Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data

Self-organization underlies developmental robustness in plants

Development is a self-organized process that builds on cells and their interactions. Cells are heterogeneous in gene expression, growth, and division; yet how development is robust despite such heterogeneity is a fascinating question. Here, we review recent progress on this topic, highlighting how developmental robustness is achieved through self-organization. We will first discuss sources of heterogeneity, including stochastic gene expression, heterogeneity in growth rate and direction, and heterogeneity in division rate and precision. We then discuss cellular mechanisms that buffer against such noise, including Paf1C- and miRNA-mediated denoising, spatiotemporal growth averaging and compensation, mechanisms to improve cell division precision, and coordination of growth rate and developmental timing between different parts of an organ. We also discuss cases where such heterogeneity is not buffered but utilized for development. Finally, we highlight potential directions for future studies of noise and developmental robustness.

Somatosensory neurons express specific sets of lincRNAs, and lincRNA CLAP promotes itch sensation in mice

Somatosensory neurons are highly heterogeneous with distinct types of neural cells responding to specific stimuli. However, the distribution and roles of cell-type-specific long intergenic noncoding RNAs (lincRNAs) in somatosensory neurons remain largely unexplored. Here, by utilizing droplet-based single-cell RNA-seq (scRNA-seq) and full-length Smart-seq2, we show that lincRNAs, but not coding mRNAs, are enriched in specific types of mouse somatosensory neurons. Profiling of lincRNAs from single neurons located in dorsal root ganglia (DRG) identifies 200 lincRNAs localized in specific types or subtypes of somatosensory neurons. Among them, the conserved cell-type-specific lincRNA CLAP associates with pruritus and is abundantly expressed in somatostatin (SST)-positive neurons. CLAP knockdown reduces histamine-induced Ca2+ influx in cultured SST-positive neurons and in vivo reduces histamine-induced scratching in mice. In vivo knockdown of CLAP also decreases the expression of neuron-type-specific and itch-related genes in somatosensory neurons, and this partially depends on the RNA binding protein MSI2. Our data reveal a cell-type-specific landscape of lincRNAs and a function for CLAP in somatosensory neurons in sensory transmission.

Stochastic expression of invasion genes in Plasmodium falciparum schizonts

Genetically identical cells are known to exhibit differential phenotypes in the same environmental conditions. These phenotypic variants are linked to transcriptional stochasticity and have been shown to contribute towards adaptive flexibility of a wide range of unicellular organisms. Here, we investigate transcriptional heterogeneity and stochastic gene expression in Plasmodium falciparum by performing the quasilinear multiple annealing and looping based amplification cycles (MALBAC) based amplification and single cell RNA sequencing of blood stage schizonts. Our data reveals significant transcriptional variations in the schizont stage with a distinct group of highly variable invasion gene transcripts being identified. Moreover, the data reflects several diversification processes including putative developmental “checkpoint”; transcriptomically distinct parasite sub-populations and transcriptional switches in variable gene families (var, rifin, phist). Most of these features of transcriptional variability are preserved in isogenic parasite cell populations (albeit with a lesser amplitude) suggesting a role of epigenetic factors in cell-to-cell transcriptional variations in human malaria parasites. Lastly, we apply quantitative RT-PCR and RNA-FISH approach and confirm stochastic expression of key invasion genes, such as, msp1, msp3, msp7, eba181 and ama1 which represent prime candidates for invasion-blocking vaccines.

Genetically identical cells can be phenotypically diverse to allow adaptive flexibility in a given environment. This phenotypic diversity is driven by epigenetic and transcriptional variability. Here, Tripathi et al. perform scRNA-seq of isogenic and non-isogenic Plasmodium falciparum schizont populations to explore transcriptional heterogeneity and stochastic gene expression during the course of development.

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

Background

To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective.

Methods

We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms.

Results

ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P < 0.05), and its expression was higher in early-stage patients, including T1 (P < 0.05) and N0 (P < 0.05). Additionally, ZC3H12D expression was higher in immune cells displayed by single-cell RNA-sequencing data, especially in Treg cells of lung cancer and CD8 T cells, B cells and CD4 T cells of LUAD. The functional enrichment analysis showed that the co-expressed genes mainly played a role in lymphocyte activation and cytokine-cytokine receptor interaction. In addition, ZC3H12D was associated with multiple immune cells and immune molecules, including immune checkpoints CTLA4, CD96 and TIGIT.

Conclusion

ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08992-1.

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

Recent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this, we used transcriptomes that were generated from genetically identical individual plants that were grown under the same conditions and for the same amount of time. Twelve time points were used to cover the 24-h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcriptional regulators GI, PIF4, and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

Single-Cell Expression Variability Implies Cell Function

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.

Paf1c defects challenge the robustness of flower meristem termination in Arabidopsis thaliana

Although accumulating evidence suggests that gene regulation is highly stochastic, genetic screens have successfully uncovered master developmental regulators, questioning the relationship between transcriptional noise and intrinsic robustness of development. To identify developmental modules that are more or less resilient to large-scale genetic perturbations, we used the Arabidopsis polymerase II-associated factor 1 complex (Paf1c) mutant vip3, which is impaired in several RNA polymerase II-dependent transcriptional processes. We found that the control of flower termination was not as robust as classically pictured. In angiosperms, the floral female organs, called carpels, display determinate growth: their development requires the arrest of stem cell maintenance. In vip3 mutant flowers, carpels displayed a highly variable morphology, with different degrees of indeterminacy defects up to wild-type size inflorescence emerging from carpels. This phenotype was associated with variable expression of two key regulators of flower termination and stem cell maintenance in flowers, WUSCHEL and AGAMOUS. The phenotype was also dependent on growth conditions. Together, these results highlight the surprisingly plastic nature of stem cell maintenance in plants and its dependence on Paf1c.

Summary: Using a mutant with increased transcriptional noise, we reveal that stem cell maintenance is not as robust as anticipated in plants, even leading to major defects in essential developmental processes such as flower indeterminacy.

Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Expression dynamics of repetitive DNA in early human embryonic development

BACKGROUND

The last decade witnessed a number of genome-wide studies on human pre-implantation, which mostly focused on genes and provided only limited information on repeats, excluding the satellites. Considering the fact that repeats constitute a large portion of our genome with reported links to human physiology and disease, a thorough understanding of their spatiotemporal regulation during human embryogenesis will give invaluable clues on chromatin dynamics across time and space. Therefore, we performed a detailed expression analysis of all repetitive DNA elements including the satellites across stages of human pre-implantation and embryonic stem cells.

RESULTS

We uncovered stage-specific expressions of more than a thousand repeat elements whose expressions fluctuated with a mild global decrease at the blastocyst stage. Most satellites were highly expressed at the 4-cell level and expressions of ACRO1 and D20S16 specifically peaked at this point. Whereas all members of the SVA elements were highly upregulated at 8-cell and morula stages, other transposons and small RNA repeats exhibited a high level of variation among their specific subtypes. Our repeat enrichment analysis in gene promoters coupled with expression correlations highlighted potential links between repeat expressions and nearby genes, emphasising mostly 8-cell and morula specific genes together with SVA_D, LTR5_Hs and LTR70 transposons. The DNA methylation analysis further complemented the understanding on the mechanistic aspects of the repeatome's regulation per se and revealed critical stages where DNA methylation levels are negatively correlating with repeat expression.

CONCLUSIONS

Taken together, our study shows that specific expression patterns are not exclusive to genes and long non-coding RNAs but the repeatome also exhibits an intriguingly dynamic pattern at the global scale. Repeats identified in this study; particularly satellites, which were historically associated with heterochromatin, and those with potential links to nearby gene expression provide valuable insights into the understanding of key events in genomic regulation and warrant further research in epigenetics, genomics and developmental biology.

Single-Cell RNA-Sequencing and Optical Electrophysiology of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Reveal Discordance Between Cardiac Subtype-Associated Gene Expression Patterns and Electrophysiological Phenotypes

The ability to accurately phenotype cells differentiated from human induced pluripotent stem cells (hiPSCs) is essential for their application in modeling developmental and disease processes, yet also poses a particular challenge without the context of anatomical location. Our specific objective was to determine if single-cell gene expression was sufficient to predict the electrophysiology of iPSC-derived cardiac lineages, to evaluate the concordance between molecular and functional surrogate markers. To this end, we used the genetically encoded voltage indicator ArcLight to profile hundreds of hiPSC-derived cardiomyocytes (hiPSC-CMs), thus identifying patterns of electrophysiological maturation and increased prevalence of cells with atrial-like action potentials (APs) between days 11 and 42 of differentiation. To profile expression patterns of cardiomyocyte subtype-associated genes, single-cell RNA-seq was performed at days 12 and 40 after the populations were fully characterized with the high-throughput ArcLight platform. Although we could detect global gene expression changes supporting progressive differentiation, individual cellular expression patterns alone were not able to delineate the individual cardiomyocytes into atrial, ventricular, or nodal subtypes as functionally documented by electrophysiology measurements. Furthermore, our efforts to understand the distinct electrophysiological properties associated with day 12 versus day 40 hiPSC-CMs revealed that ion channel regulators SLMAP, FGF12, and FHL1 were the most significantly increased genes at day 40, categorized by electrophysiology-related gene functions. Notably, FHL1 knockdown during differentiation was sufficient to significantly modulate APs toward ventricular-like electrophysiology. Thus, our results establish the inability of subtype-associated gene expression patterns to specifically categorize hiPSC-derived cells according to their functional electrophysiology, and yet, altered FHL1 expression is able to redirect electrophysiological maturation of these developing cells. Therefore, noncanonical gene expression patterns of cardiac maturation may be sufficient to direct functional maturation of cardiomyocytes, with canonical gene expression patterns being insufficient to temporally define cardiac subtypes of in vitro differentiation.

Transcriptomics of Arabidopsis sperm cells at single-cell resolution

We present a detailed protocol for isolation of single sperm cells and transcriptome analysis to study variation in gene expression between sperm cells. Male gametophyte development in flowering plants begins with a microspore mother cell, which upon two consecutive cell divisions forms a mature pollen grain containing a vegetative nucleus and two sperm cells. Pollen development is a highly dynamic process, involving changes at both the transcriptome and epigenome levels of vegetative nuclei and the pair of sperm cells that have their own cytoplasm and nucleus. While the overall transcriptome of Arabidopsis pollen development is well documented, studies at single-cell level, in particular of sperm cells, are still lacking. Such studies would be essential to understand whether and how the two sperm cells are transcriptionally different, in particular once the pollen tube grows through the transmitting tissue of the pistil. Here we describe a detailed protocol for isolation of single sperm cells from growing pollen tubes and analysis of their transcriptome.

Widespread inter-individual gene expression variability in Arabidopsis thaliana

A fundamental question in biology is how gene expression is regulated to give rise to a phenotype. However, transcriptional variability is rarely considered although it could influence the relationship between genotype and phenotype. It is known in unicellular organisms that gene expression is often noisy rather than uniform, and this has been proposed to be beneficial when environmental conditions are unpredictable. However, little is known about inter-individual transcriptional variability in multicellular organisms. Using transcriptomic approaches, we analysed gene expression variability between individual Arabidopsis thaliana plants growing in identical conditions over a 24-h time course. We identified hundreds of genes that exhibit high inter-individual variability and found that many are involved in environmental responses, with different classes of genes variable between the day and night. We also identified factors that might facilitate gene expression variability, such as gene length, the number of transcription factors regulating the genes and the chromatin environment. These results shed new light on the impact of transcriptional variability in gene expression regulation in plants.

Single-cell systems biology: probing the basic unit of information flow

Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.

Delineating biological and technical variance in single cell expression data

Single cell transcriptomics is becoming a common technique to unravel new biological phenomena whose functional significance can only be understood in the light of differences in gene expression between single cells. The technology is still in its early days and therefore suffers from many technical challenges. This review discusses the continuous effort to identify and systematically characterise various sources of technical variability in single cell expression data and the need to further develop experimental and computational tools and resources to help deal with it.