Control of Transcript Variability in Single Mammalian Cells

Variations in brain defects result from cellular mosaicism in the activation of heat shock signalling

Repetitive prenatal exposure to identical or similar doses of harmful agents results in highly variable and unpredictable negative effects on fetal brain development ranging in severity from high to little or none. However, the molecular and cellular basis of this variability is not well understood. This study reports that exposure of mouse and human embryonic brain tissues to equal doses of harmful chemicals, such as ethanol, activates the primary stress response transcription factor heat shock factor 1 (Hsf1) in a highly variable and stochastic manner. While Hsf1 is essential for protecting the embryonic brain from environmental stress, excessive activation impairs critical developmental events such as neuronal migration. Our results suggest that mosaic activation of Hsf1 within the embryonic brain in response to prenatal environmental stress exposure may contribute to the resulting generation of phenotypic variations observed in complex congenital brain disorders.

Prenatal exposure to environmental stressors is known to impair cortical development. Here the authors show that upon exposure to stressors, the activation of Hsf1-Hsp signalling is highly variable among cells in the embryonic cortex of mice, and either too much or too little activation can result in defects in cortical development.

Shaping development by stochasticity and dynamics in gene regulation

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two 'seemingly contradictory' mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Single-Cell and Single-Molecule Analysis of Gene Expression Regulation

Recent advancements in single-cell and single-molecule imaging technologies have resolved biological processes in time and space that are fundamental to understanding the regulation of gene expression. Observations of single-molecule events in their cellular context have revealed highly dynamic aspects of transcriptional and post-transcriptional control in eukaryotic cells. This approach can relate transcription with mRNA abundance and lifetimes. Another key aspect of single-cell analysis is the cell-to-cell variability among populations of cells. Definition of heterogeneity has revealed stochastic processes, determined characteristics of under-represented cell types or transitional states, and integrated cellular behaviors in the context of multicellular organisms. In this review, we discuss novel aspects of gene expression of eukaryotic cells and multicellular organisms revealed by the latest advances in single-cell and single-molecule imaging technology.

The Chromatin Remodeler ISW1 Is a Quality Control Factor that Surveys Nuclear mRNP Biogenesis

Chromatin dynamics play an essential role in regulating DNA transaction processes, but it is unclear whether transcription-associated chromatin modifications control the mRNA ribonucleoparticles (mRNPs) pipeline from synthesis to nuclear exit. Here, we identify the yeast ISW1 chromatin remodeling complex as an unanticipated mRNP nuclear export surveillance factor that retains export-incompetent transcripts near their transcription site. This tethering activity of ISW1 requires chromatin binding and is independent of nucleosome sliding activity or changes in RNA polymerase II processivity. Combination of in vivo UV-crosslinking and genome-wide RNA immunoprecipitation assays show that Isw1 and its cofactors interact directly with premature mRNPs. Our results highlight that the concerted action of Isw1 and the nuclear exosome ensures accurate surveillance mechanism that proofreads the efficiency of mRNA biogenesis.

Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network

Cell-to-cell variation in gene expression and the propagation of such variation (PoV or "noise propagation") from one gene to another in the gene network, as reflected by gene-gene correlation across single cells, are commonly observed in single-cell transcriptomic studies and can shape the phenotypic diversity of cell populations. While gene network "rewiring" is known to accompany cellular adaptation to different environments, how PoV changes between environments and its underlying regulatory mechanisms are less understood. Here, we systematically explored context-dependent PoV among genes in human macrophages, utilizing different cytokines as natural perturbations of multiple molecular parameters that may influence PoV. Our single-cell, epigenomic, computational, and stochastic simulation analyses reveal that environmental adaptation can tune PoV to potentially shape cellular heterogeneity by changing parameters such as the degree of phosphorylation and transcription factor-chromatin interactions. This quantitative tuning of PoV may be a widespread, yet underexplored, property of cellular adaptation to distinct environments.

Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis

Stochastic fluctuations, termed "noise," in the level of biological molecules can greatly impact cellular functions. While biological noise can sometimes be detrimental, recent studies have provided an increasing number of examples in which biological noise can be functionally beneficial. Rather than provide an exhaustive review of the growing literature in this field, in this review, we focus on single-cell studies based on quantitative microscopy that have generated a deeper understanding of the sources, characteristics, limitations, and benefits of biological noise. Specifically, we highlight studies showing how noise can help coordinate the expression of multiple downstream target genes, impact the channel capacity of signaling networks, and interact synergistically with oscillatory dynamics to enhance the sensitivity of signal processing. We conclude with a discussion of current challenges and future opportunities.

Systems Chronobiology: Global Analysis of Gene Regulation in a 24-Hour Periodic World

Mammals have evolved an internal timing system, the circadian clock, which synchronizes physiology and behavior to the daily light and dark cycles of the Earth. The master clock, located in the suprachiasmatic nucleus (SCN) of the brain, takes fluctuating light input from the retina and synchronizes other tissues to the same internal rhythm. The molecular clocks that drive these circadian rhythms are ticking in nearly all cells in the body. Efforts in systems chronobiology are now being directed at understanding, on a comprehensive scale, how the circadian clock controls different layers of gene regulation to provide robust timing cues at the cellular and tissue level. In this review, we introduce some basic concepts underlying periodicity of gene regulation, and then highlight recent genome-wide investigations on the propagation of rhythms across multiple regulatory layers in mammals, all the way from chromatin conformation to protein accumulation.

Adrenergic Receptors in Individual Ventricular Myocytes: The Beta-1 and Alpha-1B Are in All Cells, the Alpha-1A Is in a Subpopulation, and the Beta-2 and Beta-3 Are Mostly Absent

RATIONALE

It is unknown whether every ventricular myocyte expresses all 5 of the cardiac adrenergic receptors (ARs), β1, β2, β3, α1A, and α1B. The β1 and β2 are thought to be the dominant myocyte ARs.

OBJECTIVE

Quantify the 5 cardiac ARs in individual ventricular myocytes.

METHODS AND RESULTS

We studied ventricular myocytes from wild-type mice, mice with α1A and α1B knockin reporters, and β1 and β2 knockout mice. Using individual isolated cells, we measured knockin reporters, mRNAs, signaling (phosphorylation of extracellular signal-regulated kinase and phospholamban), and contraction. We found that the β1 and α1B were present in all myocytes. The α1A was present in 60%, with high levels in 20%. The β2 and β3 were detected in only ≈5% of myocytes, mostly in different cells. In intact heart, 30% of total β-ARs were β2 and 20% were β3, both mainly in nonmyocytes.

CONCLUSION

The dominant ventricular myocyte ARs present in all cells are the β1 and α1B. The β2 and β3 are mostly absent in myocytes but are abundant in nonmyocytes. The α1A is in just over half of cells, but only 20% have high levels. Four distinct myocyte AR phenotypes are defined: 30% of cells with β1 and α1B only; 60% that also have the α1A; and 5% each that also have the β2 or β3. The results raise cautions in experimental design, such as receptor overexpression in myocytes that do not express the AR normally. The data suggest new paradigms in cardiac adrenergic signaling mechanisms.

The SH-SY5Y cell line in Parkinson's disease research: a systematic review

Parkinson’s disease (PD) is a devastating and highly prevalent neurodegenerative disease for which only symptomatic treatment is available. In order to develop a truly effective disease-modifying therapy, improvement of our current understanding of the molecular and cellular mechanisms underlying PD pathogenesis and progression is crucial. For this purpose, standardization of research protocols and disease models is necessary. As human dopaminergic neurons, the cells mainly affected in PD, are difficult to obtain and maintain as primary cells, current PD research is mostly performed with permanently established neuronal cell models, in particular the neuroblastoma SH-SY5Y lineage. This cell line is frequently chosen because of its human origin, catecholaminergic (though not strictly dopaminergic) neuronal properties, and ease of maintenance. However, there is no consensus on many fundamental aspects that are associated with its use, such as the effects of culture media composition and of variations in differentiation protocols. Here we present the outcome of a systematic review of scientific articles that have used SH-SY5Y cells to explore PD. We describe the cell source, culture conditions, differentiation protocols, methods/approaches used to mimic PD and the preclinical validation of the SH-SY5Y findings by employing alternative cellular and animal models. Thus, this overview may help to standardize the use of the SH-SY5Y cell line in PD research and serve as a future user’s guide.

Effective detection of variation in single-cell transcriptomes using MATQ-seq

The quantification of transcriptional variation in single cells, particularly within the same cell population, is currently limited by the low sensitivity and high technical noise of single-cell RNA-seq assays. We report multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq), a highly sensitive and quantitative method for single-cell sequencing of total RNA. By systematically determining technical noise, we show that MATQ-seq captures genuine biological variation between whole transcriptomes of single cells.

On the Dependency of Cellular Protein Levels on mRNA Abundance

The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway

Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.

Individual cell behaviors are controlled by a variety of noise, such as fluctuations in biochemical reactions, protein variability, molecular diffusion, transcriptional noise, cell-to-cell contact, temperature, and pH. Such cellular noise often interferes with signal responses from external stimuli, and such heterogeneity functions in induction of drug resistance, survival, and migration of cells. Thus, heterogeneous cellular responses have positive and negative roles. However, the regulatory mechanisms that produce cellular heterogeneity are unclear. By mathematical modeling and simulations, we investigated how heterogeneous signaling responses are evoked in the EGF signaling pathway and influence the switch-like activation of nuclear ERK. This study demonstrated that cellular heterogeneity of the EGF signaling response is evoked by cell-to-cell variation of particular signaling proteins, such as EGFR, Ras, Raf, and MEK, which act as sensitive nodes in the pathway. These results suggest that signaling responses in individual cells can be predicted from the levels of proteins of sensitive nodes. This study also suggested that proteins of sensitive nodes may serve as cell survival mechanisms.

Integrative classification of human coding and noncoding genes through RNA metabolism profiles

Pervasive transcription of the human genome results in a heterogeneous mix of coding RNAs and long noncoding RNAs (lncRNAs). Only a small fraction of lncRNAs have demonstrated regulatory functions, thus making functional lncRNAs difficult to distinguish from nonfunctional transcriptional byproducts. This difficulty has resulted in numerous competing human lncRNA classifications that are complicated by a steady increase in the number of annotated lncRNAs. To address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and noncoding human genes. We observed that annotated lncRNAs had lower synthesis and higher degradation rates than mRNAs and discovered mechanistic differences explaining slower lncRNA splicing. We grouped genes into classes with similar RNA metabolism profiles, containing both mRNAs and lncRNAs to varying extents. These classes exhibited distinct RNA metabolism, different evolutionary patterns and differential sensitivity to cellular RNA-regulatory pathways. Our classification provides an alternative to genomic context-driven annotations of lncRNAs.

Exploring viral infection using single-cell sequencing

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication.

Single Cell Peptide Heterogeneity of Rat Islets of Langerhans

Measuring the chemical composition of individual cells in mammalian organs can provide critical insights toward understanding the mechanisms leading to their normal and pathological function. In this work, single cell heterogeneity of islets of Langerhans is characterized with high throughput by microscopy-guided single cell matrix-assisted laser desorption/ionization mass spectrometry. Two levels of chemical heterogeneity were observed from the analysis of more than 3000 individual cells. Within a single islet, cellular heterogeneity was evident from the exclusive expression of the canonical biomarkers glucagon, insulin, pancreatic polypeptide (PP), and somatostatin within α-, β-, γ-, and δ-cells, respectively. We localized the neuropeptide WE-14, a known cell-to-cell signaling molecule, to individual δ-cells. Moreover, several unreported endogenous peptides generated by dibasic site cleavages of PP were detected within individual γ-cells. Of these, PP(27-36) was previously shown to activate the human Y4 receptor, suggesting it has a signaling role in vivo. Heterogeneity in cell composition was also observed between islets as evidenced by a 50-fold larger α-cell population in islets of the dorsal pancreas compared to the ventral-derived pancreatic islets. Finally, PP(27-36) was more abundant in γ-cells from the ventral region of the pancreas, indicating differences in the extent of PP-prohormone processing in the two regions of the pancreas.

The molecular hallmarks of epigenetic control

Over the past 20 years, breakthrough discoveries of chromatin-modifying enzymes and associated mechanisms that alter chromatin in response to physiological or pathological signals have transformed our knowledge of epigenetics from a collection of curious biological phenomena to a functionally dissected research field. Here, we provide a personal perspective on the development of epigenetics, from its historical origins to what we define as 'the modern era of epigenetic research'. We primarily highlight key molecular mechanisms of and conceptual advances in epigenetic control that have changed our understanding of normal and perturbed development.

Single-Cell Genomics for Virology

Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review.

Potentials of single-cell biology in identification and validation of disease biomarkers

Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers.

Cell-to-Cell Transcript Variability: Seeing Signal in the Noise

How stochastic is gene expression in mammalian cells? Not very, according to Battich et al., who report that single-cell variability in cytoplasmic mRNAs is remarkably predictable given measurements of a cell's phenotypic state and microenvironment. The noise from transcriptional bursts is buffered by a hallmark of eukaryotes-the nucleus.

Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method

Recent advances have created new opportunities to dissect cellular heterogeneity at the omics level. The enthusiasm for deep single-cell profiling has obscured a discussion of different types of heterogeneity and the most-appropriate techniques for studying each type. Here, I distinguish heterogeneity in regulation from heterogeneity in lineage. Snapshots of lineage heterogeneity provide a cell atlas that catalogs cellular diversity within complex tissues. Profiles of regulatory heterogeneity seek to interrogate one lineage deeply to capture an ensemble of single-cell states. Single-cell atlases require molecular signatures from many cells at a throughput afforded by mass cytometry-based, microfluidic-based, and microencapsulation-based methods. Single-cell states are more dependent on time, microenvironment, and low-abundance transcripts, emphasizing in situ methods that stress depth of profiling and quantitative accuracy.

Single-cell analysis of transcription kinetics across the cell cycle

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.

Nuclear Retention of mRNA in Mammalian Tissues

mRNA is thought to predominantly reside in the cytoplasm, where it is translated and eventually degraded. Although nuclear retention of mRNA has a regulatory potential, it is considered extremely rare in mammals. Here, to explore the extent of mRNA retention in metabolic tissues, we combine deep sequencing of nuclear and cytoplasmic RNA fractions with single-molecule transcript imaging in mouse beta cells, liver, and gut. We identify a wide range of protein-coding genes for which the levels of spliced polyadenylated mRNA are higher in the nucleus than in the cytoplasm. These include genes such as the transcription factor ChREBP, Nlrp6, Glucokinase, and Glucagon receptor. We demonstrate that nuclear retention of mRNA can efficiently buffer cytoplasmic transcript levels from noise that emanates from transcriptional bursts. Our study challenges the view that transcripts predominantly reside in the cytoplasm and reveals a role of the nucleus in dampening gene expression noise.

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Genome-wide catalog of nuclear and cytoplasmic mRNA in mouse tissues

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Spliced, polyadenylated mRNA is retained in the nucleus for many protein-coding genes

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Retained genes include ChREBP and liver Nlrp6, co-localized with nuclear speckles

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Nuclear retention of mRNA reduces cytoplasmic gene expression noise