Investigating transcriptional states at single-cell-resolution

Single-cell transcriptomics allows novel insights into aging and circadian processes

Aging and circadian rhythms are two biological processes that affect an organism, although at different time scales. Nevertheless, due to the overlap of their actions, it was speculated that both interfere or interact with each other. However, to address this question, a much deeper insight into these processes is necessary, especially at the cellular level. New methods such as single-cell RNA-sequencing (scRNA-Seq) have the potential to close this gap in our knowledge. In this review, we analyze applications of scRNA-Seq from the aging and circadian rhythm fields and highlight new findings emerging from the analysis of single cells, especially in humans or rodents. Furthermore, we judge the potential of scRNA-Seq to identify common traits of both processes. Overall, this method offers several advantages over more traditional methods analyzing gene expression and will become an important tool to unravel the link between these biological processes.

Alkaline phosphatase-based chromogenic and fluorescence detection method for BaseScope™ In Situ hybridization

The BaseScope™ assay is a novel, highly sensitive RNA in situ hybridization (ISH) technique, allowing detection of short RNA sequences as well as discrimination between single-nucleotide alterations. Multiplexing BaseScope™ ISH with immunofluorescence assay has proven challenging because the diffusion of colorimetric dyes such as Fast Red in aqueous solutions degrades spatial resolution. In this study, we explore alkaline phosphatase-based fluorescent signal detection methods and integrate it with BaseScope™ RNA ISH. We found that Fast Blue BB/NAMP can be used in BaseScope™ ISH for signal detection. Additionally, we found that the calcium binding fluorochromes calcein and xylenol orange can be used to localize alkaline phosphatase activity in both immunohistochemistry (IHC) and BaseScope™ ISH assays. When applied to mouse brain and intestine tissue sections, we successfully detected circular RNA molecules and cell proliferation antigen Ki-67 proteins. This technological advance expanded the substrate selection of alkaline phosphatase-based BaseScope™ RNA ISH to allow researchers and clinical professionals accurately assess RNA targets with immunofluorescent multiplexing.

Global and targeted approaches to single-cell transcriptome characterization

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell's expression status.

Quantitative, in situ analysis of mRNAs and proteins with subcellular resolution

We describe here a method, termed immunoFISH, for simultaneous in situ analysis of the composition and distribution of proteins and individual RNA transcripts in single cells. Individual RNA molecules are labeled by hybridization and target proteins are concurrently stained using immunofluorescence. Multicolor fluorescence images are acquired and analyzed to determine the abundance, composition, and distribution of hybridized probes and immunofluorescence. We assessed the ability of immunoFISH to simultaneous quantify protein and transcript levels and distribution in cultured HER2 positive breast cancer cells and human breast tumor samples. We demonstrated the utility of this assay in several applications including demonstration of the existence of a layer of normal myoepithelial KRT14 expressing cells that separate HER2+ cancer cells from the stromal and immune microenvironment in HER2+ invasive breast cancer. Our studies show that immunoFISH provides quantitative information about the spatial heterogeneity in transcriptional and proteomic features that exist between and within cells.

CD-tagging-MS2: detecting allelic expression of endogenous mRNAs and their protein products in single cells

Discriminating between the mRNA and protein outputs of each of the alleles of an endogenous gene in intact cells, is a difficult task. To examine endogenous transcripts originating from a specific allele, we applied Central Dogma tagging (CD-tagging), which is based on a tag insertion into an endogenous gene by creation of a new exon. Previously, CD-tagging was used to tag endogenous proteins. Here we developed a CD-tagging-MS2 approach in which two tags were inserted in tandem; a fluorescent protein tag in conjunction with the mRNA MS2 tag used for tagging mRNAs in cells. A cell clone library of CD-tagged-MS2 genes was generated, and protein and mRNA distributions were examined and characterized in single cells. Taking advantage of having one allele tagged, we demonstrate how the transcriptional activity of all alleles, tagged and untagged, can be identified using single molecule RNA fluorescence in situ hybridization (smFISH). Allele-specific mRNA expression and localization were quantified under normal and stress conditions. The latter generate cytoplasmic stress granules (SGs) that can store mRNAs, and the distribution of the mRNAs within and outside of the SGs was measured. Altogether, CD-tagging-MS2 is a robust and inexpensive approach for direct simultaneous detection of an endogenous mRNA and its translated protein product in the same cell.

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Single-cell RNA sequencing (scRNA-seq) is an exciting new technology allowing the analysis of transcriptomes from individual cells, and is ideally suited to address the inherent complexity and dynamics of the central nervous system. scRNA-seq has already been applied to the study of molecular taxonomy of the brain. These works have paved the way to expanding our understanding of the nervous system and provide insights into cellular susceptibilities and molecular mechanisms in neurological and neurodegenerative diseases. We discuss recent progress and challenges in applying this technology to advance our understanding of the brain. We advocate the application of scRNA-seq in the discovery of targets and biomarkers as a new approach in developing novel therapeutics for the treatment of neurodegenerative diseases.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling.

Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development

An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that HDDC2 plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.

Effect of Intrinsic Noise on the Phenotype of Cell Populations Featuring Solution Multiplicity: An Artificial lac Operon Network Paradigm

Heterogeneity in cell populations originates from two fundamentally different sources: the uneven distribution of intracellular content during cell division, and the stochastic fluctuations of regulatory molecules existing in small amounts. Discrete stochastic models can incorporate both sources of cell heterogeneity with sufficient accuracy in the description of an isogenic cell population; however, they lack efficiency when a systems level analysis is required, due to substantial computational requirements. In this work, we study the effect of cell heterogeneity in the behaviour of isogenic cell populations carrying the genetic network of lac operon, which exhibits solution multiplicity over a wide range of extracellular conditions. For such systems, the strategy of performing solely direct temporal solutions is a prohibitive task, since a large ensemble of initial states needs to be tested in order to drive the system--through long time simulations--to possible co-existing steady state solutions. We implement a multiscale computational framework, the so-called "equation-free" methodology, which enables the performance of numerical tasks, such as the computation of coarse steady state solutions and coarse bifurcation analysis. Dynamically stable and unstable solutions are computed and the effect of intrinsic noise on the range of bistability is efficiently investigated. The results are compared with the homogeneous model, which neglects all sources of heterogeneity, with the deterministic cell population balance model, as well as with a stochastic model neglecting the heterogeneity originating from intrinsic noise effects. We show that when the effect of intrinsic source of heterogeneity is intensified, the bistability range shifts towards higher extracellular inducer concentration values.

Isotachophoresis for fractionation and recovery of cytoplasmic RNA and nucleus from single cells

There is a substantial need for simultaneous analyses of RNA and DNA from individual single cells. Such analysis provides unique evidence of cell-to-cell differences and the correlation between gene expression and genomic mutation in highly heterogeneous cell populations. We present a novel microfluidic system that leverages isotachophoresis to fractionate and isolate cytoplasmic RNA and genomic DNA (gDNA) from single cells. The system uniquely enables independent, sequence-specific analyses of these critical markers. Our system uses a microfluidic chip with a simple geometry and four end-channel electrodes, and completes the entire process in <5 min, including lysis, purification, fractionation, and delivery to DNA and RNA output reservoirs, each containing high quality and purity aliquots with no measurable cross-contamination of cytoplasmic RNA versus gDNA. We demonstrate our system with simultaneous, sequence-specific quantitation using off-chip RT-qPCR and qPCR for simultaneous cytoplasmic RNA and gDNA analyses, respectively.

Making a big thing of a small cell--recent advances in single cell analysis

Single cell analysis is an emerging field requiring a high level interdisciplinary collaboration to provide detailed insights into the complex organisation, function and heterogeneity of life. This review is addressed to life science researchers as well as researchers developing novel technologies. It covers all aspects of the characterisation of single cells (with a special focus on mammalian cells) from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods. In recent years, tremendous advances have been achieved in all fields of single cell analysis: (1) improved spatial and temporal resolution of imaging techniques to enable the tracking of single molecule dynamics within single cells; (2) increased throughput to reveal unexpected heterogeneity between different individual cells raising the question what characterizes a cell type and what is just natural biological variation; and (3) emerging multimodal approaches trying to bring together information from complementary techniques paving the way for a deeper understanding of the complexity of biological processes. This review also covers the first successful translations of single cell analysis methods to diagnostic applications in the field of tumour research (especially circulating tumour cells), regenerative medicine, drug discovery and immunology.

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Reconstructing complex tissues from single-cell analyses

Techniques for profiling individual cells are rapidly advancing and are providing an unprecedented opportunity for studying the genetic regulation of development and disease. In this issue, Durruthy-Durruthy et al. analyze gene expression at the single-cell level for a simple but highly organized three-dimensional structure, the mouse otocyst.

MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Background

Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable.

Results

We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations.

Conclusions

MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.

Bayesian approach to single-cell differential expression analysis

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

Bayesian approach to single-cell differential expression analysis

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution

The otocyst harbors progenitors for most cell types of the mature inner ear. Developmental lineage analyses and gene expression studies suggest that distinct progenitor populations are compartmentalized to discrete axial domains in the early otocyst. Here, we conducted highly parallel quantitative RT-PCR measurements on 382 individual cells from the developing otocyst and neuroblast lineages to assay 96 genes representing established otic markers, signaling-pathway-associated transcripts, and novel otic-specific genes. By applying multivariate cluster, principal component, and network analyses to the data matrix, we were able to readily distinguish the delaminating neuroblasts and to describe progressive states of gene expression in this population at single-cell resolution. It further established a three-dimensional model of the otocyst in which each individual cell can be precisely mapped into spatial expression domains. Our bioinformatic modeling revealed spatial dynamics of different signaling pathways active during early neuroblast development and prosensory domain specification.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.

The importance of tissue specificity for RNA-seq: highlighting the errors of composite structure extractions

Background

A composite biological structure, such as an insect head or abdomen, contains many internal structures with distinct functions. Composite structures are often used in RNA-seq studies, though it is unclear how expression of the same gene in different tissues and structures within the same structure affects the measurement (or even utility) of the resulting patterns of gene expression. Here we determine how complex composite tissue structure affects measures of gene expression using RNA-seq.

Results

We focus on two structures in the honey bee (the sting gland and digestive tract) both contained within one larger structure, the whole abdomen. For each of the three structures, we used RNA-seq to identify differentially expressed genes between two developmental stages, nurse bees and foragers. Based on RNA-seq for each structure-specific extraction, we found that RNA-seq with composite structures leads to many false negatives (genes strongly differentially expressed in particular structures which are not found to be differentially expressed within the composite structure). We also found a significant number of genes with one pattern of differential expression in the tissue-specific extraction, and the opposite in the composite extraction, suggesting multiple signals from such genes within the composite structure. We found these patterns for different classes of genes including transcription factors.

Conclusions

Many RNA-seq studies currently use composite extractions, and even whole insect extractions, when tissue and structure specific extractions are possible. This is due to the logistical difficultly of micro-dissection and unawareness of the potential errors associated with composite extractions. The present study suggests that RNA-seq studies of composite structures are prone to false negatives and difficult to interpret positive signals for genes with variable patterns of local expression. In general, our results suggest that RNA-seq on large composite structures should be avoided unless it is possible to demonstrate that the effects shown here do not exist for the genes of interest.

Single-molecule fluorescence in situ hybridization: quantitative imaging of single RNA molecules

In situ detection of RNAs is becoming increasingly important for analysis of gene expression within and between intact cells in tissues. International genomics efforts are now cataloging patterns of RNA transcription that play roles in cell function, differentiation, and disease formation, and they are demonstrating the importance of coding and noncoding RNA transcripts in these processes. However, these techniques typically provide ensemble averages of transcription across many cells. In situ hybridization-based analysis methods complement these studies by providing information about how expression levels change between cells within normal and diseased tissues, and they provide information about the localization of transcripts within cells, which is important in understanding mechanisms of gene regulation. Multi-color, single-molecule fluorescence in situ hybridization (smFISH) is particularly useful since it enables analysis of several different transcripts simultaneously. Combining smFISH with immunofluorescent protein detection provides additional information about the association between transcription level, cellular localization, and protein expression in individual cells. [BMB Reports 2013; 46(2): 65-72]

The applications of single-cell genomics

We all start out as a single totipotent cell that is programmed to produce a multicellular organism. How do individual cells make those complex developmental switches? How do single cells within a tissue or organ differ, how do they coordinate their actions or go astray in a disease process? These are long-standing and fundamental questions in biology that are now becoming tractable because of advances in microfluidics, DNA amplification and DNA sequencing. Methods for studying single-cell transcriptomes (or at least the polyadenylated mRNA fraction of it) are by far the furthest ahead and reveal remarkable heterogeneity between morphologically identical cells. The analysis of genomic DNA variation is not far behind. The other 'omics' of single cells pose greater technological obstacles, but they are progressing and promise to yield highly integrated large data sets in the near future.

High-throughput microfluidic single-cell digital polymerase chain reaction

Here we present an integrated microfluidic device for the high-throughput digital polymerase chain reaction (dPCR) analysis of single cells. This device allows for the parallel processing of single cells and executes all steps of analysis, including cell capture, washing, lysis, reverse transcription, and dPCR analysis. The cDNA from each single cell is distributed into a dedicated dPCR array consisting of 1020 chambers, each having a volume of 25 pL, using surface-tension-based sample partitioning. The high density of this dPCR format (118,900 chambers/cm(2)) allows the analysis of 200 single cells per run, for a total of 204,000 PCR reactions using a device footprint of 10 cm(2). Experiments using RNA dilutions show this device achieves shot-noise-limited performance in quantifying single molecules, with a dynamic range of 10(4). We performed over 1200 single-cell measurements, demonstrating the use of this platform in the absolute quantification of both high- and low-abundance mRNA transcripts, as well as micro-RNAs that are not easily measured using alternative hybridization methods. We further apply the specificity and sensitivity of single-cell dPCR to performing measurements of RNA editing events in single cells. High-throughput dPCR provides a new tool in the arsenal of single-cell analysis methods, with a unique combination of speed, precision, sensitivity, and specificity. We anticipate this approach will enable new studies where high-performance single-cell measurements are essential, including the analysis of transcriptional noise, allelic imbalance, and RNA processing.

A fully unsupervised compartment-on-demand platform for precise nanoliter assays of time-dependent steady-state enzyme kinetics and inhibition

The ability to miniaturize biochemical assays in water-in-oil emulsion droplets allows a massive scale-down of reaction volumes, so that high-throughput experimentation can be performed more economically and more efficiently. Generating such droplets in compartment-on-demand (COD) platforms is the basis for rapid, automated screening of chemical and biological libraries with minimal volume consumption. Herein, we describe the implementation of such a COD platform to perform high precision nanoliter assays. The coupling of a COD platform to a droplet absorbance detection set-up results in a fully automated analytical system. Michaelis–Menten parameters of 4-nitrophenyl glucopyranoside hydrolysis by sweet almond β-glucosidase can be generated based on 24 time-courses taken at different substrate concentrations with a total volume consumption of only 1.4 μL. Importantly, kinetic parameters can be derived in a fully unsupervised manner within 20 min: droplet production (5 min), initial reading of the droplet sequence (5 min), and droplet fusion to initiate the reaction and read-out over time (10 min). Similarly, the inhibition of the enzymatic reaction by conduritol B epoxide and 1-deoxynojirimycin was measured, and K_i values were determined. In both cases, the kinetic parameters obtained in droplets were identical within error to values obtained in titer plates, despite a >10⁴-fold volume reduction, from micro- to nanoliters.