The effect of background noise and its removal on the analysis of single-cell expression data
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PROVIDER: PRJNA906016 | ENA |
REPOSITORIES: ENA
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Project description:BACKGROUND: In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swappingevents. Here, we characterize this background noise exemplified by three single-cell RNA-seq(scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise. RESULTS: We find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of the background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination profiles and hence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. CONCLUSION: Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.
2022-12-10 | GSE218853 | GEO
Project description:BackgroundIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events.ResultsHere, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3-35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure.ConclusionsOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.
| S-EPMC10278251 | biostudies-literature
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:Environmental stressors present in the modern world can have a fundamental effect on the physiology and health of humans. Exposure to stressors like air pollution, heat and traffic noise has been linked to a pronounced increase in non-communicable diseases. Specifically, aircraft noise has been identified as a risk factor for cardiovascular and metabolic diseases, such as arteriosclerosis, heart failure, stroke and diabetes. Noise stress leads to neuronal activation with subsequent stress hormone release that ultimately leads to activation of the renin-angiotensin-aldosterone system, increasing inflammation and oxidative stress, with dramatic effects on the cardiovascular system. However, despite the epidemiological evidence of a link between noise stress and metabolic dysfunction, the consequences of exposure at the molecular, metabolic level of the cardiovascular system are largely unknown. Here we use a murine model system of aircraft noise exposure to show that noise stress profoundly alters heart metabolism. Within days of exposing animals to aircraft noise the heart has a reduced potential for utilising fatty-acid beta-oxidation, the tricarboxylic acid cycle, and the electron transport chain for generating ATP. This is compensated by shifting energy production towards glycolysis. Intriguingly, the metabolic shift is reminiscent of what is observed in failing and ischaemic hearts. Our results demonstrate that within a relatively short exposure time, the cardiovascular system undergoes a fundamental metabolic shift that bears the hallmarks of cardiovascular disease. Overall, aircraft noise induces rapid, detrimental metabolic shifts in the heart, resembling patterns seen in cardiovascular diseases. These findings underscore the urgent need to comprehend molecular consequences of environmental stressors, paving the way for targeted interventions aiming mitigating health risks associated with chronic noise exposure in our modern, noisy environments.
2025-05-14 | PXD051406 | Pride
Project description:Seeds in the experimental group were exposed to 48 h of traffic noise recorded in a highly congested area of the city, while the control group was kept under background white noise. While traffic noise exposure made no effects on seed germination, noise treatment significantly increased the growth of seedlings by 39%. By employing RNA-sequencing profiling, we further investigated the noise-induced molecular changes in the A. thaliana seedlings. The aberrant expression of 690 genes was detected in the noise-exposed seedlings.
2023-10-15 | GSE240794 | GEO
Project description:Effect of genomic and cellular environments on gene expression noise
| PRJNA925876 | ENA
Project description:Effect of genomic and cellular environments on gene expression noise
| PRJNA1108301 | ENA
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 perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign 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.
2024-05-12 | GSE266730 | GEO
Project description:Noise-induced hidden hearing loss (HHL) is a new type of hearing loss that has been identified in recent years and leads to insidious damage to the cochlea, unlike the well-known noise-induced hearing loss (NIHL). However, the cellular and molecular basis for it remains to be elucidated. Here, we established a single-cell transcriptome profile of the C57BL/6J mouse cochlea, in which we describe the transcriptome changes of individual cell types within the cochlea with HHL and NIHL. Mice in the HHL group were exposed to 110 dB of noise for 2 hours, and those in the NIHL group were exposed to 115 dB of noise for 4 hours for 3 days. The cochlea was taken 6 hours after the last noise exposure. The control group was not exposed to noise, with other conditions being the same as those in the noise-exposed group. The results of sequencing at the single-cell level help us gain a deeper understanding of the mechanisms of the development of HHL and NIHL.
2024-06-24 | GSE252285 | 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