Effect of genomic and cellular environments on gene expression noise
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE266730 | GEO | 2024/05/12
REPOSITORIES: GEO
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Project description:Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we performed the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We found that the mean and noise of expression correlate with different histone modifications. We quantified the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assigned the extrinsic component to differences between the CD24+ “stem-like” sub-state and the more “differentiated” sub-state. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci. Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome, and that the data generated by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.
2023-01-23 | GSE223369 | GEO
Project description:Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we performed the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We found that the mean and noise of expression correlate with different histone modifications. We quantified the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assigned the extrinsic component to differences between the CD24+ “stem-like” sub-state and the more “differentiated” sub-state. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci. Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome, and that the data generated by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.
2023-01-23 | GSE223367 | GEO
Project description:Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This “noise” in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we performed the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We found that the mean and noise of expression correlate with different histone modifications. We quantified the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assigned the extrinsic component to differences between the CD24+ “stem-like” sub-state and the more “differentiated” sub-state. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for “safe-harbor” loci. Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome, and that the data generated by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.
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Project description:Cellular processes are subject to variability, or noise, yet mechanisms that promote cell-to-cell uniformity are poorly understood. We have identified such a role for Dig1, a redundant (with Dig2) MAPK-responsive inhibitor of the S. cerevisiae mating pathway transcription factor Ste12. Cells lacking Dig1, but not Dig2, exhibited increased variability in outputs of the mating pathway. dig1∆ mutants also displayed a Ste12-dependent defect in growth in the absence of mating pheromone and a quantitative defect in the process of mating, itself. In cells expressing two reporter genes driven by the same promoter, both intrinsic/uncorrelated and extrinsic/correlated noise were found to increase. Remarkably, the extrinsic noise phenotype in cells lacking Dig1 correlates with the aggregation of target genes: we observed subnuclear foci of Ste12 specifically in dig1∆ cells and, using a newly-developed method to immunoprecipitate a single locus from crosslinked chromatin, we found that Dig1 inhibits long-range interactions between Ste12 target genes in vivo. Dig1 may shield binding surfaces on Ste12 whose unmasking leads to inappropriate gene associations and increased gene expression noise. These studies reveal how investigations of variability modulation mechanisms can yield unexpected biological insights. The FUS1 locus is tagged with an array of lac operators. Wild type and dig1∆ cells containing this tagged FUS1 locus and expressing a mCherry-LacI were grown up for ChIP-chip. The FUS1 locus was selectively immunoprecipitated using an anti-DsRed antibody.
2010-12-15 | E-GEOD-17583 | biostudies-arrayexpress
Project description:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not known. Here, we study how different noise levels are encoded by the promoter sequence using massively parallel noise measurements of thousands of synthetically designed promoters. We find that the noise levels of promoters with similar mean expression levels can vary over more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We devised a computational model that can accurately predict the mean-independent component of the noise from DNA sequence alone. Our model suggests that the effect of promoters on noise is partly mediated by the combination of non-specific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Overall, our results demonstrate that small changes in the DNA sequence of promoters can allow tuning of noise levels in a manner that is largely predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels. Expression measurements of a collection of synthetic promoters collection that was published in Sharon et al. Nature Biotechnology 2012(doi: 10.1038/nbt.2205). Two replicates of the promoter library integrated into a plasmid in yeast were measured in SC-Glu-URA medium. The promoter library was measured as described in Sharon et. al.(Sharon et al. 2012), except for the differences below. Briefly, a large collection of synthetic promoter reporter gene strains was generated by a pooled ligation of 6500 fully designed DNA oligos (obtained by synthesis on a microarray(LeProust et al. 2010) by Agilent Technologies, Santa Clara, California). The oligos were ligated upstream to a yellow fluorescent protein (YFP) gene with a short (100 bp) core promoter sequence taken from HIS3 gene promoter and into a low copy plasmid which also contains a TEF2 promoter deriving red fluorescent protein (mCherry). The resulting plasmids were then transformation into yeast (S. cerevisiae). Next, the pool of cells was grown in amino acid starvation condition (SCD without amino acid except Histidine), and sorted according to their YFP expression level into 32 expression bins (mCherry was used for gating one plasmid copy cells and for normalization). The DNA of the promoters in each bin were then amplified and sent to multiplexed parallel sequencing. Each sequencing result was mapped to a specific promoter and expression bin, resulting in a distribution of cells that contain each promoter across all expression bins. The following differences were applied relative to the description in Sharon et. al.(Sharon et al. 2012). The medium used both for growing the cells and for their sorting was SC-Glu-URA (synthetic complete media with 2% glucose and without uracil) medium without amino acids, except for Histidine. In order to achieve expression distributions with high resolution that would allow good assessment of expression noise, the library cells were sorted into 32 bins according to their ratio of YFP and mCherry expression level, thereby normalizing for extrinsic noise effects. Each of the two extreme expression bins contained 2% of the library cells and each of the remaining 30 bins contained 3.2%. We collected a total of 10,000,000 cells. As previously described, the mapping of cells to bins involves parallel sequencing of the amplified promoter regions. For this purpose, Illumina Hi-Seq 2000 was used to obtain >30,000,000 mapped reads. The two replicates were separately generated from the ssDNA oligo library and separately measured as described above.
2014-07-12 | E-GEOD-55346 | biostudies-arrayexpress
Project description:Cellular processes are subject to variability, or noise, yet mechanisms that promote cell-to-cell uniformity are poorly understood. We have identified such a role for Dig1, a redundant (with Dig2) MAPK-responsive inhibitor of the S. cerevisiae mating pathway transcription factor Ste12. Cells lacking Dig1, but not Dig2, exhibited increased variability in outputs of the mating pathway. dig1∆ mutants also displayed a Ste12-dependent defect in growth in the absence of mating pheromone and a quantitative defect in the process of mating, itself. In cells expressing two reporter genes driven by the same promoter, both intrinsic/uncorrelated and extrinsic/correlated noise were found to increase. Remarkably, the extrinsic noise phenotype in cells lacking Dig1 correlates with the aggregation of target genes: we observed subnuclear foci of Ste12 specifically in dig1∆ cells and, using a newly-developed method to immunoprecipitate a single locus from crosslinked chromatin, we found that Dig1 inhibits long-range interactions between Ste12 target genes in vivo. Dig1 may shield binding surfaces on Ste12 whose unmasking leads to inappropriate gene associations and increased gene expression noise. These studies reveal how investigations of variability modulation mechanisms can yield unexpected biological insights.
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Project description:Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Resulting from a previous screen, the protein SON was identified as a potential noise regulator. We perform Son KD and utilize single-cell RNA sequencing (scRNA-seq) to quantify the changes in mean/noise of all transcripts. This dataset corresponds to the aforementioned scRNA-seq experiment upon Son KD.
2024-08-05 | GSE270846 | GEO
Project description:Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Resulting from a previous screen, a possible role of nuclear speckles in noise regulation was highlighted. We utilize tubercidin treatment and single-cell RNA sequencing (scRNA-seq) to quantify the changes in mean/noise of all transcripts upon nuclear speckle disruption. This dataset corresponds to the aforementioned scRNA-seq experiment upon tubercidin treatment.
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Project description:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not known. Here, we study how different noise levels are encoded by the promoter sequence using massively parallel noise measurements of thousands of synthetically designed promoters. We find that the noise levels of promoters with similar mean expression levels can vary over more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We devised a computational model that can accurately predict the mean-independent component of the noise from DNA sequence alone. Our model suggests that the effect of promoters on noise is partly mediated by the combination of non-specific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Overall, our results demonstrate that small changes in the DNA sequence of promoters can allow tuning of noise levels in a manner that is largely predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels.
2014-07-12 | GSE55346 | GEO
Project description:Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Our integrative approach, combining single-cell RNA sequencing with proteomics and regulator enrichment analysis, reveals 32 putative noise regulators. The approach utilizes global translation inhibition (i.e., potential protein regulators), and single-cell RNA sequencing (scRNA-seq) to quantify the changes in noise of all transcripts (i.e., potential mRNA targets). This dataset corresponds to a control bulk RNAseq experiment experiment upon translation inhibition with cycloheximide to validate the changes in mean observed with single-cell.
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