Stochastic gene expression in a single cell pdf

Control of stochasticity in eukaryotic gene expression. More recently, increasing evidence has supported the notion that the accuracy of gene expression is indeed flexibly variable. Klemm,5 alexander van oudenaarden,2,4 6 and rudolf jaenisch1,2 1the whitehead institute for biomedical research, cambridge, ma. Single cell rna sequencing technologies and bioinformatics pipelines,emm 07 aug 2018 doi. The process linking genes to proteins is called gene expression. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure.

When 96 of the ms2gfp proteins bind to an individual mrna, enough. Stochastic expression dynamics of a transcription factor. Stochastic gene expression in arabidopsis thaliana nature. Stochastic gene expression in a single cell michael b. Jan 10, 2014 although genetic traits are often dominant or recessive, the impact of the same heterozygous genotype can vary quite a bit between individuals. In this thesis, a combination of experiments and computational modelling was performed in an attempt to further our understanding of purefrex gene expression. In the case of gene expression, this means follow ing transcription, translation, and the pro duction of proteins at the singlemolecule level. The details of the biology background of processes of gene expression are given in chapter 2. Clonal populations of cells exhibit substantial phenotypic variation. Gene expression profiling of a single cell using laser capture microdissection and openarray technology.

Quantifying gene expression variability arising from. Jeff gore centers on discussion of one of his favorite scientific papers. The stochastic nature of gene expression revealed at the. Hence a markov process is needed to describe the stochastic. We identified,606 single nucleotide polymorphisms snps on 5161 genes between 9311 and npb, among which 2046 genes contain one snp and 3115. In this paper, we consider two stochastic models of gene expression in prokaryotic cells. Stochastic gene expression in fluctuating environments. Dec 10, 20 the human thp1 myeloid monocytic leukemia cell line is an ideal model to study the temporal dynamics of singlecells because. Analytic theory of stochastic oscillations in singlecell. In the first model, sixteen biochemical reactions involved in transcription, translation and transcriptional regulation in the presence of inducer molecules are considered. May 09, 2006 fluctuations in protein numbers noise due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks.

In many cases, a core circuit that generates this rhythm has been elucidated and been shown to oscillate in single cells. A quantitative single molecule analysis of transcription factor expression dynamics based on. In this article, we extend the classical twostate model of stochastic mrna dynamics to include a considerable number of salient features of single cell biology, such as cell. Exact stochastic simulation of coupled chemical reactions. In the first model, sixteen biochemical reactions involved in transcription, translation and transcriptional regulation in the. In single cells, gene expression is made up of fundamentally stochastic processes raj and van oudenaarden, 2008 due to intrinsic and extrinsic variation. Apr 26, 2018 the circadian clock controls gene expression throughout the day and night in most organisms, from single cell photosynthetic bacteria to mammals bellpedersen et al. Inferring single cell gene expression mechanisms using stochastic simulation bernie j.

Living cells possess very low copy numbers of many components, including dna and important regulatory molecules 1. Stochastic gene expression in fluctuating environments genetics. Inferring singlecell gene expression mechanisms using. Stochastic gene expression in a single cell request pdf. Results linkage effect on gene expression cofluctuation is general and longrange let us consider two genes a and b each with two alleles respectively named 1 and 2 in a diploid cell.

Such heterogeneity can be essential for many biological processes. Although researchers have been interested in the stochastic. Singlemolecule approaches to stochastic gene expression. Naively, we could place green fluorescent protein gfp on a bacterial chromosome downstream of a promoter that is activated by the system of interest. Singlecell expression analyses during cellular reprogramming. Predicting stochastic gene expression dynamics in single cells. Sultan doganay, maurice youzong lee, alina baum, jessie peh, sunyoung hwang, jooyeon yoo, paul j. Article an abundant class of noncoding dna can prevent stochastic gene silencing in the c. Request pdf stochastic gene expression in a single cell clonal populations of cells exhibit substantial phenotypic variation.

Cells with the same amount of each protein appear yellow, whereas cells expressing more of one fluorescent protein than the other appear red or green. Stochastic gene expression has been studied mainly in prokaryotic organisms and lower metazoans, where it provides the means for genetically identical populations to. The standard kinetic model for gene expression assumes that a gene can fluctuate randomly between on and off promoter states, where mrna can be transcribed only in the on state 16, 25 figure 1a. If you have not installed and configured the adobe acrobat reader on your system, please. Singlecell transcriptome analysis reveals widespread.

An exhaustive simulation over the parameter space of the model shows that there are three major patterns of gene expression, type a, type b and type c. Here we performed single cell rnaseq analysis of single blastomeres of mouse embryos, which revealed significant changes in the transcriptome. As many quantities of cell biological interest are present in discrete copy number within the cell single dnas, dozens of mrnas, hundreds of proteins, tools from discrete stochastic mathematics are often used to analyse and model cellular noise. As in most previous studies, we chose mefs as the donor cell type, and it is possible that other donor cell types may reveal different expression profiles. The effect of varying the cellular parameters on the patterns, in particular, the transition from one type of pattern to another, is studied. By applying our model to a single cell rnaseq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with rna polymerase ii binding and chromatin modifications. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. Probing gene expression in live cells, one protein molecule at a time, by yu et al. Stochastic simulation of gene expression in a single cell. Stochastic promoter activation affects nanog expression. Both stochasticity inherent in the biochemical process of gene expression intrinsic noise and fluctuations in other cellular components extrinsic. Gene expression is a fundamental process in all living cells and controls the accurate flow of genetic information from dna to rna to protein.

Transcriptomes of individual mesophyll cells isolated from hybrids offer us a unique opportunity to investigate the prevalence of monoallelic gene expression because the allelic gene expression can be distinguished fig. Localization of ash1 mrna particles in living yeast. Perhaps the most apparent manifestation of stochastic mechanisms in gene expression is the heterogeneity of cell populations. A singlecell expression simulator guided by gene regulatory. Petzold3 and abhyudai singh2, 1institute for collaborative biotechnologies, university of california, santa barbara, ca 93106, 2department of. In a genetic regulatory system, it is possible for the protein produced from a gene to inuence the rate of expression of that same gene. Stochastic phenotype transition of a single cell in. It is often implicitly assumed that transitions between promoter states are memoryless, i. By measuring the variation in uorescence across a population of cells, we could quantify stochasticity.

This gene is then expressed in a cell that already expresses the ms2 coat protein fused to gfp. Stochastic gene expression in a single cell nasaads. Jul 28, 2019 a common approach to benchmarking of single cell transcriptomics tools is to generate synthetic data sets that resemble experimental data in their statistical properties. In summary, single cell gene expression analysis revealed an unanticipated heterogeneity in gene expression between sister cells, consistent with stochastic epigenetic alterations during the. If a single ratelimiting step determines the rates of transcription and transitions between the two promoter. Singlecell rnaseq reveals dynamic, random monoallelic gene.

Such effects can play crucial roles in biological processes, such. Introduction to stochastic gene expression lecture. However, existing single cell simulators do not incorporate known principles of transcription factor gene regulatory interactions that underlie expression dynamics. Stochastic and deterministic allele specific gene expression ase might influence single cell phenotype, but the extent and nature of the phenomenon at the onset of early mouse development is unknown. Accurate detection and quantification of rare alleles for singlecell analysis on the quantstudio 12k flex realtime pcr system pdf. Such effects can play crucial roles in biological processes, such as development, by establishing initial asymmetries that, amplified by feedback mechanisms, determine cell fates 4. Modeling the stochastic dynamics of gene expression in single cells. Gene expression was visualized in single living mammalian cells with. Recent experiments tracking growth and division of individual cells reveal that cell division times.

The overall pattern is composed of a small number of pig ments. Even though random translational bursts yield large gene expression noise, it can enhance the efficiency and the regime of stochastic oscillations. May 01, 2004 perhaps the most apparent manifestation of stochastic mechanisms in gene expression is the heterogeneity of cell populations. Modelling stochastic gene expression mcgill university. Predicting stochastic gene expression dynamics in single cells jerome t. Stochastic promoter switching between transcriptionally active on and inactive off states is a major source of noise in gene expression. Pdf stochastic simulation of gene expression in a single. The random nature of gene expression is well established experimentally. To ensure the accuracy of gene expression, extensive substrate selection and proofreading mechanisms are utilized at each step during dna replication, transcription, and translation 1 3. Stochastic gene expression in a single cell michael elowitz. Stochastic gene expression in live cells, one protein molecule at a time. Unambiguously measuring stochastic gene expression, however, can be challenging 5. Stochastic gene expression and its consequences ncbi nih.

Top 100 most variable genes expressed in 1% cells are in red and. How intrinsic stochasticity of gene expression is controlled is not well understood. Inferring the kinetics of stochastic gene expression from. Mar 16, 2006 in a living cell, gene expressionthe transcription of dna to messenger rna followed by translation to proteinoccurs stochastically, as a consequence of the low copy number of dna and mrna. Here we present sergio, a simulator of single cell gene. Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell to cell variations in mrna and protein levels. The stochastic nature of a genetic network in single cells cannot be neglected when modeling cellular control circuits. Thus, stochastic effects in gene expression may account for the large amounts of cell cell variation observed in isogenic populations 2, 3. Living cells possess very low copy numbers of many components, including dna and important regulatory molecules. Deterministic and stochastic modelling of gene expression. Single cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase yosef buganim,1,7 dina a. Stochasticity in gene expression refers to the random mechanisms that govern transcription and translation resulting in variable levels of mrna and proteins across cells of the same population.

These products are often proteins, but in nonprotein coding genes such as transfer rna trna or small nuclear rna snrna genes, the product is a functional rna. Singlecell analysis of early antiviral gene expression. When a and b are chromosomally linked, without loss of generality, we assume that a 1 and b 1 are on the same chromosome whereas a 2 and b 2 are on its homologous chromosome fig 2a. Such heterogeneity can be essential for many biological processes and is conjectured to. Although single cell rnaseq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts. Cheng,1,3 elena itskovich,1 styliani markoulaki,1 kibibi ganz,1 sandy l. Fluctuations of gene expression over time have been successfully measured in singlecell systems including. Swain 2 clonal populations of cells exhibit substantial phenotypic variation. Sunney xie and coworkers have followed single protein expression events in vivo in an. Using a solvable model, we illustrate the oscillation as a stochastic circulation along a hysteresis loop. We constructed strains of escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Hastymonitoring dynamics of single cell gene expression over multiple cell cycles mol syst biol, 1 2005 2005 0024. Predicting stochastic gene expression dynamics in single. In the oscillatory regime, the essence of stochastic oscillations is revealed to be a circular motion along a stochastic hysteresis loop.

In particular, master equation treatments where the probabilities, of observing a system in a state at time are linked. Gene expression profiling in single cells from the pancreatic islets of langerhans reveals lognormal distribution of mrna levels. Inferring the kinetics of stochastic gene expression from single cell rnasequencing data jong kyoung kim and john c marioni abstract background. Genetically identical populations of cells grown in the same environmental condition show substantial variability in gene expression profiles. Stochastic gene expression in a single cell by larry. Single molecule approaches to stochastic gene expression arjun raj and alexander van oudenaarden department of physics, massachusetts institute of technology, cambridge, massachusetts 029. Gene expression has been considered a highly accurate process, and deviation from such fidelity has been shown previously to be detrimental for the cell. Between individual cells, heterogeneity occurs at the genetic and molecular level, due to stochastic gene expressions, age of cells, asymmetric partitioning during cell division, and inhomogeneous. Mathematical modeling provides a means of understanding the factors leading to the observed stochasticity. Pdf stochastic simulation of gene expression in a single cell. Here, intrinsic variability refers to the variation observed between different realizations of identical biological systems within identical environments due to the probabilistic nature of the. Single cell stochastic gene expression, with gene state switching, transcription, translation, and negative feedback, can exhibit oscillatory kinetics that is statistically characterized in terms of a nonmonotonic power spectrum. By indrani bose, rajesh karmakar and siddhartha roy. Much work has focused on understanding the different sources of noise in the gene expression process that drive this stochastic variability in gene expression.

Stochastic gene expression in a single cell science. Wayne davis,1 yongbin li,8 di zhao,8 karine rebora,6 jonathan r. Gene expression inferring single cell gene expression mechanisms using stochastic simulation bernie j. The time evolution of the number of biomolecules of a particular type is determined using the stochastic. Recent advances in measuring gene expression at the singlecell level have highlighted the stochastic nature of messenger rna and protein synthesis. We develop a statistical framework for studying the kinetics of stochastic gene expression from single cell rnaseq data. Stochastic gene expression can have important consequences for cellular. Intrinsic and extrinsic contributions to stochasticity in gene expression p natl acad sci usa. Analytical distributions for detailed models of stochastic. Pdf modeling the stochastic dynamics of gene expression. The level of a given mrna or protein exhibits significant variations from cell to cell across a homogeneous population of living cells. A detailed deterministic model for the expression of a single gene was rst developed and validated. Stochastic models of gene expression by lanjia lin bachelor of engineering wuhan university, 2003.

Aug 16, 2002 stochastic gene expression in a single cell. Multiple phenotypic states often arise in a single cell with different gene expression states that undergo transcription regulation with positive feedback. Sep 14, 2012 in summary, single cell gene expression analysis revealed an unanticipated heterogeneity in gene expression between sister cells, consistent with stochastic epigenetic alterations during the early phase of the reprogramming process. Stochastic protein expression in individual cells at the. Petzold 3, and abhyudai singh2 1institute for collaborative biotechnologies, university of california, santa barbara, ca 93106.

Figure 1 stochastic gene expression in a single cell. Scatterplot of the average gene expression in single t reg cells and variability of expression fano factor variancemean. A in the absence of intrinsic noise, the two fluorescent proteins fluctuate in a correlated fashion over time in a single cell left. Such effects can play crucial roles in biological processes, such as development, by establishing initial. Although single cell rnaseq provides an opportunity to. In the simplest case, the concentration of a constitutively expressed protein could show some variability from cell to cell e lowitz et al. Recently, stochastic models have been used to predict distributions of steadystate protein levels within a population but not to predict the dynamic, presteadystate distributions. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. Temporal dynamics and transcriptional control using single. Singlecell gene expression reveals a landscape of regulatory. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the. A triphasic bifurcation upon the increasing strength of.