Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

High-dimension single-cell analysis applied to cancer

High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

Technical aspects and recommendations for single-cell qPCR

Single cells are basic physiological and biological units that can function individually as well as in groups in tissues and organs. It is central to identify, characterize and profile single cells at molecular level to be able to distinguish different kinds, to understand their functions and determine how they interact with each other. During the last decade several technologies for single-cell profiling have been developed and used in various applications, revealing many novel findings. Quantitative PCR (qPCR) is one of the most developed methods for single-cell profiling that can be used to interrogate several analytes, including DNA, RNA and protein. Single-cell qPCR has the potential to become routine methodology but the technique is still challenging, as it involves several experimental steps and few molecules are handled. Here, we discuss technical aspects and provide recommendation for single-cell qPCR analysis. The workflow includes experimental design, sample preparation, single-cell collection, direct lysis, reverse transcription, preamplification, qPCR and data analysis. Detailed reporting and sharing of experimental details and data will promote further development and make validation studies possible. Efforts aiming to standardize single-cell qPCR open up means to move single-cell analysis from specialized research settings to standard research laboratories.

Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy

Objectives

Important findings can be masked in gene expression studies of mixed cell populations. We examined single-cell gene expression in SLE patient monocytes in the context of clinical and immunological features.

Methods

Monocytes were purified from patients with SLE and controls, and individually isolated for single-cell gene expression measurement. A panel of monocyte-related transcripts were measured in individual classical (CL) and non-classical (NCL) monocytes.

Results

Analyses of both CL and NCL monocytes demonstrated that many genes had a lower expression rate in SLE monocytes than in controls. Unsupervised hierarchical clustering of the CL and NCL data sets demonstrated independent clusters of cells from the patients with SLE that were related to disease activity, type I interferon (IFN) and medication use. Thus, each of these factors exerted a different impact on monocyte gene expression that could be identified separately, and a number of genes correlated uniquely with disease activity. We found within-cell correlations between genes directly induced by type I IFN-induced and other non–IFN-induced genes, suggesting the downstream biological effects of type I IFN in individual human SLE monocytes which differed between CLs and NCLs.

Conclusions

In summary, single-cell gene expression in monocytes was associated with a wide range of clinical and biological features in SLE, providing much greater detail and insight into the cellular biology underlying the disease than previous mixed-cell population studies.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

SCALE: modeling allele-specific gene expression by single-cell RNA sequencing

Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing allows the comparison of expression distribution between the two alleles of a diploid organism and the characterization of allele-specific bursting. Here, we propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that cis control in gene expression overwhelmingly manifests as differences in burst frequency.

A smart ZnO@polydopamine-nucleic acid nanosystem for ultrasensitive live cell mRNA imaging by the target-triggered intracellular self-assembly of active DNAzyme nanostructures

Efficient strategies for the ultrasensitive imaging of gene expression in living cells are essential in chemistry and cell biology. Here, we report a novel and efficient enzyme-free dual signal amplification strategy for live cell mRNA imaging by using a smart nucleic acid hairpin-based nanosystem. This nanosystem consists of a ZnO nanoparticle core, an interlayer of polydopamine and an outer layer of four hairpin DNA (hpDNA) probes. Such a core-shell nanosystem facilitates the cellular uptake of molecular hairpin payloads, protects them from nuclease digestion, and delivers them into the cytoplasm by the acid-triggered dissolution of the ZnO core. In the presence of target mRNA, the released hpDNA probes self-assemble via HCR into wire-shaped active DNAzymes that catalyze the generation of a fluorescence signal. The target-initiated HCR events and DNAzyme cascades offer efficient dual amplification and enable the ultrasensitive detection of mRNA with a femtomolar detection limit. Live cell assays show an intense fluorescence response from a tumor-related biomarker survivin mRNA only in tumor cells untreated with a survivin expression repressor YM155, but not in normal cells. The developed nanosystem provides a potential platform for the amplified imaging of low-abundance disease-related biomarkers in live cells.

Discovering sparse transcription factor codes for cell states and state transitions during development

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships.

Plasma cell treatment device Plasma-on-Chip: Monitoring plasma-generated reactive species in microwells

We have developed a plasma cell treatment device called Plasma-on-Chip that enables the real-time monitoring of a single cell culture during plasma treatment. The device consists of three parts: 1) microwells for cell culture, 2) a microplasma device for generating reactive oxygen and nitrogen species (RONS) for use in cell treatment, and 3) through-holes (microchannels) that connect each microwell with the microplasma region for RONS delivery. Here, we analysed the delivery of the RONS to the liquid culture medium stored in the microwells. We developed a simple experimental set-up using a microdevice and applied in situ ultraviolet absorption spectroscopy with high sensitivity for detecting RONS in liquid. The plasma-generated RONS were delivered into the liquid culture medium via the through-holes fabricated into the microdevice. The RONS concentrations were on the order of 10–100 μM depending on the size of the through-holes. In contrast, we found that the amount of dissolved oxygen was almost constant. To investigate the process of RONS generation, we numerically analysed the gas flow in the through-holes. We suggest that the circulating gas flow in the through-holes promotes the interaction between the plasma (ionised gas) and the liquid, resulting in enhanced RONS concentrations.

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

Massively parallel digital transcriptional profiling of single cells

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3' mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

DNA tetrahedron nanostructures for biological applications: biosensors and drug delivery

Herein, we review and summarise the development and biological applications of DNA tetrahedron, including cellular biosensors and drug delivery systems.

Deformation-induced transitional myofibroblasts contribute to compensatory lung growth

In many mammals, including humans, removal of one lung (pneumonectomy) results in the compensatory growth of the remaining lung. Compensatory growth involves not only an increase in lung size, but also an increase in the number of alveoli in the peripheral lung; however, the process of compensatory neoalveolarization remains poorly understood. Here, we show that the expression of α-smooth muscle actin (SMA)-a cytoplasmic protein characteristic of myofibroblasts-is induced in the pleura following pneumonectomy. SMA induction appears to be dependent on pleural deformation (stretch) as induction is prevented by plombage or phrenic nerve transection (P < 0.001). Within 3 days of pneumonectomy, the frequency of SMA+ cells in subpleural alveolar ducts was significantly increased (P < 0.01). To determine the functional activity of these SMA+ cells, we isolated regenerating alveolar ducts by laser microdissection and analyzed individual cells using microfluidic single-cell quantitative PCR. Single cells expressing the SMA (Acta2) gene demonstrated significantly greater transcriptional activity than endothelial cells or other discrete cell populations in the alveolar duct (P < 0.05). The transcriptional activity of the Acta2+ cells, including expression of TGF signaling as well as repair-related genes, suggests that these myofibroblast-like cells contribute to compensatory lung growth.

Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease

Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.

Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.

The correlation between expression profiles measured in single cells and in traditional bulk samples

Reverse transcription quantitative PCR (RT-qPCR) is already an established tool for mRNA detection and quantification. Since recently, this technique has been successfully employed for gene expression analyses, and also in individual cells (single cell RT-qPCR). Although the advantages of single cell measurements have been proven several times, a study correlating the expression measured on single cells, and in bulk samples consisting of a large number of cells, has been missing. Here, we collected a large data set to explore the relation between gene expression measured in single cells and in bulk samples, reflected by qPCR Cq values. We measured the expression of 95 genes in 12 bulk samples, each containing thousands of astrocytes, and also in 693 individual astrocytes. Combining the data, we described the relation between Cq values measured in bulk samples with either the percentage of the single cells that express the given genes, or the average expression of the genes across the single cells. We show that data obtained with single cell RT-qPCR are fully consistent with measurements in bulk samples. Our results further provide a base for quality control in single cell expression profiling, and bring new insights into the biological process of cellular expression.

Advances in analyzing RNA diversity in eukaryotic transcriptomes: peering through the Omics lens

Alternative splicing, polyadenylation, and chemical modifications of RNA generate astonishing complexity within eukaryotic transcriptomes. The last decade has brought numerous advances in sequencing technologies that allow biologists to investigate these phenomena with greater depth and accuracy while reducing time and cost. A commensurate development in biochemical techniques for the enrichment and analysis of different RNA variants has accompanied the advancement of global sequencing analysis platforms. Here, we present a detailed overview of the latest biochemical methods, along with bioinformatics pipelines that have aided in identifying different RNA variants. We also highlight the ongoing developments and challenges associated with RNA variant detection and quantification, including sample heterogeneity and isolation, as well as 'Omics' big data handling.

Massive and parallel expression profiling using microarrayed single-cell sequencing

Single-cell transcriptome analysis overcomes problems inherently associated with averaging gene expression measurements in bulk analysis. However, single-cell analysis is currently challenging in terms of cost, throughput and robustness. Here, we present a method enabling massive microarray-based barcoding of expression patterns in single cells, termed MASC-seq. This technology enables both imaging and high-throughput single-cell analysis, characterizing thousands of single-cell transcriptomes per day at a low cost (0.13 USD/cell), which is two orders of magnitude less than commercially available systems. Our novel approach provides data in a rapid and simple way. Therefore, MASC-seq has the potential to accelerate the study of subtle clonal dynamics and help provide critical insights into disease development and other biological processes.

Currently available single-cell transcriptomic analyses are expensive and low throughput. Here, Vickovic et al. describe a new method called MASC-seq that is based on microarray barcoding of expression pattern and of low cost with high robustness.

A Microchip for Integrated Single-Cell Gene Expression Profiling and Genotoxicity Detection

Microfluidics-based single-cell study is an emerging approach in personalized treatment or precision medicine studies. Single-cell gene expression holds a potential to provide treatment selections with maximized efficacy to help cancer patients based on a genetic understanding of their disease. This work presents a multi-layer microchip for single-cell multiplexed gene expression profiling and genotoxicity detection. Treated by three drug reagents (i.e., methyl methanesulfonate, docetaxel and colchicine) with varied concentrations and time lengths, individual human cancer cells (MDA-MB-231) are lysed on-chip, and the released mRNA templates are captured and reversely transcribed into single strand DNA. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), cyclin-dependent kinase inhibitor 1A (CDKN1A), and aurora kinase A (AURKA) genes from single cells are amplified and real-time quantified through multiplex polymerase chain reaction. The microchip is capable of integrating all steps of single-cell multiplexed gene expression profiling, and providing precision detection of drug induced genotoxic stress. Throughput has been set to be 18, and can be further increased following the same approach. Numerical simulation of on-chip single cell trapping and heat transfer has been employed to evaluate the chip design and operation.

Oocytes from small and large follicles exhibit similar development competence following goat cloning despite their differences in meiotic and cytoplasmic maturation

Reduced developmental competence after IVF has been reported using oocyte derived from small follicles in several species including cattle, sheep, and goats. No information is currently available about the effect of follicle size of the cytoplast donor on in vivo development after somatic cell nuclear transfer (SCNT) in goats. Oocytes collected from large (≥3 mm) and small follicles (<3 mm) were examined for maturation and in vivo developmental competence after SCNT. Significantly greater maturation rate was observed in oocytes derived from large follicles compared with that of small follicles (51.6% and 33.7%, P < 0.05). Greater percent of large follicle oocytes exhibited a low glucose-6-phosphate dehydrogenase activity at germinal vesicle stage compared with small follicle oocytes (54.9% and 38.7%, P < 0.05). Relative mRNA expression analysis of 48 genes associated with embryonic and fetal development revealed that three genes (MATER, IGF2R, and GRB10) had higher level of expression in metaphase II oocytes from large follicles compared with oocytes from small follicles. Nevertheless, no difference was observed in pregnancy rates (33.3% vs. 47.1%) and birth rates (22.2% vs. 16.7%) after SCNT between the large and small follicle groups). These results indicate that metaphase II cytoplasts from small and large follicles have similar developmental competence when used in goat SCNT.

Cellular identity at the single-cell level

A single cell creates surprising heterogeneity in a multicellular organism. While every organismal cell shares almost an identical genome, molecular interactions in cells alter the use of DNA sequences to modulate the gene of interest for specialization of cellular functions. Each cell gains a unique identity through molecular coding across the DNA, RNA, and protein conversions. On the other hand, loss of cellular identity leads to critical diseases such as cancer. Most cell identity dissection studies are based on bulk molecular assays that mask differences in individual cells. To probe cell-to-cell variability in a population, we discuss single cell approaches to decode the genetic, epigenetic, transcriptional, and translational mechanisms for cell identity formation. In combination with molecular instructions, the physical principles behind cell identity determination are examined. Deciphering and reprogramming cellular types impact biology and medicine.

Genetics and immunity in the era of single-cell genomics

Recent developments in the field of single-cell genomics (SCG) are changing our understanding of how functional phenotypes of cell populations emerge from the behaviour of individual cells. Some of the applications of SCG include the discovery of new gene networks and novel cell subpopulations, fine mapping of transcription kinetics, and the relationships between cell clonality and their functional phenotypes. Immunology is one of the fields that is benefiting the most from such advancements, providing us with completely new insights into mammalian immunity. In this review, we start by covering new immunological insights originating from the use of single-cell genomic tools, specifically single-cell RNA-sequencing. Furthermore, we discuss how new genetic study designs are starting to explain inter-individual variation in the immune response. We conclude with a perspective on new multi-omics technologies capable of integrating several readouts from the same single cell and how such techniques might push our biological understanding of mammalian immunity to a new level.

Novel method to ascertain chromatin accessibility at specific genomic loci from frozen brain homogenates and laser capture microdissected defined cells

We describe a novel method for assessing the "open" or "closed" state of chromatin at selected locations within the genome. This method combines the use of Benzonase, which can digest DNA in the presence of actin, with qPCR to define digested regions. We demonstrate the application of this method in brain homogenates and laser captured cells. We also demonstrate application to selected sites within more than one gene and multiple sites within one gene. We demonstrate the validity of the method by treating cells with valproate, known to render chromatin more permissive, and by comparison with classical digestion with DNase I in an in vitro preparation. Although we demonstrate the use of this method in brain tissue we also recognize its applicability to other tissue types.

Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure

Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.

Single-Cell Isolation of Circulating Tumor Cells from Whole Blood by Lateral Magnetophoretic Microseparation and Microfluidic Dispensing

This paper introduces a single-cell isolation technology for circulating tumor cells (CTCs) using a microfluidic device (the "SIM-Chip"). The SIM-Chip comprises a lateral magnetophoretic microseparator and a microdispenser as a two-step cascade platform. First, CTCs were enriched from whole blood by the lateral magnetophoretic microseparator based on immunomagnetic nanobeads. Next, the enriched CTCs were electrically identified by single-cell impedance cytometer and isolated as single cells using the microshooter. Using 200 μL of whole blood spiked with 50 MCF7 breast cancer cells, the analysis demonstrated that the single-cell isolation efficiency of the SIM-Chip was 82.4%, and the purity of the isolated MCF7 cells with respect to WBCs was 92.45%. The data also showed that the WBC depletion rate of the SIM-Chip was 2.5 × 10(5) (5.4-log). The recovery rates were around 99.78% for spiked MCF7 cells ranging in number from 10 to 90. The isolated single MCF7 cells were intact and could be used for subsequent downstream genetic assays, such as RT-PCR. Single-cell culture evaluation of the proliferation of MCF7 cells isolated by the SIM-Chip showed that 84.1% of cells at least doubled in 5 days. Consequently, the SIM-Chip could be used for single-cell isolation of rare target cells from whole blood with high purity and recovery without cell damage.

The mechanisms shaping the single-cell transcriptional landscape

Recent technological and computational advances in understanding the transcriptional and chromatin features of single cells have begun answering longstanding questions in the extent and impact of biological heterogeneity. Here, we outline the intrinsic and extrinsic mechanisms that underlie the transcriptional and functional diversity within superficially homogeneous populations, and we discuss how fascinating new studies have afforded novel insight into each mechanism. The studies are chosen in part to include initial reports of novel functional genomics tools where the eventual applications will clearly have profound impact on our understanding the dynamics of cell-to-cell transcriptional variation-from individual cells to whole organisms.

Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data

Background

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.

Results

We present a discrete, distributional method for differential gene expression (D³E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D³E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D³E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D³E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

Conclusion

Evaluation using synthetic data shows that D³E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D³E makes it possible to gain additional biological insights.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Suspended Planar-Array Chips for Molecular Multiplexing at the Microscale

A novel suspended planar-array chips technology is described, which effectively allows molecular multiplexing using a single suspended chip to analyze extraordinarily small volumes. The suspended chips are fabricated by combining silicon-based technology and polymer-pen lithography, obtaining increased molecular pattern flexibility, and improving miniaturization and parallel production. The chip miniaturization is so dramatic that it permits the intracellular analysis of living cells.

A low absolute number of expanded transcripts is involved in myotonic dystrophy type 1 manifestation in muscle

Muscular manifestation of myotonic dystrophy type 1 (DM1), a common inheritable degenerative multisystem disorder, is mainly caused by expression of RNA from a (CTG·CAG)_n-expanded DM1 locus. Here, we report on comparative profiling of expression of normal and expanded endogenous or transgenic transcripts in skeletal muscle cells and biopsies from DM1 mouse models and patients in order to help us in understanding the role of this RNA-mediated toxicity. In tissue of HSA^LR mice, the most intensely used ‘muscle-only’ model in the DM1 field, RNA from the α-actin (CTG)250 transgene was at least 1000-fold more abundant than that from the Dmpk gene, or the DMPK gene in humans. Conversely, the DMPK transgene in another line, DM500/DMSXL mice, was expressed ∼10-fold lower than the endogenous gene. Temporal regulation of expanded RNA expression differed between models. Onset of expression occurred remarkably late in HSA^LR myoblasts during in vitro myogenesis whereas Dmpk or DMPK (trans)genes were expressed throughout proliferation and differentiation phases. Importantly, quantification of absolute transcript numbers revealed that normal and expanded Dmpk/DMPK transcripts in mouse models and DM1 patients are low-abundance RNA species. Northern blotting, reverse transcriptase–quantitative polymerase chain reaction, RNA-sequencing and fluorescent in situ hybridization analyses showed that they occur at an absolute number between one and a few dozen molecules per cell. Our findings refine the current RNA dominance theory for DM1 pathophysiology, as anomalous factor binding to expanded transcripts and formation of soluble or insoluble ribonucleoprotein aggregates must be nucleated by only few expanded DMPK transcripts and therefore be a small numbers game.

Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

Sensory neurons are distinguished by distinct signaling networks and receptive characteristics. Thus, sensory neuron types can be defined by linking transcriptome-based neuron typing with the sensory phenotypes. Here we classify somatosensory neurons of the mouse dorsal root ganglion (DRG) by high-coverage single-cell RNA-sequencing (10 950 ± 1 218 genes per neuron) and neuron size-based hierarchical clustering. Moreover, single DRG neurons responding to cutaneous stimuli are recorded using an in vivo whole-cell patch clamp technique and classified by neuron-type genetic markers. Small diameter DRG neurons are classified into one type of low-threshold mechanoreceptor and five types of mechanoheat nociceptors (MHNs). Each of the MHN types is further categorized into two subtypes. Large DRG neurons are categorized into four types, including neurexophilin 1-expressing MHNs and mechanical nociceptors (MNs) expressing BAI1-associated protein 2-like 1 (Baiap2l1). Mechanoreceptors expressing trafficking protein particle complex 3-like and Baiap2l1-marked MNs are subdivided into two subtypes each. These results provide a new system for cataloging somatosensory neurons and their transcriptome databases.

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis

A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.

SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data

Background

Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.

Results

We present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.

Conclusions

SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells

Both intrinsic cell state changes and variations in the composition of stem cell populations have been implicated as contributors to aging. We used single-cell RNA-seq to dissect variability in hematopoietic stem cell (HSC) and hematopoietic progenitor cell populations from young and old mice from two strains. We found that cell cycle dominates the variability within each population and that there is a lower frequency of cells in the G1 phase among old compared with young long-term HSCs, suggesting that they traverse through G1 faster. Moreover, transcriptional changes in HSCs during aging are inversely related to those upon HSC differentiation, such that old short-term (ST) HSCs resemble young long-term (LT-HSCs), suggesting that they exist in a less differentiated state. Our results indicate both compositional changes and intrinsic, population-wide changes with age and are consistent with a model where a relationship between cell cycle progression and self-renewal versus differentiation of HSCs is affected by aging and may contribute to the functional decline of old HSCs.

Transcriptional Control of Stem and Progenitor Potential

Hematopoiesis is characterized by a lifelong balance between hematopoietic stem cell (HSC) self-renewal and differentiation into mature blood populations. Proper instruction of cell fate decisions requires tight homeostatic regulation of transcriptional programs through a combination of epigenetic modifications, management of cis-regulatory elements, and transcription factor activity. Recent work has focused on integrating biochemical, genetic, and evolutionary data sets to gain further insight into these regulatory components. Long noncoding RNA (lncRNA), post-translational modifications of transcription factors, and circadian rhythm add additional layers of complexity. These analyses have provided a wealth of information, much of which has been made available through public databases. Elucidating the regulatory processes that govern hematopoietic transcriptional programs is expected to provide useful insights into hematopoiesis that may be applied broadly across tissue types while enabling the discovery and implementation of therapeutics to treat human disease.

Application of single-cell genomics in cancer: promise and challenges

Recent advances in single-cell genomics are opening up unprecedented opportunities to transform cancer genomics. While bulk tissue genomic analysis across large populations of tumour cells has provided key insights into cancer biology, this approach does not provide the resolution that is critical for understanding the interaction between different genetic events within the cellular hierarchy of the tumour during disease initiation, evolution, relapse and metastasis. Single-cell genomic approaches are uniquely placed to definitively unravel complex clonal structures and tissue hierarchies, account for spatiotemporal cell interactions and discover rare cells that drive metastatic disease, drug resistance and disease progression. Here we present five challenges that need to be met for single-cell genomics to fulfil its potential as a routine tool alongside bulk sequencing. These might be thought of as being challenges related to samples (processing and scale for analysis), sensitivity and specificity of mutation detection, sources of heterogeneity (biological and technical), synergies (from data integration) and systems modelling. We discuss these in the context of recent advances in technologies and data modelling, concluding with implications for moving cancer research into the clinic.

Nature-inspired DNA nanosensor for real-time in situ detection of mRNA in living cells

Rapid and precise in situ detection of gene expressions within a single cell is highly informative and offers valuable insights into its state. Detecting mRNA within single cells in real time and nondestructively remains an important challenge. Using DNA nanotechnology and inspired by nature's many examples of "protective-yet-accessible" exoskeletons, we designed our mRNA nanosensor, nano-snail-inspired nucleic acid locator (nano-SNEL), to illustrate these elements. The design of the nano-SNEL is composed of a sensory molecular beacon module to detect mRNA and a DNA nanoshell component, mimicking the functional anatomy of a snail. Accurate and sensitive visualization of mRNA is achieved by the exceptional protection conferred by the nanoshell to the sensory component from nucleases-mediated degradation by approximately 9-25-fold compared to its unprotected counterpart. Our nano-SNEL design strategy improved cell internalization is a demonstration of accurate, dynamic spatiotemporal resolved detection of RNA transcripts in living cells.

Primed and ready: understanding lineage commitment through single cell analysis

Regulation of lineage commitment in multipotential cells is key to maintaining a balanced hematopoietic output throughout life while retaining the capacity to respond to stress and infection. Cell fate decisions are made by individual stem cells, but population-level analysis obscures the mechanics of cell fate choice by averaging the molecular and functional heterogeneity that exists even in the most highly purified stem cell populations. Therefore, single cell analysis of both molecular and cellular phenotypes is crucial to delineate and interrogate the process of lineage commitment. We review recent single cell expression profiling, imaging, and clonal tracking studies that have provided new insights into commitment, focusing on the hematopoietic system, and suggest how new technologies may illuminate our understanding of lineage commitment in the near future.

Emerging single-cell technologies in immunology

During evolution, the immune system has diversified to protect the host from the extremely wide array of possible pathogens. Until recently, immune responses were dissected by use of global approaches and bulk tools, averaging responses across samples and potentially missing particular contributions of individual cells. This is a strongly limiting factor, considering that initial immune responses are likely to be triggered by a restricted number of cells at the vanguard of host defenses. The development of novel, single-cell technologies is a major innovation offering great promise for basic and translational immunology with the potential to overcome some of the limitations of traditional research tools, such as polychromatic flow cytometry or microscopy-based methods. At the transcriptional level, much progress has been made in the fields of microfluidics and single-cell RNA sequencing. At the protein level, mass cytometry already allows the analysis of twice as many parameters as flow cytometry. In this review, we explore the basis and outcome of immune-cell diversity, how genetically identical cells become functionally different, and the consequences for the exploration of host-immune defense responses. We will highlight the advantages, trade-offs, and potential pitfalls of emerging, single-cell-based technologies and how they provide unprecedented detail of immune responses.

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.

Single cell transcriptomics: methods and applications

Traditionally, gene expression measurements were performed on “bulk” samples containing populations of thousands of cells. Recent advances in genomic technology have made it possible to measure gene expression in hundreds of individual cells at a time. As a result, cellular properties that were previously masked in “bulk” measurements can now be observed directly. In this review, we will survey emerging technologies for single cell transcriptomics, and describe how they are used to study complex disease such as cancer, as well as other biological phenomena such as tissue regeneration, embryonic development, and immune response.