Dataset Information

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

ABSTRACT: In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

SUBMITTER: Liu J

PROVIDER: S-EPMC11336957 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

Liu Jiajia J Ma Jian J Wen Jianguo J Zhou Xiaobo X

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20240617 31

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-c ...[more]

PMID: 38887194

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Project description:ObjectiveAlthough glucagon-secreting α-cells and insulin-secreting β-cells have opposing functions in regulating plasma glucose levels, the two cell types share a common developmental origin and exhibit overlapping transcriptomes and epigenomes. Notably, destruction of β-cells can stimulate repopulation via transdifferentiation of α-cells, at least in mice, suggesting plasticity between these cell fates. Furthermore, dysfunction of both α- and β-cells contributes to the pathophysiology of type 1 and type 2 diabetes, and β-cell de-differentiation has been proposed to contribute to type 2 diabetes. Our objective was to delineate the molecular properties that maintain islet cell type specification yet allow for cellular plasticity. We hypothesized that correlating cell type-specific transcriptomes with an atlas of open chromatin will identify novel genes and transcriptional regulatory elements such as enhancers involved in α- and β-cell specification and plasticity.MethodsWe sorted human α- and β-cells and performed the "Assay for Transposase-Accessible Chromatin with high throughput sequencing" (ATAC-seq) and mRNA-seq, followed by integrative analysis to identify cell type-selective gene regulatory regions.ResultsWe identified numerous transcripts with either α-cell- or β-cell-selective expression and discovered the cell type-selective open chromatin regions that correlate with these gene activation patterns. We confirmed cell type-selective expression on the protein level for two of the top hits from our screen. The "group specific protein" (GC; or vitamin D binding protein) was restricted to α-cells, while CHODL (chondrolectin) immunoreactivity was only present in β-cells. Furthermore, α-cell- and β-cell-selective ATAC-seq peaks were identified to overlap with known binding sites for islet transcription factors, as well as with single nucleotide polymorphisms (SNPs) previously identified as risk loci for type 2 diabetes.ConclusionsWe have determined the genetic landscape of human α- and β-cells based on chromatin accessibility and transcript levels, which allowed for detection of novel α- and β-cell signature genes not previously known to be expressed in islets. Using fine-mapping of open chromatin, we have identified thousands of potential cis-regulatory elements that operate in an endocrine cell type-specific fashion.

Project description:Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations. IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Dataset Information

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

Publications

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

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