Perfect Sampling of the Master Equation for Gene Regulatory Networks

We present a Perfect Sampling algorithm that can be applied to the Master Equation of Gene Regulatory Networks (GRNs). The method recasts Gillespie's Stochastic Simulation Algorithm (SSA) in the light of Markov Chain Monte Carlo methods and combines it with the Dominated Coupling From The Past (DCFTP) algorithm to provide guaranteed sampling from the stationary distribution. We show how the DCFTP-SSA can be generically applied to genetic networks with feedback formed by the interconnection of linear enzymatic reactions and nonlinear Monod- and Hill-type elements. We establish rigorous bounds on the error and convergence of the DCFTP-SSA, as compared to the standard SSA, through a set of increasingly complex examples. Once the building blocks for GRNs have been introduced, the algorithm is applied to study properly averaged dynamic properties of two experimentally relevant genetic networks: the toggle switch, a two-dimensional bistable system, and the repressilator, a six-dimensional genetic oscillator.Comment: Minor rewriting; final version to be published in Biophysical Journa

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 (D(3)E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D(3)E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D(3)E 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 D(3)E 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 D(3)E 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 D(3)E makes it possible to gain additional biological insights

Quantification of mRNA in single cells and modelling of RT-qPCR induced noise

<p>Abstract</p> <p>Background</p> <p>Gene expression has a strong stochastic element resulting in highly variable mRNA levels between individual cells, even in a seemingly homogeneous cell population. Access to fundamental information about cellular mechanisms, such as correlated gene expression, motivates measurements of multiple genes in individual cells. Quantitative reverse transcription PCR (RT-qPCR) is the most accessible method which provides sufficiently accurate measurements of mRNA in single cells.</p> <p>Results</p> <p>Low concentration of guanidine thiocyanate was used to fully lyse single pancreatic β-cells followed by RT-qPCR without the need for purification. The accuracy of the measurements was determined by a quantitative noise-model of the reverse transcription and PCR. The noise is insignificant for initial copy numbers >100 while at lower copy numbers the noise intrinsic of the PCR increases sharply, eventually obscuring quantitative measurements. Importantly, the model allows us to determine the RT efficiency without using artificial RNA as a standard. The experimental setup was applied on single endocrine cells, where the technical and biological noise levels were determined.</p> <p>Conclusion</p> <p>Noise in single-cell RT-qPCR is insignificant compared to biological cell-to-cell variation in mRNA levels for medium and high abundance transcripts. To minimize the technical noise in single-cell RT-qPCR, the mRNA should be analyzed with a single RT reaction, and a single qPCR reaction per gene.</p

Tau promotes neurodegeneration through global chromatin relaxation

The microtubule-associated protein tau is involved in a number of neurodegenerative disorders, including Alzheimer’s disease (AD). Previous studies link oxidative stress and subsequent DNA damage to neuronal death in AD and related tauopathies. Since DNA damage can significantly alter chromatin structure, we examined epigenetic changes in tau-induced neurodegeneration. We have found widespread loss of heterochromatin in tau transgenic Drosophila and mice, and in human AD. Importantly, genetic rescue of tau-induced heterochromatin loss substantially reduced neurodegeneration in Drosophila. We identified oxidative stress and subsequent DNA damage as a mechanistic link between transgenic tau expression and heterochromatin relaxation, and found that heterochromatin loss permits aberrant gene expression in tauopathies. Furthermore, large-scale analyses from human AD brains revealed a widespread transcriptional increase in genes that are heterochromatically silenced in controls. Our results establish heterochromatin loss as a toxic effector of tau-induced neurodegeneration, and identify chromatin structure as a potential therapeutic target in AD

Integrated Genome Analysis Suggests that Most Conserved Non-Coding Sequences are Regulatory Factor Binding Sites

More than 98% of a typical vertebrate genome does not code for proteins. Although non-coding regions are sprinkled with short (<200 bp) islands of evolutionarily conserved sequences, the function of most of these unannotated conserved islands remains unknown. One possibility is that unannotated conserved islands could encode non-coding RNAs (ncRNAs); alternatively, unannotated conserved islands could serve as promoter-distal regulatory factor binding sites (RFBSs) like enhancers. Here we assess these possibilities by comparing unannotated conserved islands in the human and mouse genomes to transcribed regions and to RFBSs, relying on a detailed case study of one human and one mouse cell type. We define transcribed regions by applying a novel transcript-calling algorithm to RNA-Seq data obtained from total cellular RNA, and we define RFBSs using ChIP-Seq and DNAse-hypersensitivity assays. We find that unannotated conserved islands are four times more likely to coincide with RFBSs than with unannotated ncRNAs. Thousands of conserved RFBSs can be categorized as insulators based on the presence of CTCF or as enhancers based on the presence of p300/CBP and H3K4me1. While many unannotated conserved RFBSs are transcriptionally active to some extent, the transcripts produced tend to be unspliced, non-polyadenylated and expressed at levels 10 to 100-fold lower than annotated coding or ncRNAs. Extending these findings across multiple cell types and tissues, we propose that most conserved non-coding genomic DNA in vertebrate genomes corresponds to promoter-distal regulatory elements

Obstacles to detecting isoforms using full-length scRNA-seq data

Abstract: Background: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq

Obstacles to detecting isoforms using full-length scRNA-seq data

Abstract: Background: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq

Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes.

Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.We acknowledge the Wellcome Sanger Institute’s DNA Pipelines for construction of the 10x sequencing libraries. We thank Allan Muhwezi and Andrew Russell for assistance with parasite culture and 10x Single-cell 3’ RNA-seq respectively. In addition, we would like to thank Matthew Young for useful conversations about ambient RNA, Mirjana Efremova for providing information about the maternal/fetal data, and Katie Gray for assistance in interpreting the previously unannotated cluster. The Wellcome Sanger Institute is funded by the Wellcome Trust (grant 206194/Z/17/Z), which supports MKNL and MH. This work was supported by an MRC Career Development Award (G1100339) to MKNL. We would like to acknowledge the Wellcome Trust Sanger Institute as the source of the human induced pluripotent cell lines that were generated under the Human Induced Pluripotent Stem Cell Initiative funded by a grant from the Wellcome Trust and Medical Research Council, supported by the Wellcome Trust (WT098051) and the NIHR/Wellcome Trust Clinical Research Facility, and acknowledges Life Science Technologies Corporation as the provider of Cytotune (HipSci.org). The Cardiovascular Epidemiology Unit is supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospital’s NHS Foundation Trust]. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care

Single paternal dexamethasone challenge programs offspring metabolism and reveals multiple candidates in RNA-mediated inheritance.

Single traumatic events that elicit an exaggerated stress response can lead to the development of neuropsychiatric conditions. Rodent studies suggested germline RNA as a mediator of effects of chronic environmental exposures to the progeny. The effects of an acute paternal stress exposure on the germline and their potential consequences on offspring remain to be seen. We find that acute administration of an agonist for the stress-sensitive Glucocorticoid receptor, using the common corticosteroid dexamethasone, affects the RNA payload of mature sperm as soon as 3 hr after exposure. It further impacts early embryonic transcriptional trajectories, as determined by single-embryo sequencing, and metabolism in the offspring. We show persistent regulation of tRNA fragments in sperm and descendant 2-cell embryos, suggesting transmission from sperm to embryo. Lastly, we unravel environmentally induced alterations in sperm circRNAs and their targets in the early embryo, highlighting this class as an additional candidate in RNA-mediated inheritance of disease risk.KG was funded by the Swiss National Science Foundation early postdoc and advanced postdoc mobility a SPARK and Novartis foundation grant. Some of this work was supported by Cancer Research UK (C13474/A18583, C6946/A14492) and Wellcome (104640/Z/14/Z, 092096/Z/10/Z) to EAM. GP and MH were supported by a core grant from the Wellcome Trust. The lab of JB is currently funded by the ETH Zurich, SNSF Project Grant 310030_172889/1, ETH Research Grant ETH-20 19-1, the Kurt und Senta Herrmann-Stiftung, the Botnar Research Center for Child Health and a 3R Competence Center Project Grant. JK was supported by a Swiss-european mobility programme scholarship

MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development.

BACKGROUND: Microexons, exons that are ≤ 30 nucleotides, are a highly conserved and dynamically regulated set of cassette exons. They have key roles in nervous system development and function, as evidenced by recent results demonstrating the impact of microexons on behaviour and cognition. However, microexons are often overlooked due to the difficulty of detecting them using standard RNA-seq aligners. RESULTS: Here, we present MicroExonator, a novel pipeline for reproducible de novo discovery and quantification of microexons. We process 289 RNA-seq datasets from eighteen mouse tissues corresponding to nine embryonic and postnatal stages, providing the most comprehensive survey of microexons available for mice. We detect 2984 microexons, 332 of which are differentially spliced throughout mouse embryonic brain development, including 29 that are not present in mouse transcript annotation databases. Unsupervised clustering of microexons based on their inclusion patterns segregates brain tissues by developmental time, and further analysis suggests a key function for microexons in axon growth and synapse formation. Finally, we analyse single-cell RNA-seq data from the mouse visual cortex, and for the first time, we report differential inclusion between neuronal subpopulations, suggesting that some microexons could be cell type-specific. CONCLUSIONS: MicroExonator facilitates the investigation of microexons in transcriptome studies, particularly when analysing large volumes of data. As a proof of principle, we use MicroExonator to analyse a large collection of both mouse bulk and single-cell RNA-seq datasets. The analyses enabled the discovery of previously uncharacterized microexons, and our study provides a comprehensive microexon inclusion catalogue during mouse development