Single-Cell Omics in Metabolic Disorders

Syed, Nida Ali; Bhatti, Attya; Ahmed, Shanzay; Syed, Huma; John, Peter

doi:10.1016/b978-0-12-817532-3.00008-6

Cited by 2 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The risk haplotype increased the presence of a less adherent, less contractile phenotype within a VSMC population, but phenotype penetrance was variable; heterogeneous expression of specific isoforms of an lncRNA, which could potentially exacerbate CAD and was especially apparent when populations were sorted, could drive the variability that we observed in the lines across and within patients. Although we focused specifically on the 9p21 locus, the majority of disease-associated SNPs are in noncoding loci, and thus, it is important to recognize that some of the heterogeneity observed in disease progression ( 41 – 43 ) could be due to this. We believe that our findings are supportive of a general approach to exploring mechanisms by which noncoding loci enhance risk for disease and in which cellular heterogeneity is a hallmark.…”

Section: Discussionmentioning

confidence: 99%

“…While adhesion sorting is not novel, it has primarily been used to study cancer ( 47 ) or sort rare populations from blood, such as circulating tumor cells ( 48 – 50 ) or stem cells ( 51 ). Many diseases, including CAD, progress through the development of divergent behaviors within a cell type ( 41 – 43 , 52 ); thus, application of biophysical sorting has the potential to unveil key insights where other methods cannot. For example, we found that adhesion-based sorting of VSMCs allowed us to isolate subpopulations of weakly and strongly adherent phenotypes and directly compare them to better assess phenotype plasticity.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous expression of alternatively spliced lncRNA mediates vascular smooth cell plasticity

Mayner,

Masutani,

Demeester

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

9p21.3 locus polymorphisms have the strongest correlation with coronary artery disease, but as a noncoding locus, disease connection is enigmatic. The lncRNA ANRIL found in 9p21.3 may regulate vascular smooth muscle cell (VSMC) phenotype to contribute to disease risk. We observed significant heterogeneity in induced pluripotent stem cell–derived VSMCs from patients homozygous for risk versus isogenic knockout or nonrisk haplotypes. Subpopulations of risk haplotype cells exhibited variable morphology, proliferation, contraction, and adhesion. When sorted by adhesion, risk VSMCs parsed into synthetic and contractile subpopulations, i.e., weakly adherent and strongly adherent, respectively. Of note, >90% of differentially expressed genes coregulated by haplotype and adhesion and were associated with Rho GTPases, i.e., contractility. Weakly adherent subpopulations expressed more short isoforms of ANRIL , and when overexpressed in knockout cells, ANRIL suppressed adhesion, contractility, and αSMA expression. These data suggest that variable lncRNA penetrance may drive mixed functional outcomes that confound pathology.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Heterogeneous expression of alternatively spliced lncRNA mediates vascular smooth cell plasticity

Mayner,

Masutani,

Demeester

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering

Wang

et al. 2023

Bioinformatics

View full text Add to dashboard Cite

Motivation Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. Results To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial (ZINB) reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on twenty scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. Availability The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Single-Cell Omics in Metabolic Disorders

Cited by 2 publications

References 37 publications

Heterogeneous expression of alternatively spliced lncRNA mediates vascular smooth cell plasticity

Heterogeneous expression of alternatively spliced lncRNA mediates vascular smooth cell plasticity

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering

Contact Info

Product

Resources

About