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Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets

ABSTRACT: Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets

PROVIDER: PRJNA865472 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR20751909.fastq.gz	Fastqsanger.gz
	SRR20751910.fastq.gz	Fastqsanger.gz
	SRR20751911.fastq.gz	Fastqsanger.gz
	SRR20751912.fastq.gz	Fastqsanger.gz

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Similar Datasets

Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets

Project description:Ambient RNA contamination in single-cell RNA sequencing (RNA-seq) is a significant problem, but its consequences are poorly understood. Here, we show that ambient RNAs in brain single-nuclei RNA-seq can have a nuclear or extra-nuclear origin, and each origin results in a distinct gene set signature. Both ambient RNA signatures are predominantly neuronal and we find that some previously annotated neuronal cell types are distinguished by ambient RNA. Strikingly, we also detect pervasive neuronal ambient RNA contamination in all glial cell types unless glia and neurons are physically separated prior to sequencing. We demonstrate that this contamination can be removed in silico using existing tools. We also show that previous annotations of immature oligodendrocytes are likely glial cells contaminated with ambient RNAs. After ambient RNA removal, we can detect extremely rare committed oligodendrocyte progenitor cells, which were infrequently annotated in previously published adult human brain datasets. Together, these results provide an in-depth analysis of ambient RNA contamination in brain single-cell datasets.

2022-10-19 | GSE198951 | GEO

Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets.

Project description:Ambient RNA contamination in single-cell and single-nuclei RNA sequencing (snRNA-seq) is a significant problem, but its consequences are poorly understood. Here, we show that ambient RNAs in brain snRNA-seq datasets have a nuclear or non-nuclear origin with distinct gene set signatures. Both ambient RNA signatures are predominantly neuronal, and we find that some previously annotated neuronal cell types are distinguished by ambient RNA contamination. We detect pervasive neuronal ambient RNA contamination in all glial cell types unless glia and neurons are physically separated prior to sequencing. We demonstrate that this contamination can be removed in silico and show that previous single-nuclei RNA-seq-based annotations of immature oligodendrocytes are glial nuclei contaminated with ambient RNAs. After ambient RNA removal, we detect rare, committed oligodendrocyte progenitor cells not annotated in most previous adult human brain datasets. Together, these results provide an in-depth analysis of ambient RNA contamination in brain single-nuclei datasets.

| S-EPMC9789184 | biostudies-literature

Ambient RNA analysis of single-nuclei RNA-Seq data from the frontal cortex of C57BL/6J mice

Project description:Contamination of ambient RNA was found in single-nuclei RNA-Seq data of mouse frontal cortex. CellBender plus subcluster cleaning selectively removed ambient RNA signature from glial cell types.

2022-10-19 | GSE198640 | GEO

Ambient RNA analysis of cortex-specific single-nuclei RNA-Seq (snRNA-seq) data in the sham and bilateral carotid artery stenosis (BCAS) groups

Project description:When compared with the sham group, ambient RNAs were more predominant in the BCAS group, which originated from damaged neuronal nuclei. After CellBender and subcluster cleaning treatments, detectable ambient RNA contamination could be largely reduced, and cortex-specific transcriptomic maps in the sham and BCAS groups were generated successfully. The underlying cellular and molecular mechanisms related to cortex damage and glial activation after severe cerebral hypoperfusion were further investigated at single nuclei resolution.

2024-05-17 | GSE229259 | GEO

Isolated nuclei from frozen tissue are the superior source for single cell RNA-seq compared with whole cells

Project description:The isolation of intact single cells from frozen tissue is a challenge due to the mechanical and physical stress inflicted upon the cell during the freeze-thaw process. Ruptured cells release ambient RNA into the cell suspension, which can become encapsulated into droplets during droplet based single cell RNA-seq library preparation methods. The presence of ambient RNA in droplets has been suggested to impact data quality, however there have been limited reports on single cell RNA-seq data from frozen tissue. Here, we compare the results of single cell RNA-seq derived from disaggregated cells from frozen brain tissue with single nuclei RNA-seq derived from purified nuclei of identical tissue using the 10X Genomics Chromium 3’ gene expression assay. Our results indicated that presence of ambient RNA in the cell suspension resulted in single cell RNA-seq data with a 25-fold lower gene count, a 5-fold lower UMI count per cell and a 2-fold lower fraction of reads per cell compared with single nuclei RNA-seq data. Cell clustering with the single cell RNA-seq data was unable to resolve the heterogeneity of brain cell types. Our conclusion is that nuclei from frozen tissue are the superior substrate for single cell transcriptome analysis.

2023-02-28 | GSE225915 | GEO

Single-nuclei isoform RNA sequencing reveals combination patterns of transcript elements across human brain cell types

Project description:Single-nuclei RNA-Seq is widely employed to investigate cell types, especially of human brain and frozen samples. In contrast to single-cell approaches, many single-nuclei reads are purely intronic. Here, using microfluidics, PCR-based artifact removal, target enrichment, and long-read sequencing, we developed single-nuclei isoform RNA-sequencing (‘SnISOr-Seq’), and applied it to human adult frontal cortex. SnISOr-Seq dramatically increased the fraction of informative reads. We found that exons associated with autism exhibit coordinated and highly cell-type specific inclusion. We discovered two distinct combination patterns: first, those distinguishing neural cell types, enriched in TSS-exon, exon-polyA-site, and non-adjacent exon pairs. Second, those with multiple configurations within one cell type, enriched in adjacent exon pairs. Furthermore, adjacent exons are predominantly mutually-associated, while distant exons are frequently mutually-exclusive. Finally, we observed that human-specific exons are almost as tightly coordinated as conserved exons. SnISOr-Seq enables single-nuclei long-read isoform analysis in human brain, and in any frozen or hard-to-dissociate sample.

2022-01-16 | GSE178175 | GEO

In vivo tissue-specific chromatin profiling in Drosophila melanogaster using GFP-tagged nuclei

Project description:We report the application of different chromatin profiling techniques applied to cell-type specific nuclei obtained with a nuclei immuno-enrichment protocol described in this manuscript, referred as NEI protocol. By optimizing these methods, we generated genome-wide datasets associated with the nuclear RNA transcriptome, chromatin accessibility, and distribution of histone H3 and two H3 two lysine tri-methylation marks, H3K4me3 and H3K36me3 respectively. This study provides a framework for the application of different aspects of chromatin biology using nuclei derived from specific cell types in Drosophila. Further, it demonstrates the low input requirements necessary for these chromatin studies.

2021-05-23 | GSE169328 | GEO

Ambient RNA analysis of single-nuclei RNA-Seq data from the frontal cortex of C57BL/6J mice

Project description:Ambient RNA analysis of single-nuclei RNA-Seq data from the frontal cortex of C57BL/6J mice

| PRJNA816205 | ENA

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [CRISPR]

Project description:Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

2024-10-08 | GSE272454 | GEO

Single-nuclei RNA-seq of the nucleus tractus solitarii and area postrema at for baseline and LPS-activated cell types

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