Revealing allele-specific gene expression by single-cell transcriptomics

Single-Cell Transcriptome Sequencing Reveals Molecular Expression Differences and Marker Genes in Testes during the Sexual Maturation of Mongolian Horses

This study aimed to investigate differences in testicular tissue morphology, gene expression, and marker genes between sexually immature (1-year-old) and sexually mature (10-year-old) Mongolian horses. The purposes of our research were to provide insights into the reproductive physiology of male Mongolian horses and to identify potential markers for sexual maturity. The methods we applied included the transcriptomic profiling of testicular cells using single-cell sequencing techniques. Our results revealed significant differences in tissue morphology and gene expression patterns between the two age groups. Specifically, 25 cell clusters and 10 cell types were identified, including spermatogonial and somatic cells. Differential gene expression analysis highlighted distinct patterns related to cellular infrastructure in sexually immature horses and spermatogenesis in sexually mature horses. Marker genes specific to each stage were also identified, including APOA1, AMH, TAC3, INHA, SPARC, and SOX9 for the sexually immature stage, and PRM1, PRM2, LOC100051500, PRSS37, HMGB4, and H1-9 for the sexually mature stage. These findings contribute to a deeper understanding of testicular development and spermatogenesis in Mongolian horses and have potential applications in equine reproductive biology and breeding programs. In conclusion, this study provides valuable insights into the molecular mechanisms underlying sexual maturity in Mongolian horses.

The emerging roles of interstitial macrophages in pulmonary fibrosis: A perspective from scRNA-seq analyses

Pulmonary fibrosis is an irreversible and progressive disease affecting the lungs, and the etiology remains poorly understood. This disease can be lethal and currently has no specific clinical therapeutic regimen. Macrophages, the most common type of immune cell in the lungs, have been reported to play a key role in the pathogenesis of fibrotic disease. The lung macrophage population is mostly composed of alveolar macrophages and interstitial macrophages, both of which have not been thoroughly studied in the pathogenesis of lung fibrosis. Interstitial macrophages have recently been recognised for their participation in lung fibrosis due to new technology arising from a combination of bioinformatics and single-cell RNA sequencing analysis. This paper reviews recent developments regarding lung macrophage classification and summarizes the origin and replenishment of interstitial macrophages and their function in pulmonary fibrosis.

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene–environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

Towards accurate, contiguous and complete alignment-based polyploid phasing algorithms

Phasing, and in particular polyploid phasing, have been challenging problems held back by the limited read length of high-throughput short read sequencing methods which can't overcome the distance between heterozygous sites and labor high cost of alternative methods such as the physical separation of chromosomes for example. Recently developed single molecule long-read sequencing methods provide much longer reads which overcome this previous limitation. Here we review the alignment-based methods of polyploid phasing that rely on four main strategies: population inference methods, which leverage the genetic information of several individuals to phase a sample; objective function minimization methods, which minimize a function such as the Minimum Error Correction (MEC); graph partitioning methods, which represent the read data as a graph and split it into k haplotype subgraphs; cluster building methods, which iteratively grow clusters of similar reads into a final set of clusters that represent the haplotypes. We discuss the advantages and limitations of these methods and the metrics used to assess their performance, proposing that accuracy and contiguity are the most meaningful metrics. Finally, we propose the field of alignment-based polyploid phasing would greatly benefit from the use of a well-designed benchmarking dataset with appropriate evaluation metrics. We consider that there are still significant improvements which can be achieved to obtain more accurate and contiguous polyploid phasing results which reflect the complexity of polyploid genome architectures.

Single-cell RNA sequencing identifies a migratory keratinocyte subpopulation expressing THBS1 in epidermal wound healing

Keratinocyte differentiation is an intricate process that is regulated by multiple mediators. Using cultured human keratinocytes, we found that lysophosphatidic acid (LPA) induced the differentiation of a previously unsuspected keratinocyte subpopulation expressing the extracellular matrix protein, thrombospondin-1 (THBS1). This action of LPA was mediated by the RHO/ROCK-SRF signaling downstream of LPA₁ and LPA₅ receptors and required ERK activity. Suppression of THBS1 in vitro suggested a migratory role of THBS1⁺ keratinocytes. Moreover, we analyzed publicly deposited single-cell RNA sequencing dataset and identified Thbs1-expressing keratinocytes in the mouse wound skin. Immunohistochemistry analysis revealed that Thbs1⁺ keratinocytes were apparently differentiated from basal keratinocytes upon wounding, subsequently polarized and migrated suprabasally toward the wound front, and eventually underwent terminal differentiation in the neo-epidermis. Importantly, inhibition of Erk activity suppressed Thbs1⁺ keratinocyte differentiation in wound healing. Based on these findings, we suggest that THBS1⁺ keratinocyte is a migratory keratinocyte subpopulation that facilitates epidermal wound healing.

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Single-cell RNA sequencing reveals a keratinocyte subpopulation expressing THBS1

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LPA and ERK activity are required for the induction of THBS1⁺ keratinocyte

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Thbs1⁺ keratinocytes are differentiated from basal keratinocytes upon epidermal wounding

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Thbs1⁺ keratinocytes are migratory cells and may contribute to epidermal wound healing

Perspectives on Allele-Specific Expression

Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.

nPhase: an accurate and contiguous phasing method for polyploids

While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy (https://github.com/OmarOakheart/nPhase). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0.5%). nPhase allows population genomics and hybrid studies of polyploids.

Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2

Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45^- cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%-30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.

SNPs in Sites for DNA Methylation, Transcription Factor Binding, and miRNA Targets Leading to Allele-Specific Gene Expression and Contributing to Complex Disease Risk: A Systematic Review

INTRODUCTION

The complex genetic diversity among human populations results from an assortment of factors acting at various sequential levels, including mutations, population migrations, genetic drift, and selection. Although there are a plethora of DNA sequence variations identified through genome-wide association studies (GWAS), the challenge remains to explain the mechanisms underlying interindividual phenotypic disparity accounting for disease susceptibility. Single nucleotide polymorphisms (SNPs) present in the sites for DNA methylation, transcription factor (TF) binding, or miRNA targets can alter the gene expression. The systematic review aimed to evaluate the complex crosstalk among SNPs, miRNAs, DNA methylation, and TFs for complex multifactorial disease risk.

METHODS

PubMed and Scopus databases were used from inception until May 15, 2019. Initially, screening of articles involved studies assessing the interaction of SNPs with TFs, DNA methylation, or miRNAs resulting in allele-specific gene expression in complex multifactorial diseases. We also included the studies which provided experimental validation of the interaction of SNPs with each of these factors. The results from various studies on multifactorial diseases were assessed.

RESULTS

A total of 11 articles for SNPs interacting with DNA methylation, 30 articles for SNPs interacting with TFs, and 11 articles for SNPs in miRNA binding sites were selected. The interactions of SNPs with epigenetic factors were found to be implicated in different types of cancers, autoimmune diseases, cardiovascular diseases, diabetes, and asthma.

CONCLUSION

The systematic review provides evidence for the interplay between genetic and epigenetic risk factors through allele-specific gene expression in various complex multifactorial diseases.

X-Chromosome Inactivation and Escape from X Inactivation in Mouse

X chromosome inactivation silences one X chromosome in female mammals. However, this silencing is incomplete, and some genes escape X inactivation. We describe methods to determine the chromosome-wide X inactivation status of genes in tissues or cell lines derived from mice using a combination of skewing of X inactivation and allele-specific analyses of gene expression based on RNA-seq.

Quantification of allelic differential expression using a simple Fluorescence primer PCR-RFLP-based method

Allelic differential expression (ADE) is common in diploid organisms, and is often the key reason for specific phenotype variations. Thus, ADE detection is important for identification of major genes and causal mutations. To date, sensitive and simple methods to detect ADE are still lacking. In this study, we have developed an accurate, simple, and sensitive method, named fluorescence primer PCR-RFLP quantitative method (fPCR-RFLP), for ADE analysis. This method involves two rounds of PCR amplification using a pair of primers, one of which is double-labeled with an overhang 6-FAM. The two alleles are then separated by RFLP and quantified by fluorescence density. fPCR-RFLP could precisely distinguish ADE cross a range of 1- to 32-fold differences. Using this method, we verified PLAG1 and KIT, two candidate genes related to growth rate and immune response traits of pigs, to be ADE both at different developmental stages and in different tissues. Our data demonstrates that fPCR-RFLP is an accurate and sensitive method for detecting ADE on both DNA and RNA level. Therefore, this powerful tool provides a way to analyze mutations that cause ADE.

Single-Cell Allele-Specific Gene Expression Analysis

Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average gene expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism, and characterization of allele-specific bursting. Here we describe SCALE, a bioinformatic and statistical framework for allele-specific gene expression analysis by scRNA-seq. SCALE estimates genome-wide bursting kinetics at the allelic level while accounting for technical bias and other complicating factors such as cell size. SCALE detects genes with significantly different bursting kinetics between the two alleles, as well as genes where the two alleles exhibit non-independent bursting processes. Here, we illustrate SCALE on a mouse blastocyst single-cell dataset with step-by-step demonstration from the upstream bioinformatic processing to the downstream biological interpretation of SCALE's output.

Meta-Analysis of the Relation Between IL10 Promoter Polymorphisms and Autoimmune Liver Disease Risk

BACKGROUND

Single nucleotide polymorphisms of the IL10 gene have been linked to the occurrence of autoimmune liver disease.

METHODS

We performed a meta-analysis to assess the association between three IL10 promoter polymorphisms (rs1800896, rs1800871, and rs1800872) and the risk of autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis.

RESULTS

In total, 1420 articles were initially identified through database retrieval. After screening, seven eligible articles were ultimately included in the meta-analysis. A fixed-effect model was used for all Mantel-Haenszel statistics due to the absence of large between-study heterogeneity (all I² < 50%, p > 0.1). No association between any of the studied polymorphisms and risk of autoimmune liver disease was detected in the allele, homozygote, heterozygote, dominant, recessive, or carrier genetic models (p_association > 0.05). Potential publication bias was excluded using Begg's and Egger's tests. Similar negative results were observed in subgroup analyses and in an analysis of the three haplotypes of rs1800896/rs1800871/rs1800872 (G/C/C, A/C/C, and A/T/A).

CONCLUSION

Our meta-analysis strongly suggests that the IL10 rs1800896, rs1800871, and rs1800872 polymorphisms are not associated with the risk of autoimmune liver disease.