Single-Cell RNA-seq Analysis Reveals a Positive Correlation between Ferroptosis and Beta-Cell Dedifferentiation in Type 2 Diabetes
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Insulin deficiency in patients with type 2 diabetes mellitus (T2D) is associated with beta-cell dysfunction, a condition increasingly recognized to involve processes such as dedifferentiation and apoptosis. Moreover, emerging research points to a potential role for ferroptosis in the pathogenesis of T2D. In this study, we aimed to investigate the potential involvement of ferroptosis in the dedifferentiation of beta cells in T2D. We performed single-cell RNA sequencing analysis of six public datasets. Differential expression and gene set enrichment analyses were carried out to investigate the role of ferroptosis. Gene set variation and pseudo-time trajectory analyses were subsequently used to verify ferroptosis-related beta clusters. After cells were categorized according to their ferroptosis and dedifferentiation scores, we constructed transcriptional and competitive endogenous RNA networks, and validated the hub genes via machine learning and immunohistochemistry. We found that ferroptosis was enriched in T2D beta cells and that there was a positive correlation between ferroptosis and the process of dedifferentiation. Upon further analysis, we identified two beta clusters that presented pronounced features associated with ferroptosis and dedifferentiation. Several key transcription factors and 2 long noncoding RNAs (Keywords:
RNA-Seq
BETA (programming language)
Cell type
Abstract Given most tissues are consist of abundant and diverse sub cell-types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which sub cell-type(s) the differential expression occur. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell , a computational method aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders and LRcell is more sensitive than even the leading deconvolution methods.
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BETA (programming language)
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Abstract Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.
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BackgroundNowadays, both customers and producers prefer thin-tailed fat sheep.To effectively breed for this phenotype, it is important to identify candidate genes and uncover the genetic mechanism related to tail fat deposition in sheep.Accumulating evidence suggesting that post-transcriptional modification events of precursor-messenger RNA (pre-mRNA), including alternative splicing (AS) and alternative polyadenylation (APA), may regulate tail fat deposition in sheep.Differentially expressed transcripts (DETs) analysis is a way to identify candidate genes related to tail fat deposition.However, due to the technological limitation, post-transcriptional modification events in the tail fat of sheep and DETs between thin-tailed and fat-tailed sheep remains unclear.MethodsIn the present study, we applied pooled PacBio isoform sequencing (Iso-Seq) to generate transcriptomic data of tail fat tissue from six sheep (three thin-tailed sheep and three fat-tailed sheep).By comparing with reference genome, potential gene loci and novel transcripts were identified.Post-transcriptional modification events, including AS and APA, and lncRNA in sheep tail fat were uncovered using pooled Iso-Seq data.Combining Iso-Seq data with and six RNAsequencing (RNA-Seq) data, DETs between thin-and fat-tailed sheep were identified.Protein
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BackgroundNowadays, both customers and producers prefer thin-tailed fat sheep.To effectively breed for this phenotype, it is important to identify candidate genes and uncover the genetic mechanism related to tail fat deposition in sheep.Accumulating evidence suggesting that post-transcriptional modification events of precursor-messenger RNA (pre-mRNA), including alternative splicing (AS) and alternative polyadenylation (APA), may regulate tail fat deposition in sheep.Differentially expressed transcripts (DETs) analysis is a way to identify candidate genes related to tail fat deposition.However, due to the technological limitation, post-transcriptional modification events in the tail fat of sheep and DETs between thin-tailed and fat-tailed sheep remains unclear.MethodsIn the present study, we applied pooled PacBio isoform sequencing (Iso-Seq) to generate transcriptomic data of tail fat tissue from six sheep (three thin-tailed sheep and three fat-tailed sheep).By comparing with reference genome, potential gene loci and novel transcripts were identified.Post-transcriptional modification events, including AS and APA, and lncRNA in sheep tail fat were uncovered using pooled Iso-Seq data.Combining Iso-Seq data with and six RNAsequencing (RNA-Seq) data, DETs between thin-and fat-tailed sheep were identified.Protein
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Additional file 4 All metrics (Microsoft Excel).
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Abstract The ubiquitin-specific protease 22 (USP22) is an oncogene and its expression is upregulated in many types of cancer. In the nucleus, USP22 functions as one subunit of the SAGA to regulate gene transcription. However, the genome-wide USP22 binding sites and its direct target genes are yet clear. In this study, we characterized the potential genomic binding sites of UPS22 and GCN5 by ChIP-seq using specific antibodies in HeLa cells. There were 408 overlapping putative target genes bound by both USP22 and GCN5. Motif analysis showed that the sequences bound by USP22 and GCN5 shared two common motifs. Gene ontology (GO) and pathway analysis indicated that the genes targeted by USP22 and GCN5 were involved in different physiological processes and pathways. Further RNA-seq, GO and pathway analyses revealed that knockdown of UPS22 induced differential expression of many genes that participated in diverse physiological processes, such as metabolic process. Integration of ChIP-seq and RNA-seq data revealed that UPS22 bound to the promoters of 56 genes. These findings may provide new insights into the regulation of USP22 on gene expression during the development of cervical cancer.
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HeLa
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Tavella_etal_Scer_transcriptome.gtf: Transcriptome of S.cerevisiae that includes the genomic coordinates of annotated features as well as de novo assembled transcripts mapped to genome S288C. Yeast was grown in normal and oxidative stress conditions, as described in the section "Biological Material". De novo transcript assembly was performed with Trinity, transcripts that did not overlap with annotated genes were kept and combined with the gene annotations.
Tavella_etal_tableofcounts.txt: Number of mapped reads per gene in Ribo-Seq (RF) and RNA-Seq (RNA) experiments, for control and oxidative stress (treated); Rep1: sequencing replicate 1; Rep2: sequencing replicate 2.
Tavella_etal_X_up.csv: list of genes significantly up-regulated by Ribo-Seq (X=RP) or RNA-Seq (X=RNA).
Tavella_etal_X_down.csv: list of genes significantly down-regulated by Ribo-Seq (X=RP) or RNA-Seq (X=RNA).
Tavella_etal_tableofcounts.txt: Number of mapped reads per gene in Ribo-Seq (RF) and RNA-Seq (RNA) experiments, for control and oxidative stress (treated); Rep1: sequencing replicate 1; Rep2: sequencing replicate 2.
Tavella_etal_X_up.csv: list of genes significantly up-regulated by Ribo-Seq (X=RP) or RNA-Seq (X=RNA).
Tavella_etal_X_down.csv: list of genes significantly down-regulated by Ribo-Seq (X=RP) or RNA-Seq (X=RNA).
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Expression (computer science)
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Additional file 6: Table S5. Predicted abundance and clinical annotations of pediatric AML and neuroblastoma TARGET patients that were used to evaluate the outcomes-predictive value of subclone abundances in diagnostic samples.
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