scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference

Z, Li; Nagai, James Shiniti; Kuppe, Christoph; Kramann, Rafael; Costa, Ivan G.

doi:10.1093/bioadv/vbad003

Cited by 15 publications

(14 citation statements)

References 19 publications

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“…We also compared RENIN with other methods that predict CREs using single cell datasets including Signac’s LinkPeaks, DIRECT-NET 25 , FigR 28 , SCENIC+ 29 , Cicero 43 , and scMEGA 44 . The univariable approach taken by LinkPeaks resulted in sub-0.500 AUCs with PT_VCAM1 and healthy CREs for both H3K27ac and H3K4me3 peaks (Fig.…”

Section: Resultsmentioning

confidence: 99%

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

Ledru,

Wilson,

Muto

et al. 2024

Nat Commun

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Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

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Section: Resultsmentioning

confidence: 99%

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

Ledru,

Wilson,

Muto

et al. 2024

Nat Commun

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“…Space Ranger (v2.0.0) with the reference genome GRCh38 2020-A was used to perform expression anEalysis, mapping, counting, and clustering. To localize transcription factor activity, we first created trajectories to obtain gene regulatory networks (GRNs) in scMEGA 61 . The multiome data was subdivided into aPT/PT-S1S2 cells and aTAL / C-TAL cells to identify the pseudotime spectrum of cells from health to adaptive injury states.…”

Section: Methodsmentioning

confidence: 99%

The chromatin landscape of healthy and injured cell types in the human kidney

Gisch,

Brennan,

Lake

et al. 2024

Nat Commun

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There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

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“…To find motifs enriched in the high-activity enhancers, all generated enhancers are divided into three groups according to their activity, high-activity group with activity larger than 3.0, low-activity group with activity less than 0.0 and mid-activity group with activity between them. STREME [22, 23] was employed to find relatively enriched motifs in each group.…”

Section: Methodsmentioning

confidence: 99%

High-Activity Enhancer Generation based on Feedback GAN with Domain Constraint and Curriculum Learning

Li,

Xiao,

Luo

et al. 2023

Preprint

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Enhancers are important cis-regulatory elements, enhancing the transcription of target genes. De novo design of high-activity enhancers is one of long-standing goals in generated biology for both clinical purpose and artificial life, because of their vital roles on regulation of cell development, differentiation, and apoptosis. But designing the enhancers with specific properties remains challenging, primarily due to the unclear understanding of enhancer regulatory codes. Here, we propose an AI-driven enhancer design method, named Enhancer-GAN, to generate high-activity enhancer sequences. Enhancer-GAN is firstly pre-trained on a large enhancer dataset that contains both low-activity and high-activity enhancers, and then is optimized to generate high-activity enhancers with feedback-loop mechanism. Domain constraint and curriculum learning were introduced into Enhancer-GAN to alleviate the noise from feedback loop and accelerate the training convergence. Experimental results on benchmark datasets demonstrate that the activity of generated enhancers is significantly higher than ones in benchmark dataset. Besides, we find 10 new motifs from generated high-activity enhancers. These results demonstrate Enhancer-GAN is promising to generate and optimize bio-sequences with desired properties.

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scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference

Cited by 15 publications

References 19 publications

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

The chromatin landscape of healthy and injured cell types in the human kidney

High-Activity Enhancer Generation based on Feedback GAN with Domain Constraint and Curriculum Learning

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