Cellular Deconstruction: Finding Meaning in Individual Cell Variation

Understanding metabolic plasticity at single cell resolution

It is increasingly clear that cellular metabolic function varies not just between cells of different tissues, but also within tissues and cell types. In this essay, we envision how differences in central carbon metabolism arise from multiple sources, including the cell cycle, circadian rhythms, intrinsic metabolic cycles, and others. We also discuss and compare methods that enable such variation to be detected, including single-cell metabolomics and RNA-sequencing. We pay particular attention to biosensors for AMPK and central carbon metabolites, which when used in combination with metabolic perturbations, provide clear evidence of cellular variance in metabolic function.

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.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

Single-Cell Heterogeneity in Snake Venom Expression Is Hardwired by Co-Option of Regulators from Progressively Activated Pathways

The ubiquitous cellular heterogeneity underlying many organism-level phenotypes raises questions about what factors drive this heterogeneity and how these complex heterogeneous systems evolve. Here, we use single-cell expression data from a Prairie rattlesnake (Crotalus viridis) venom gland to evaluate hypotheses for signaling networks underlying snake venom regulation and the degree to which different venom gene families have evolutionarily recruited distinct regulatory architectures. Our findings suggest that snake venom regulatory systems have evolutionarily co-opted trans-regulatory factors from extracellular signal-regulated kinase and unfolded protein response pathways that specifically coordinate expression of distinct venom toxins in a phased sequence across a single population of secretory cells. This pattern of co-option results in extensive cell-to-cell variation in venom gene expression, even between tandemly duplicated paralogs, suggesting this regulatory architecture has evolved to circumvent cellular constraints. While the exact nature of such constraints remains an open question, we propose that such regulatory heterogeneity may circumvent steric constraints on chromatin, cellular physiological constraints (e.g., endoplasmic reticulum stress or negative protein-protein interactions), or a combination of these. Regardless of the precise nature of these constraints, this example suggests that, in some cases, dynamic cellular constraints may impose previously unappreciated secondary constraints on the evolution of gene regulatory networks that favors heterogeneous expression.

Gene expression underlying parenting and being parented shows limited plasticity in response to different ambient temperatures

Flexible interactions between parents and offspring are essential for buffering families against variable, unpredictable, and challenging environmental conditions. In the subsocial carrion beetle, Nicrophorus orbicollis, mid-summer temperatures impose steep fitness costs on parents and offspring but do not elicit behavioural plasticity in parents. Here, we ask if plasticity of gene expression underpins this behavioural stability or facilitates independent compensation by larvae. To test this, we characterized gene expression of parents and offspring before and during active parenting under benign (20°C) and stressful (24°C) temperatures to identify genes of parents and offspring associated with thermal response, parenting/being parented, and gene expression plasticity associated with behavioural stability of parental care. The main effects of thermal and social condition each shaped patterns of gene expression in females, males, and larvae. In addition, we implicated 79 genes in females as "buffering" parental behaviour across environments. The majority of these underwent significant changes in expression in actively parenting mothers at the benign temperature, but not at the stressful temperature. Our results suggest that neither genetic programmes for parenting nor their effects on offspring gene expression are fundamentally different under stressful conditions, and that behavioural stability is associated primarily with the maintenance of existing genetic programmes rather than replacement or supplementation. Thus, while selection for compensatory gene expression could expand the range of thermal conditions parents will tolerate, without expanding the toolkit of genes involved selection is unlikely to lead to adaptive changes of function.

Single-cell secretion analysis reveals a dual role for IL-10 in restraining and resolving the TLR4-induced inflammatory response

Following Toll-like receptor 4 (TLR4) stimulation of macrophages, negative feedback mediated by the anti-inflammatory cytokine interleukin-10 (IL-10) limits the inflammatory response. However, extensive cell-to-cell variability in TLR4-stimulated cytokine secretion raises questions about how negative feedback is robustly implemented. To explore this, we characterize the TLR4-stimulated secretion program in primary murine macrophages using a single-cell microwell assay that enables evaluation of functional autocrine IL-10 signaling. High-dimensional analysis of single-cell data reveals three tiers of TLR4-induced proinflammatory activation based on levels of cytokine secretion. Surprisingly, while IL-10 inhibits TLR4-induced activation in the highest tier, it also contributes to the TLR4-induced activation threshold by regulating which cells transition from non-secreting to secreting states. This role for IL-10 in restraining TLR4 inflammatory activation is largely mediated by intermediate interferon (IFN)-β signaling, while TNF likely mediates response resolution by IL-10. Thus, cell-to-cell variability in cytokine regulatory motifs provides a means to tailor the TLR4-induced inflammatory response.

Investigating the Effects of Brainstem Neuronal Adaptation on Cardiovascular Homeostasis

Central coordination of cardiovascular function is accomplished, in part, by the baroreceptor reflex, a multi-input multi-output physiological control system that regulates the activity of the parasympathetic and sympathetic nervous systems via interactions among multiple brainstem nuclei. Recent single-cell analyses within the brain revealed that individual neurons within and across brain nuclei exhibit distinct transcriptional states contributing to neuronal function. Such transcriptional heterogeneity complicates the task of understanding how neurons within and across brain nuclei organize and function to process multiple inputs and coordinate cardiovascular functions within the larger context of the baroreceptor reflex. However, prior analysis of brainstem neurons revealed that single-neuron transcriptional heterogeneity reflects an adaptive response to synaptic inputs and that neurons organize into distinct subtypes with respect to synaptic inputs received. Based on these results, we hypothesize that adaptation of neuronal subtypes support robust biological function through graded cellular responses. We test this hypothesis by examining the functional impact of neuronal adaptation on parasympathetic activity within the context of short-term baroreceptor reflex regulation. In this work, we extend existing quantitative closed-loop models of the baroreceptor reflex by incorporating into the model distinct input-driven neuronal subtypes and neuroanatomical groups that modulate parasympathetic activity. We then use this extended model to investigate, via simulation, the functional role of neuronal adaptation under conditions of health and systolic heart failure. Simulation results suggest that parasympathetic activity can be modulated appropriately by the coordination of distinct neuronal subtypes to maintain normal cardiovascular functions under systolic heart failure conditions. Moreover, differing degrees of adaptation of these neuronal subtypes contribute to cardiovascular behaviors corresponding to distinct clinical phenotypes of heart failure, such as exercise intolerance. Further, our results suggest that an imbalance between sympathetic and parasympathetic activity regulating ventricular contractility contributes to exercise intolerance in systolic heart failure patients, and restoring this balance can improve the short-term cardiovascular performance of these patients.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Maeve O’Huallachain et al. report a method that enables simultaneous, ultra-high throughput single-cell barcoding for targeted single-cell protein and RNA analysis. They show the utility of their method in analyses of mRNA and protein expression in human and mouse cells.

Transcriptional Regulation in the Immune System: One Cell at a Time

Transcriptional regulation of cells in the immune system must be strictly controlled at multiple levels to ensure that a proper immune response is elicited only when required. Analysis in bulk, or ensemble of cells, provides a wealth of important information leading to a better understanding of the various molecular steps and mechanisms involved in regulating gene expression in immune cells. However, given the substantial heterogeneity of these cells, it is imperative now to decipher these mechanisms at a single cell level. Here I bring together several recent examples to review our understanding of transcriptional regulation of the immune system via single cell analysis and to further illustrate the immense power of such analyses to interrogate immune cell heterogeneity.

Single-cell analysis of diversity in human stem cell-derived neurons

Neural stem and progenitor cells produce one of the most remarkable organs in nature, the human brain. Among neural stem cell progeny, post-mitotic neurons are likewise remarkably diverse. Single-cell transcriptomic approaches are now cataloging a long-sought-after molecular taxonomy of neuronal diversity in the brain. Contemporary single-cell omic classifications of neuronal diversity build from electrophysiological approaches that for decades have measured and cataloged diverse biophysical properties of single neurons. With the widespread application of human pluripotent stem cell-based models of neurogenesis to investigate disease pathology and to develop new drugs, a high-resolution understanding of neuronal diversity in vivo is essential to benchmark the state of in vitro models of human neurological disease.

Single cell transcriptomics of noncoding RNAs and their cell-specificity

Recent developments of single cell transcriptome profiling methods have led to the realization that many seemingly homogeneous cells have surprising levels of expression variability. The biological implications of the high degree of variability is unclear but one possibility is that many genes are restricted in expression to small lineages of cells, suggesting the existence of many more cell types than previously estimated. Noncoding RNA (ncRNA) are thought to be key parts of gene regulatory processes and their single cell expression patterns may help to dissect the biological function of single cell variability. Technology for measuring ncRNA in single cell is still in development and most of the current single cell datasets have reliable measurements for only long noncoding RNA (lncRNA). Most works report that lncRNAs show lineage-specific restricted expression patterns, which suggest that they might determine, at least in part, lineage fates and cell subtypes. However, evidence is still inconclusive as to whether lncRNAs and other ncRNAs are more lineage-specific than protein-coding genes. Nevertheless, measurement of ncRNAs in single cells will be important for studies of cell types and single cell function. WIREs RNA 2017, 8:e1433. doi: 10.1002/wrna.1433 For further resources related to this article, please visit the WIREs website.

High-Throughput Processing to Preserve Viable Cells: A Precision Medicine Initiative Cohort Program Workshop

Conventionally, biobanks supporting clinical research studies have preserved serum, plasma, urine, saliva, a variety of tissue types, and stool. With the emergence of increasingly sophisticated technologies for analyzing single cells, there is growing interest in preserving viable blood cells for future functional studies. The new All of Us Research Program (formerly the Precision Medicine Initiative Cohort Program) biobank plans to house samples from a million or more individuals as part of a cohort with rich phenotypic data and longitudinal follow-up ( www.nih.gov/research-training/allofus-research-program ). Storage of viable cells for future single-cell analysis offers the promise of new biology, discovery of novel biomarkers, and advances toward the goal of precision medicine. A workshop was held in the summer of 2016 to evaluate the case for preservation of viable mononuclear blood cells and its feasibility within the collection plan for the biobank.

The size-speed-force relationship governs migratory cell response to tumorigenic factors

Normal and transformed motile cells follow a common trend in which size and contractile forces are negatively correlated with cell speed. However, tumorigenic factors amplify the preexisting population heterogeneity and lead some cells to exhibit biomechanical properties that are more extreme than those observed with normal cells.

Tumor development progresses through a complex path of biomechanical changes leading first to cell growth and contraction and then cell deadhesion, scattering, and invasion. Tumorigenic factors may act specifically on one of these steps or have a wider spectrum of actions, leading to a variety of effects and thus sometimes to apparent contradictory outcomes. Here we used micropatterned lines of collagen type I/fibronectin on deformable surfaces to standardize cell behavior and measure simultaneously cell size, speed of motion and magnitude of the associated traction forces at the level of a single cell. We analyzed and compared the normal human breast cell line MCF10A in control conditions and in response to various tumorigenic factors. In all conditions, a wide range of biomechanical properties was identified. Despite this heterogeneity, normal and transformed motile cells followed a common trend whereby size and contractile forces were negatively correlated with cell speed. Some tumorigenic factors, such as activation of ErbB2 or loss of the βsubunit of casein kinase 2, shifted the whole population toward a faster speed and lower contractility state. Treatment with transforming growth factor β induced some cells to adopt opposing behaviors such as extremely high versus extremely low contractility. Thus tumor transformation amplified preexisting population heterogeneity and led some cells to exhibit biomechanical properties that were more extreme than those observed with normal cells.

Dissecting stem cell differentiation using single cell expression profiling

Many assumptions about the way cells behave are based on analyses of populations. However, it is now widely recognized that even apparently pure populations can display a remarkable level of heterogeneity. This is particularly true in stem cell biology where it hinders our understanding of normal development and the development of strategies for regenerative medicine. Over the past decade technologies facilitating gene expression analysis at the single cell level have become widespread, providing access to rare cell populations and insights into population structure and function. Here we review the contributions of single cell biology to understanding stem cell differentiation so far, both as a new methodology for defining cell types and a tool for understanding the complexities of cellular decision-making.

The natural defense system and the normative self model

Infectious agents are not the only agressors, and the immune system is not the sole defender of the organism. In an enlarged perspective, the ‘normative self model’ postulates that a ‘natural defense system’ protects man and other complex organisms against the environmental and internal hazards of life, including infections and cancers. It involves multiple error detection and correction mechanisms that confer robustness to the body at all levels of its organization. According to the model, the self relies on a set of physiological norms, and NONself (meaning : Non Obedient to the Norms of the self) is anything ‘off-norms’. The natural defense system comprises a set of ‘civil defenses’ (to which all cells in organs and tissues contribute), and a ‘professional army ‘, made of a smaller set of mobile cells. Mobile and non mobile cells differ in their tuning abilities. Tuning extends the recognition capabilities of NONself by the mobile cells, which increase their defensive function. To prevent them to drift, which would compromise self/NONself discrimination, the more plastic mobile cells need to periodically refer to the more stable non mobile cells to keep within physiological standards.

Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function

There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single-cell molecular variation per se might be crucial for population-level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of identical cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population-level function. We consider several possible methods to further pursue the hypothesis that variation is function through the use of comparative analysis and novel experimental techniques.