Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data

Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

Beyond variability: a novel gene expression stability metric to unveil homeostasis and regulation

The concept of gene expression stability within a homeostatic cell is explored through the gene homeostasis Z-index, a measure that highlights genes under active regulation in response to internal and external stimuli. This index reveals distinct regulatory activities and patterns in different organs, such as enhanced synaptic transmission in pancreatic islets. The research indicates that traditional mean-based methods may miss these nuances, underlining the significance of new metrics in identifying gene regulation specifics in cellular adaptation.

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.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) - the variability of gene expression in response to environmental changes - remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also find that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics

Spatial transcriptomics has revolutionized the study of gene expression within tissues, while preserving spatial context. However, annotating spatial spots' biological identity remains a challenge. To tackle this, we introduce Pianno, a Bayesian framework automating structural semantics annotation based on marker genes. Comprehensive evaluations underscore Pianno's remarkable prowess in precisely annotating a wide array of spatial semantics, ranging from diverse anatomical structures to intricate tumor microenvironments, as well as in estimating cell type distributions, across data generated from various spatial transcriptomics platforms. Furthermore, Pianno, in conjunction with clustering approaches, uncovers a region- and species-specific excitatory neuron subtype in the deep layer 3 of the human neocortex, shedding light on cellular evolution in the human neocortex. Overall, Pianno equips researchers with a robust and efficient tool for annotating diverse biological structures, offering new perspectives on spatial transcriptomics data.

Single Cell Transcriptome Analysis During Development in Dictyostelium

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

Cellular age explains variation in age-related cell-to-cell transcriptome variability

Organs and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types, the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics

MOTIVATION

The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data.

RESULTS

We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ChengZ352/SPAN.

Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver

Tissues within an organism and even cell types within a tissue can age with different velocities. However, it is unclear whether cells of one type experience different aging trajectories within a tissue depending on their spatial location. Here, we used spatial transcriptomics in combination with single-cell ATAC-seq and RNA-seq, lipidomics and functional assays to address how cells in the male murine liver are affected by age-related changes in the microenvironment. Integration of the datasets revealed zonation-specific and age-related changes in metabolic states, the epigenome and transcriptome. The epigenome changed in a zonation-dependent manner and functionally, periportal hepatocytes were characterized by decreased mitochondrial fitness, whereas pericentral hepatocytes accumulated large lipid droplets. Together, we provide evidence that changing microenvironments within a tissue exert strong influences on their resident cells that can shape epigenetic, metabolic and phenotypic outputs.

Metabolic and senescence characteristics associated with the immune microenvironment in non-small cell lung cancer: insights from single-cell RNA sequencing

Non-small lung cancer (NSCLC) has been defined as a highly life-threatening heterogeneous disease, with high mortality and occurrence. Recent research has indicated that tumor-infiltrating lymphocytes play a key determinant role in cancer progression. Emerging single-cell RNA sequencing (also termed scRNA-seq) has been extensively applied to depict the baseline landscape of the cell composition and function phenotype in the tumor environment (TME). Herein, we dissected the cell types in NSCLC samples (including tissue and blood) and identified three types of cell marker genes including cancer cells, T cells, and macrophages by integrating two NSCLC-associated scRNA-seq datasets in GEO. Survival analysis indicated that 17 marker genes were related to tumor prognosis. Function annotation was used to scrutinize the molecular mechanism of these marker genes in different cells. Besides, we investigated the developmental trajectory and T cell receptor repertoire diversity of tumor-infiltrating T cells. Our analysis will help further understand the complexity of cell components and the heterogeneity of TME in NSCLC.

Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation.

RESULTS

We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks.

CONCLUSIONS

We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

Differential variability analysis of single-cell gene expression data

The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also found that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also found that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells

Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

Bayesian cluster analysis

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Selective inference for k-means clustering

We consider the problem of testing for a difference in means between clusters of observations identified via k -means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. In recent work, Gao et al. (2022) considered a related problem in the context of hierarchical clustering. Unfortunately, their solution is highly-tailored to the context of hierarchical clustering, and thus cannot be applied in the setting of k -means clustering. In this paper, we propose a p-value that conditions on all of the intermediate clustering assignments in the k -means algorithm. We show that the p-value controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k -means clustering in finite samples, and can be efficiently computed. We apply our proposal on hand-written digits data and on single-cell RNA-sequencing data.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

Functional link between sarcoidosis-associated gene variants and quantitative levels of bronchoalveolar lavage fluid cell types

Background

Sarcoidosis is an inflammatory disease that affects multiple organs. Cell analysis from bronchoalveolar lavage fluid (BALF) is a valuable tool in the diagnostic workup and differential diagnosis of sarcoidosis. Besides the expansion of lymphocyte expression-specific receptor segments (Vα2.3 and Vβ22) in some patients with certain HLA types, the relation between sarcoidosis susceptibility and BAL cell populations' quantitative levels is not well-understood.

Methods

Quantitative levels defined by cell concentrations of BAL cells and CD4⁺/CD8⁺ ratio were evaluated together with genetic variants associated with sarcoidosis in 692 patients with extensive clinical data. Genetic variants associated with clinical phenotypes, Löfgren's syndrome (LS) and non-Löfgren's syndrome (non-LS), were examined separately. An association test via linear regression using an additive model adjusted for sex, age, and correlated cell type was applied. To infer the biological function of genetic associations, enrichment analysis of expression quantitative trait (eQTLs) across publicly available eQTL databases was conducted.

Results

Multiple genetic variants associated with sarcoidosis were significantly associated with quantitative levels of BAL cells. Specifically, LS genetic variants, mainly from the HLA locus, were associated with quantitative levels of BAL macrophages, lymphocytes, CD3⁺ cells, CD4⁺ cells, CD8⁺ cells, CD4⁺/CD8⁺ ratio, neutrophils, basophils, and eosinophils. Non-LS genetic variants were associated with quantitative levels of BAL macrophages, CD8⁺ cells, basophils, and eosinophils. eQTL enrichment revealed an influence of sarcoidosis-associated SNPs and regulation of gene expression in the lung, blood, and immune cells.

Conclusion

Genetic variants associated with sarcoidosis are likely to modulate quantitative levels of BAL cell types and may regulate gene expression in immune cell populations. Thus, the role of sarcoidosis-associated gene-variants may be to influence cellular phenotypes underlying the disease immunopathology.

Transplanting rejuvenated blood stem cells extends lifespan of aged immunocompromised mice

One goal of regenerative medicine is to rejuvenate tissues and extend lifespan by restoring the function of endogenous aged stem cells. However, evidence that somatic stem cells can be targeted in vivo to extend lifespan is still lacking. Here, we demonstrate that after a short systemic treatment with a specific inhibitor of the small RhoGTPase Cdc42 (CASIN), transplanting aged hematopoietic stem cells (HSCs) from treated mice is sufficient to extend the healthspan and lifespan of aged immunocompromised mice without additional treatment. In detail, we show that systemic CASIN treatment improves strength and endurance of aged mice by increasing the myogenic regenerative potential of aged skeletal muscle stem cells. Further, we show that CASIN modifies niche localization and H4K16ac polarity of HSCs in vivo. Single-cell profiling reveals changes in HSC transcriptome, which underlie enhanced lymphoid and regenerative capacity in serial transplantation assays. Overall, we provide proof-of-concept evidence that a short systemic treatment to decrease Cdc42 activity improves the regenerative capacity of different endogenous aged stem cells in vivo, and that rejuvenated HSCs exert a broad systemic effect sufficient to extend murine health- and lifespan.

Brahma safeguards canalization of cardiac mesoderm differentiation

Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization^1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm^-/- cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm^-/- cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle-node bifurcations². In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

Genetic and environmental exposures cause variability in gene expression. Although most genes are affected in a population, their effect sizes vary greatly, indicating the existence of regulatory mechanisms that could amplify or attenuate expression variability. Here, we investigate the relationship between the sequence and transcription start site architectures of promoters and their expression variability across human individuals. We find that expression variability can be largely explained by a promoter's DNA sequence and its binding sites for specific transcription factors. We show that promoter expression variability reflects the biological process of a gene, demonstrating a selective trade-off between stability for metabolic genes and plasticity for responsive genes and those involved in signaling. Promoters with a rigid transcription start site architecture are more prone to have variable expression and to be associated with genetic variants with large effect sizes, while a flexible usage of transcription start sites within a promoter attenuates expression variability and limits genotypic effects. Our work provides insights into the variable nature of responsive genes and reveals a novel mechanism for supplying transcriptional and mutational robustness to essential genes through multiple transcription start site regions within a promoter.

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in "transport", which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution.. [PMID: 35313584 PMCID: PMC8936121 DOI: 10.1101/2021.09.30.462672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Synthetic glucocorticoids, such as dexamethasone, have been used as treatment for many immune conditions, such as asthma and more recently severe COVID-19. Single cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

BACKGROUND

Standard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister and Satija (Genome Biol 20:296, 2019) recommend using Pearson residuals from negative binomial regression, while Townes et al. (Genome Biol 20:295, 2019) recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing.

RESULTS

We show that the model of Hafemeister and Satija produces noisy parameter estimates because it is overspecified, which is why the original paper employs post hoc smoothing. When specified more parsimoniously, it has a simple analytic solution equivalent to the rank-one Poisson GLM-PCA of Townes et al. Further, our analysis indicates that per-gene overdispersion estimates in Hafemeister and Satija are biased, and that the data are in fact consistent with the overdispersion parameter being independent of gene expression. We then use negative control data without biological variability to estimate the technical overdispersion of UMI counts, and find that across several different experimental protocols, the data are close to Poisson and suggest very moderate overdispersion. Finally, we perform a benchmark to compare the performance of Pearson residuals, variance-stabilizing transformations, and GLM-PCA on scRNA-seq datasets with known ground truth.

CONCLUSIONS

We demonstrate that analytic Pearson residuals strongly outperform other methods for identifying biologically variable genes, and capture more of the biologically meaningful variation when used for dimensionality reduction.

Interleukin-6 actions in the hypothalamus protects against obesity and is involved in the regulation of neurogenesis

Background

Interleukin-6 (IL6) produced in the context of exercise acts in the hypothalamus reducing obesity-associated inflammation and restoring the control of food intake and energy expenditure. In the hippocampus, some of the beneficial actions of IL6 are attributed to its neurogenesis-inducing properties. However, in the hypothalamus, the putative neurogenic actions of IL6 have never been explored, and its potential to balance energy intake can be an approach to prevent or attenuate obesity.

Methods

Wild-type (WT) and IL6 knockout (KO) mice were employed to study the capacity of IL6 to induce neurogenesis. We used cell labeling with Bromodeoxyuridine (BrdU), immunofluorescence, and real-time PCR to determine the expression of markers of neurogenesis and neurotransmitters. We prepared hypothalamic neuroprogenitor cells from KO that were treated with IL6 in order to provide an ex vivo model to further characterizing the neurogenic actions of IL6 through differentiation assays. In addition, we analyzed single-cell RNA sequencing data and determined the expression of IL6 and IL6 receptor in specific cell types of the murine hypothalamus.

Results

IL6 expression in the hypothalamus is low and restricted to microglia and tanycytes, whereas IL6 receptor is expressed in microglia, ependymocytes, endothelial cells, and astrocytes. Exogenous IL6 reduces diet-induced obesity. In outbred mice, obesity-resistance is accompanied by increased expression of IL6 in the hypothalamus. IL6 induces neurogenesis-related gene expression in the hypothalamus and in neuroprogenitor cells, both from WT as well as from KO mice.

Conclusion

IL6 induces neurogenesis-related gene expression in the hypothalamus of WT mice. In KO mice, the neurogenic actions of IL6 are preserved; however, the appearance of new fully differentiated proopiomelanocortin (POMC) and neuropeptide Y (NPY) neurons is either delayed or disturbed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02242-8.

A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions

Stochastic fluctuations in gene expression ("noise") are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean expression levels. This amplified expression noise originates from shorter-duration, higher-intensity transcriptional bursts generated by Apex1-mediated DNA supercoiling. The remodeling of DNA topology first impedes and then accelerates transcription to maintain mean levels. This mechanism, which we refer to as "discordant transcription through repair" ("DiThR," which is pronounced "dither"), potentiates cellular reprogramming and differentiation. Our study reveals a potential functional role for transcriptional fluctuations mediated by DNA base modifications in embryonic development and disease.

The triumphs and limitations of computational methods for scRNA-seq

The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.

Assessing single-cell transcriptomic variability through density-preserving data visualization

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.

scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET .

Pluripotent stem cell-derived models of neurological diseases reveal early transcriptional heterogeneity

BACKGROUND

Many neurodegenerative diseases develop only later in life, when cells in the nervous system lose their structure or function. In many forms of neurodegenerative diseases, this late-onset phenomenon remains largely unexplained.

RESULTS

Analyzing single-cell RNA sequencing from Alzheimer's disease (AD) and Huntington's disease (HD) patients, we find increased transcriptional heterogeneity in disease-state neurons. We hypothesize that transcriptional heterogeneity precedes neurodegenerative disease pathologies. To test this idea experimentally, we use juvenile forms (72Q; 180Q) of HD iPSCs, differentiate them into committed neuronal progenitors, and obtain single-cell expression profiles. We show a global increase in gene expression variability in HD. Autophagy genes become more stable, while energy and actin-related genes become more variable in the mutant cells. Knocking down several differentially variable genes results in increased aggregate formation, a pathology associated with HD. We further validate the increased transcriptional heterogeneity in CHD8+/- cells, a model for autism spectrum disorder.

CONCLUSIONS

Overall, our results suggest that although neurodegenerative diseases develop over time, transcriptional regulation imbalance is present already at very early developmental stages. Therefore, an intervention aimed at this early phenotype may be of high diagnostic value.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

Gene expression variability in human and chimpanzee populations share common determinants

Inter-individual variation in gene expression has been shown to be heritable and is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

Predictive features of gene expression variation reveal mechanistic link with differential expression

For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine-learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell-to-cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter-related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while genes with high variation seem to lack both types of stabilizing mechanisms. Applying this framework to humans revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also partially predict differential expression after diverse perturbations in both Drosophila and humans. Differential gene expression signatures may therefore be partially explained by genetically encoded gene-specific features, unrelated to the studied treatment.

Prostate cancer cell-intrinsic interferon signaling regulates dormancy and metastatic outgrowth in bone

The latency associated with bone metastasis emergence in castrate-resistant prostate cancer is attributed to dormancy, a state in which cancer cells persist prior to overt lesion formation. Using single-cell transcriptomics and ex vivo profiling, we have uncovered the critical role of tumor-intrinsic immune signaling in the retention of cancer cell dormancy. We demonstrate that loss of tumor-intrinsic type I IFN occurs in proliferating prostate cancer cells in bone. This loss suppresses tumor immunogenicity and therapeutic response and promotes bone cell activation to drive cancer progression. Restoration of tumor-intrinsic IFN signaling by HDAC inhibition increased tumor cell visibility, promoted long-term antitumor immunity, and blocked cancer growth in bone. Key findings were validated in patients, including loss of tumor-intrinsic IFN signaling and immunogenicity in bone metastases compared to primary tumors. Data herein provide a rationale as to why current immunotherapeutics fail in bone-metastatic prostate cancer, and provide a new therapeutic strategy to overcome the inefficacy of immune-based therapies in solid cancers.

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from "regularized negative binomial regression," where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

Revealing dynamics of gene expression variability in cell state space

To decipher cell state transitions from single-cell transcriptomes it is crucial to quantify weak expression of lineage determining factors, requiring computational methods sensitive to variability of lowly expressed genes. We here introduce VarID, a computational method that identifies locally homogenous neighborhoods in cell state space, permitting the quantification of local gene expression variability. VarID delineates neighborhoods with differential gene expression variability and reveals pseudo-temporal dynamics of variability during differentiation.

Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers

Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.