Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Chen, Xi; Wang, Yuan; Cappuccio, Antonio; Cheng, Wan-Sze; Ruf-Zamojski, Frederique; Nair, Venugopalan D.; Miller, Clare M.; Rubenstein, Aliza B.; Nudelman, German; Tadych, Alicja; Theesfeld, Chandra L.; Vornholt, Alexandria; George, Mary‐Catherine; Ruffin, Felicia; Dagher, Michael; Chawla, Daniel G.; Soares‐Schanoski, Alessandra; Spurbeck, Rachel R.; Ndhlovu, Lishomwa C.; Sebra, Robert; Kleinstein, Steven H.; Letizia, Andrew G.; Ramos, Irene; Fowler, Vance G.; Woods, Christopher W.; Zaslavsky, Elena; Troyanskaya, Olga G.; Sealfon, Stuart C.

doi:10.1038/s43588-023-00476-5

Cited by 10 publications

(13 citation statements)

References 67 publications

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“…To further evaluate the reliability and robustness of the SPEEDI batch inference method and overall framework, we applied SPEEDI to a 20 subject scRNA-seq dataset generated in our laboratory for a study of the responses in peripheral blood mononuclear cells (PBMC) to S. aureus infection. 10 Before batch correction, a number of UMAP clusters were distinguished by sample, a pattern consistent with the presence of severe batch effects ( Figures 4A and S1 ). As can be seen in the T/NK lymphocyte subpopulations, SPEEDI data-inferred batch labels led to much better batch correction compared with batch labels obtained from metadata ( Figure 4B ; see Figure S2 for similar results obtained with all major cell types).…”

Section: Resultssupporting

confidence: 57%

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Automated single-cell omics end-to-end framework with data-driven batch inference

Wang,

Thistlethwaite,

Tadych

et al. 2023

Preprint

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SummaryTo facilitate single cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI’s data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed athttps://speedi.princeton.edu/.

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Section: Resultssupporting

confidence: 57%

“…The scRNA-seq human S. aureus infection PBMC dataset was generated by our lab and previously reported in Chen et al. 10 Female mouse pituitary study…”

Section: Human Bloodstream S Aureus Infection Studymentioning

confidence: 99%

Automated single-cell omics end-to-end framework with data-driven batch inference

Wang,

Thistlethwaite,

Tadych

et al. 2023

Preprint

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“…For these analyses, we focused on fast, intermediate, and slow fibers, as well as LUM+ FAP cells, as these cell types had adequate cell numbers and robust responses to exercise for regulatory circuit inference. Applying recently developed complementary bioinformatic methods, 39,40 a total of 2,025 exercise-regulated circuit genes were identified among the four cell types, with the highest number found in the fast fibers (Figure 4B). While some circuits were shared among cell types, the slow and fast fiber types each displayed distinct regulatory programs regulating hundreds of unique genes reflecting fiber and cell type-specific transcriptional remodeling programs activated by acute endurance exercise (Figure 4B).…”

Section: Resultsmentioning

confidence: 99%

“…Multiome Accessibility Gene Integration Calling And Looping (MAGICAL) was applied to the sc multiome data to identify regulatory circuits that include transcription factors, associated chromatin sites and target genes. 40 MAGICAL is a Bayesian framework that iteratively models chromatin accessibility and gene expression variation across cells and samples in each cell type. It estimates the confidence of TF binding at open chromatin regions and also their linkages to target genes as regulatory circuits.…”

Section: Methodsmentioning

confidence: 99%

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Rubenstein,

Smith,

Zhang

et al. 2023

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SummaryEndurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well asLUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.

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“…The extracted genes were selected using scATAC-seq alone and a scoring metric for genes targeted at the Transcription Start Site (TSS) 10,18 . Therefore, gene ontologies and pathways may not function effectively as libraries for enrichment analysis due to this method.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Methicillin Resistance inStaphylococcus AureusSepsis Using TDbasedUFE

Watanabe,

Taguchi

2024

Preprint

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scATAC-seq explains chromatin accessibility at cell-type resolution. Accordingly, this process is crucial for advancing our understanding of pathology and disease states. However, annotated data from scATAC-seq are both extensive and sparse; thus, conducting multidimensional analyses under multiple conditions is a challenging task. TDbasedUFE is a valuable tool for analyzing scATAC-seq data as it can extract genes in an unsupervised manner under multiple conditions based on tensor decomposition. We analyzed scATAC-seq data from the peripheral blood mononuclear cells of patients with sepsis infected withS. aureususing TDbasedUFE. We extracted genes that exhibited different responses in methicillin-resistant (MSSA) and methicillin-sensitiveS. aureus(MSSA) strains in sepsis for each cell type. Subsequently, we searched for studies containing gene sets similar to the extracted genes and predicted their functions. We also constructed protein-protein interactions (PPIs) for the extracted genes, defined hub proteins as central to the interactions based on degrees and clustering coefficients, and investigated the functions of these hub proteins. The genes of interest were abundant across all cell types, ranging from 710 to 1,372 genes. The functions of the extracted genes were predicted to be associated with several diseases or physiological substances. The hub proteins identified from the PPI analysis were mainly related to the ribosome, and their functions were associated with protein synthesis. These results highlight the suitability of TDbasedUFE for the analysis of scATAC-seq data. The functions of the genes identified in this study may provide insights into new promising therapeutic approaches, considering the distinction between methicillin resistance andS. aureussepsis.

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Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Cited by 10 publications

References 67 publications

Automated single-cell omics end-to-end framework with data-driven batch inference

Automated single-cell omics end-to-end framework with data-driven batch inference

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Analysis of Methicillin Resistance inStaphylococcus AureusSepsis Using TDbasedUFE

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