Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

The regulation of human cytomegalovirus (hCMV) and human immunodeficiency virus (HIV) gene expression has been studied in single intact mammalian cells. Viral promoters were placed upstream of the firefly luciferase reporter gene and the resulting hybrid reporter constructs were stably integrated into the HeLa cell genome. A highly sensitive photon-counting camera system was used to study the level of gene expression in single intact cells. Luciferase expression was studied in the absence of activators of viral gene expression, in the presence of the HIV-1 TAT transactivator protein, or in the presence of sodium butyrate, a non-viral activator of gene expression. In the absence of any activator of gene expression, while expression was undetectable in most cells, significant levels of basal luciferase activity were observed in a few cells, indicating heterogeneity in gene expression in the cell population. In the presence of the general activator of viral gene expression, sodium butyrate, transcriptional activation from the viral promoters gave rise to significant and relatively homogeneous levels of luciferase expression in a majority of cells. The luciferase imaging technology was used for the real-time analysis of changes of gene expression within a single cell. This non-invasive reporter assay should become important for studies of the temporal regulation of gene expression in single cells.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

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.

Expression Atlas is EMBL-EBI’s resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

A novel in situ double-labeling method for simultaneous detection of mRNA and expressed protein or two different mRNAs

A long-standing goal of developmental biologists is to create developmental fate maps by tracking individual cells through development. Another objective is to perturb the behavior of selected cells and follow the ensuing effects. To this end, we have developed a technique that allows for spatial and temporal control of gene expression in single cells or patches of cells using light to induce gene expression. This technique relies on "caging" the activity of the potent transcriptional activator GAL4VP16 with a photolabile compound, which can be removed with a brief exposure to long-wavelength ultraviolet (UV) light. The caged GAL4VP16 is injected into early-stage embryos, which are aged to the desired point in development, and the cell(s) of interest are irradiated with a brief pulse of long-wavelength UV light. This method has been used extensively in Drosophila, Xenopus, and Zebrafish embryos. The methods for purifying, caging, injection, and photoactivation of the GAL4VP16 protein, and methods for the visualization of marked cells are described in detail.

The rapid progress in gene therapy has expanded our ability to alter genetic structure, necessitating the development of methods for detecting the activity of new vectors. The central concept of a reporter gene is simple: it is a defined nucleotide sequence, which when introduced into a biological system, yields a readily measurable phenotype on expression. This provides a convenient parameter that is correlated to the molecular events associated with genetic expression. In this study we demonstrate that Pseudomonas exotoxin A (PE) can serve as a sensitive reporter gene to detect gene expression in single cells of mouse lung on cationic liposome delivery of PE-encoding DNA in vivo. Furthermore, we show that PE expression can be detected as early as 2 hr after systemic gene delivery in lungs of recipient mice. We compared PE with the widely used beta-galactosidase gene for this purpose. PE induces apoptosis that can be detected by TdT end labeling of DNA fragments (TUNEL assay) Since few expressed PE molecules are necessary to trigger the apoptosis cascade, the minimal amount of PE-encoding plasmid DNA needed for detection of apoptotic cells after systemic delivery was 0.1 microg per animal compared with at least 1 microg for the beta-galactosidase-encoding plasmid DNA. The maximum number of apoptotic cells detected in lungs was about 15-20 times higher than the maximum number of beta-galactosidase-positive cells. Specificity of apoptosis due to PE expression on delivery of the PE-encoding plasmid was shown by prevention of the apoptotic cascade by the caspase inhibitor Z-VAD-fmk.

Coalescing single-cell genomes and transcriptomes to decode breast cancer progression.

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.

Determination of single-cell gene expression in Arabidopsis by capillary electrophoresis with laser induced fluorescence detection

Analysis of gene expression in single cells

Viral gene expression varies significantly among genetically identical cells. The sources of these variations are not well understood and have been suggested to involve both deterministic host differences and stochastic viral host interactions. For herpesviruses, only a limited number of incoming viral genomes initiate expression and replication in each infected cell. To elucidate the effect of this limited number of productively infecting genomes on viral gene expression in single cells, we constructed a set of fluorescence-expressing genetically tagged herpes recombinants. The number of different barcodes originating from a single cell is a good representative of the number of incoming viral genomes replicating (NOIVGR) in that cell. We identified a positive correlation between the NOIVGR and viral gene expression, as measured by the fluorescent protein expressed from the viral genome. This correlation was identified in three distinct cell-types, although the average NOIVGR per cell differed among these cell-types. Among clonal single cells, high housekeeping gene expression levels are not supportive of high viral gene expression, suggesting specific host determinants effecting viral infection. We developed a model to predict NOIVGR from cellular parameters, which supports the notion that viral gene expression is tightly linked to the NOIVGR in single-cells. Our results support the hypothesis that the stochastic nature of viral infection and host cell determinants contribute together to the variability observed among infected cells.

Probability landscape of a stochastic model of gene expression in single cells through exact solution of chemical master equation

Novel Technologies to Detect Minimal Residual Disease

Multicolor Timelapse Luminescence Microscopy: Optimizing Luciferases to Track Fast Gene Dynamics in Single Yeast Cells