our initial analysis identified distinct cell types between 20 to 26 cell types across different stages, of which ten are involved in forming the CCS. Our dataset allows researchers to reuse the datasets for data mining and a wide range of analyses. Collectively, our data add valuable transcriptomic resources for further study of cardiac development, such as gene expression, transcriptional regulation and functional gene activity in developing hearts, particularly the CCS.

Atonal homolog 7 (ATOH7) confers neuroprotection for photoreceptor cells in glaucoma via inhibition of the notch pathway

Single-cell RNA sequencing (scRNA-seq) technologies enable the profiling and analysis of the transcriptomes of single cells and hold promise for clarifying gene mechanisms at single-cell resolution. We based this study on scRNA-seq data to reveal glaucoma-related genes and downstream pathways with neuroprotection effects. The scRNA-seq datasets related to glaucoma of retinal tissue samples of human beings and Atonal Homolog 7 (ATOH7)-null mice were obtained from the GEO database. The 74 top marker genes and 20 cell clusters were obtained in human retinal tissue samples. The key gene ATOH7 was found after the intersection with genes from GeneCards data. In the ATOH7-null mouse retinal tissue samples, pseudotime inference demonstrated significant changes in cell differentiation. Moreover, mouse retinal photoreceptor cells (PRCs) were cultured and treated with lentivirus carrying oe-ATOH7 alone or in combination with Notch signaling pathway activator Jagged-1/FC, after which cell biological functions were determined. The involvement of ATOH7 in glaucoma was identified through regulating PRCs. Furthermore, ATOH7 conferred neuroprotection in PRCs in glaucoma by mediating the Notch signaling pathway. In vitro data confirmed that ATOH7 overexpression promoted the differentiation of PRCs and inhibited their apoptosis by suppressing the Notch signaling pathway. The evidence provided by our study highlighted the involvement of ATOH7 in the blockade of the Notch signaling pathway, resulting in the neuroprotection for PRCs in glaucoma.

Epstein-Barr virus evades restrictive host chromatin closure by subverting B cell activation and germinal center regulatory loci

Chromatin accessibility fundamentally governs gene expression and biological response programs that can be manipulated by pathogens. Here we capture dynamic chromatin landscapes of individual B cells during Epstein-Barr virus (EBV) infection. EBV+ cells that exhibit arrest via antiviral sensing and proliferation-linked DNA damage experience global accessibility reduction. Proliferative EBV+ cells develop expression-linked architectures and motif accessibility profiles resembling in vivo germinal center (GC) phenotypes. Remarkably, EBV elicits dark zone (DZ), light zone (LZ), and post-GC B cell chromatin features despite BCL6 downregulation. Integration of single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data enables genome-wide cis-regulatory predictions implicating EBV nuclear antigens (EBNAs) in phenotype-specific control of GC B cell activation, survival, and immune evasion. Knockouts validate bioinformatically identified regulators (MEF2C and NFE2L2) of EBV-induced GC phenotypes and EBNA-associated loci that regulate gene expression (CD274/PD-L1). These data and methods can inform high-resolution investigations of EBV-host interactions, B cell fates, and virus-mediated lymphomagenesis.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

Single-Cell Multiomics

Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.

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.

RNA recording in single bacterial cells using reprogrammed tracrRNAs

Capturing an individual cell's transcriptional history is a challenge exacerbated by the functional heterogeneity of cellular communities. Here, we leverage reprogrammed tracrRNAs (Rptrs) to record selected cellular transcripts as stored DNA edits in single living bacterial cells. Rptrs are designed to base pair with sensed transcripts, converting them into guide RNAs. The guide RNAs then direct a Cas9 base editor to target an introduced DNA target. The extent of base editing can then be read in the future by sequencing. We use this approach, called TIGER (transcribed RNAs inferred by genetically encoded records), to record heterologous and endogenous transcripts in individual bacterial cells. TIGER can quantify relative expression, distinguish single-nucleotide differences, record multiple transcripts simultaneously and read out single-cell phenomena. We further apply TIGER to record metabolic bet hedging and antibiotic resistance mobilization in Escherichia coli as well as host cell invasion by Salmonella. Through RNA recording, TIGER connects current cellular states with past transcriptional states to decipher complex cellular responses in single cells.

Evaluating imputation methods for single-cell RNA-seq data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.

RESULTS

In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.

CONCLUSIONS

In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment

The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Time-resolved transcriptomes reveal diverse B cell fate trajectories in the early response to Epstein-Barr virus infection

Epstein-Barr virus infection of B lymphocytes elicits diverse host responses via well-adapted transcriptional control dynamics. Consequently, this host-pathogen interaction provides a powerful system to explore fundamental processes leading to consensus fate decisions. Here, we use single-cell transcriptomics to construct a genome-wide multistate model of B cell fates upon EBV infection. Additional single-cell data from human tonsils reveal correspondence of model states to analogous in vivo phenotypes within secondary lymphoid tissue, including an EBV+ analog of multipotent activated precursors that can yield early memory B cells. These resources yield exquisitely detailed perspectives of the transforming cellular landscape during an oncogenic viral infection that simulates antigen-induced B cell activation and differentiation. Thus, they support investigations of state-specific EBV-host dynamics, effector B cell fates, and lymphomagenesis. To demonstrate this potential, we identify EBV infection dynamics in FCRL4+/TBX21+ atypical memory B cells that are pathogenically associated with numerous immune disorders.

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.

Human Variation in DNA Repair, Immune Function, and Cancer Risk

DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.

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.

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets

MOTIVATION

Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

RESULTS

We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.

AVAILABILITY AND IMPLEMENTATION

The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Power of Single-Cell RNA Sequencing in eQTL Discovery

Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

Atherosclerosis still is the primary cause of death worldwide. Our characterization of the atherosclerotic lesion is mainly rooted in definitions based on pathological descriptions. We often speak in absolutes regarding plaque phenotypes: vulnerable vs. stable plaques or plaque rupture vs. plaque erosion. By focusing on these concepts, we may have oversimplified the atherosclerotic disease and its mechanisms. The widely used definitions of pathology-based plaque phenotypes can be fine-tuned with observations made with various -omics techniques. Recent advancements in single-cell transcriptomics provide the opportunity to characterize the cellular composition of the atherosclerotic plaque. This additional layer of information facilitates the in-depth characterization of the atherosclerotic plaque. In this review, we discuss the impact that single-cell transcriptomics may exert on our current understanding of atherosclerosis.

Single-cell expression quantitative trait loci (eQTL) analysis of SLE-risk loci in lupus patient monocytes

BACKGROUND

We performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution.

METHODS

Single-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14++CD16- CL and CD14dimCD16+ NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles.

RESULTS

The SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs (p = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets.

CONCLUSIONS

We document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function.

Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

Single-cell transcriptome and TCR profiling reveal activated and expanded T cell populations in Parkinson's disease

Given the chronic inflammatory nature of Parkinson's disease (PD), T cell immunity may be important for disease onset. Here, we performed single-cell transcriptome and TCR sequencing, and conducted integrative analyses to decode composition, function and lineage relationship of T cells in the blood and cerebrospinal fluid of PD. Combined expression and TCR-based lineage tracking, we discovered a large population of CD8⁺ T cells showing continuous progression from central memory to terminal effector T cells in PD patients. Additionally, we identified a group of cytotoxic CD4⁺ T cells (CD4 CTLs) remarkably expanded in PD patients, which derived from Th1 cells by TCR-based fate decision. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of PD patients to provide potential evidence for peripheral T cells to participate in neuronal degeneration. Our study provides valuable insights and rich resources for understanding the adaptive immune response in PD.

Interaction of HULC polymorphisms with Helicobacter pylori infection plays a strong role for the prediction of gastric cancer risk

Aim: Gene-environment interactions have better efficacy in predicting cancer susceptibility than a single gene. Materials & methods: Eight tag single nucleotide polymorphisms encompassing the whole HULC gene were detected by KASP platform (LGC Genomics, Hoddesdon, UK) in 631 gastric cancer (GC) cases and 953 controls. Results: The HULC gene rs7770772 polymorphism could increase GC risk (recessive model: odds ratio = 1.95). The multifactor dimensionality reduction (MDR) analysis suggested that the 2D model HULC rs7770772-Helicobacter pylori had better effect on GC risk prediction (maximum testing accuracy = 0.7005). No significant result was observed in our experimental expression quantitative trait loci analysis. Conclusion: 2D model HULC rs7770772-H. pylori might have superior efficacy for GC risk than a single factor.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

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.

TWO-SIGMA: A novel two-component single cell model-based association method for single-cell RNA-seq data

In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same individual via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https://github.com/edvanburen/twosigma.

Single-cell transcriptomic profiling and characterization of endothelial progenitor cells: new approach for finding novel markers

Background

Endothelial progenitor cells (EPCs) are promising candidates for the cellular therapy of peripheral arterial and cardiovascular diseases. However, hitherto there is no specific marker(s) defining precisely EPCs. Herein, we are proposing a new in silico approach for finding novel EPC markers.

Methods

We assembled five groups of chosen EPC-related genes/factors using PubMed literature and Gene Ontology databases. This shortened database of EPC factors was fed into publically published transcriptome matrix to compare their expression between endothelial colony-forming cells (ECFCs), HUVECs, and two adult endothelial cell types (ECs) from the skin and adipose tissue. Further, the database was used for functional enrichment on Mouse Phenotype database and protein-protein interaction network analyses. Moreover, we built a digital matrix of healthy donors’ PBMCs (33 thousand single-cell transcriptomes) and analyzed the expression of these EPC factors.

Results

Transcriptome analyses showed that BMP2, 4, and ephrinB2 were exclusively highly expressed in EPCs; the expression of neuropilin-1 and VEGF-C were significantly higher in EPCs and HUVECs compared with other ECs; Notch 1 was highly expressed in EPCs and skin-ECs; MIR21 was highly expressed in skin-ECs; PECAM-1 was significantly higher in EPCs and adipose ECs. Moreover, functional enrichment of EPC-related genes on Mouse Phenotype and STRING protein database has revealed significant relations between chosen EPC factors and endothelial and vascular functions, development, and morphogenesis, where ephrinB2, BMP2, and BMP4 were highly expressed in EPCs and were connected to abnormal vascular functions. Single-cell RNA-sequencing analyses have revealed that among the EPC-regulated markers in transcriptome analyses, (i) ICAM1 and Endoglin were weekly expressed in the monocyte compartment of the peripheral blood; (ii) CD163 and CD36 were highly expressed in the CD14+ monocyte compartment whereas CSF1R was highly expressed in the CD16+ monocyte compartment, (iii) L-selectin and IL6R were globally expressed in the lymphoid/myeloid compartments, and (iv) interestingly, PLAUR/UPAR and NOTCH2 were highly expressed in both CD14+ and CD16+ monocytic compartments.

Conclusions

The current study has identified novel EPC markers that could be used for better characterization of EPC subpopulation in adult peripheral blood and subsequent usage of EPCs for various cell therapy and regenerative medicine applications.

Single-Cell Transcriptome Profiling

Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.

Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.

Inside the supergene of the bird with four sexes

The white-throated sparrow (Zonotrichia albicollis) offers unique opportunities to understand the adaptive value of supergenes, particularly their role in alternative phenotypes. In this species, alternative plumage morphs segregate with a nonrecombining segment of chromosome 2, which has been called a 'supergene'. The species mates disassortatively with respect to the supergene; that is, each breeding pair consists of one individual with it and one without it. This species has therefore been called the "bird with four sexes". The supergene segregates with a behavioral phenotype; birds with it are more aggressive and less parental than birds without it. Here, we review our efforts to identify the genes inside the supergene that are responsible for the behavioral polymorphism. The gene ESR1, which encodes estrogen receptor α, differs between the morphs and predicts both territorial and parental behavior. Variation in the regulatory regions of ESR1 causes an imbalance in expression of the two alleles, and the degree to which this imbalance favors the supergene allele predicts territorial singing. In heterozygotes, knockdown of ESR1 causes a phenotypic switch, from more aggressive to less aggressive. We recently showed that another gene important for social behavior, vasoactive intestinal peptide (VIP), is differentially expressed between the morphs and predicts territorial singing. We hypothesize that ESR1 and VIP contribute to behavior in a coordinated way and could represent co-adapted alleles. Because the supergene contains more than 1000 individual genes, this species provides rich possibilities for discovering alleles that work together to mediate life-history trade-offs and maximize the fitness of alternative complex phenotypes.

Dissecting genetic determinants of variation in human immune responses

The immune system is paradigmatic for a complex arrangement of heterogenous cells performing distinct, frequently temporally and anatomically dissociated, functions. Immune dysfunction is a common characteristic across most diseases and human genetic approaches have revealed that many disease risk loci are associated with expression profiles and counts of specific immune subsets. Furthermore, genetic regulators of immune function may only demonstrate activity in specific disease-linked contexts. Here we explore steps taken to dissect the genetic determinants of variation in immune response across cell counts and function, and the insights these have provided into human immunity.

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans

Variability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome.

Triqueneaux, Burny, Symmons et al. show association between gene expression noise and genotypes, using single-cell expression of four proteins across human-derived lymphoblastoid cell lines. This study suggests that very subtle regulatory effects of human DNA variants may contribute to phenotypic variation and disease outcome.

In situ multiplex immunofluorescence analysis of the inflammatory burden in kidney allograft rejection: A new tool to characterize the alloimmune response

The exact composition of leukocyte infiltration during kidney allograft rejection is difficult to comprehend and visualize on the same biopsy slide. Using an innovative technology of multiplex immunofluorescence (mIF), we were able to detect simultaneously NK cells, macrophages, and T cells and to determine their intra- or extravascular localization using an endothelial marker. Twenty antibody-mediated rejection (ABMR), 20 T cell-mediated rejection (TCMR), and five normal biopsies were labeled, with automatic leukocyte quantification and localization. This method was compared to a classic NKp46 immunohistochemistry (IHC) with manual quantification and to mRNA quantification. mIF automatic quantification was strongly correlated to IHC (r = .91, P < .001) and to mRNA expression levels (r > .46, P < .021). T cells and macrophages were the 2 predominant populations involved in rejection (48.0 ± 4.4% and 49.3 ± 4.4%, respectively, in ABMR; 51.8 ± 6.0% and 45.3 ± 5.8% in TCMR). NK cells constituted a rare population in both ABMR (2.7 ± 0.7%) and TCMR (2.9 ± 0.6%). The intravascular compartment was mainly composed of T cells, including during ABMR, in peritubular and glomerular capillaries. However, NK cell and macrophage densities were significantly higher during ABMR in glomerular and peritubular capillaries. To conclude, this study demonstrates the feasibility and utility of mIF imaging to study and better understand the kidney allograft rejection process.

Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

Single-Cell RNA Sequencing Reveals Size-Dependent Effects of Polystyrene Microplastics on Immune and Secretory Cell Populations from Zebrafish Intestines

Microplastics (MPs) as widespread contamination pose a high risk for aquatic organisms. However, the current understanding of MP toxicity is based on cell population-averaged measurements. Our aim was to gain a comprehensive understanding of the size-dependent effects of polystyrene MPs (PS-MPs) on intestinal cell populations in zebrafish and characterize the interplay of MPs, intestinal cells, and intestinal microbiota. Here, we used single-cell RNA sequencing to determine the transcriptome heterogeneity of 12 000 intestinal cells obtained from zebrafish exposed to 100 nm, 5 μm, and 200 μm PS-MPs for 21 days. Eight intestinal cell populations were identified. Combined with changes in intestinal microbiota, our findings highlight a previously unrecognized end point that all three sizes of PS-MPs induced dysfunction of intestinal immune cells (including effects on phagosomes and the regulation of immune system processes) and increased the abundance of pathogenic bacteria. However, only 100 nm PS-MPs altered the expression of genes related to phagocyte-produced reactive oxygen species (ROS) generation and increased mucus secretion by secretory cells. Microsize PS-MPs specifically changed the lysosome (5 μm) and cell surface receptor signaling (200 μm) processes of the macrophages. Our findings pinpoint to cell-specific and size-dependent responses to PS-MPs in fish intestine, which can provide a reference for future study directions.