A macrophage-hepatocyte glucocorticoid receptor axis coordinates fasting ketogenesis DOI Creative Commons
Anne Loft, Søren Fisker Schmidt, Giorgio Caratti

et al.

Cell Metabolism, Journal Year: 2022, Volume and Issue: 34(3), P. 473 - 486.e9

Published: Feb. 3, 2022

Language: Английский

On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework DOI Open Access
Giulia Ulpiani

The Science of The Total Environment, Journal Year: 2020, Volume and Issue: 751, P. 141727 - 141727

Published: Aug. 18, 2020

Language: Английский

Citations

412

TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment DOI Creative Commons
Ya Han, Yuting Wang, Xin Dong

et al.

Nucleic Acids Research, Journal Year: 2022, Volume and Issue: 51(D1), P. D1425 - D1431

Published: Nov. 2, 2022

Abstract The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number datasets are generated public domain, this update, TISCH2 has included 190 scRNA-seq covering 6 million cells 50 types, with 110 newly collected almost tripling compared previous release. Furthermore, includes several new functions that allow users to better utilize large-scale datasets. First, Dataset module, provides cell–cell communication results each dataset, facilitating analyses interacted cell types discovery significant ligand–receptor pairs between also transcription factor for dataset visualization top enriched factors type. Second, Gene adds identifying correlated genes providing survival information input genes. In summary, user-friendly, up-to-date well-maintained TME. freely available at http://tisch.comp-genomics.org/.

Language: Английский

Citations

295

Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data DOI Creative Commons
Qian Qin, Jingyu Fan, Rongbin Zheng

et al.

Genome biology, Journal Year: 2020, Volume and Issue: 21(1)

Published: Feb. 7, 2020

Abstract We developed Lisa ( http://lisa.cistrome.org/ ) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on input sets, first uses histone mark ChIP-seq and chromatin accessibility profiles construct a model related regulation these genes. Using TR peaks imputed binding sites, probes models using in silico deletion find most relevant TRs. Applied sets derived from targeted TF perturbation experiments, boosted performance cistromes outperformed alternative methods identifying perturbed

Language: Английский

Citations

242

Gene regulatory network inference in the era of single-cell multi-omics DOI
Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller‐Dott

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(11), P. 739 - 754

Published: June 26, 2023

Language: Английский

Citations

201

Integrative analyses of single-cell transcriptome and regulome using MAESTRO DOI Creative Commons
Chenfei Wang, Dongqing Sun, Xin Huang

et al.

Genome biology, Journal Year: 2020, Volume and Issue: 21(1)

Published: Aug. 7, 2020

Abstract We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses single-cell RNA-seq (scRNA-seq) ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions pre-processing, alignment, quality control, expression chromatin accessibility quantification, clustering, differential analysis, annotation. By modeling gene regulatory potential accessibilities at level, outperforms existing methods integrating cell clusters between scRNA-seq scATAC-seq. Furthermore, supports automatic cell-type annotation using predefined type marker genes identifies driver regulators scATAC-seq peaks.

Language: Английский

Citations

163

In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target DOI Creative Commons
Xiaoqing Wang, Collin Tokheim, Shengqing Gu

et al.

Cell, Journal Year: 2021, Volume and Issue: 184(21), P. 5357 - 5374.e22

Published: Sept. 27, 2021

Language: Английский

Citations

140

Predicting measures of soil health using the microbiome and supervised machine learning DOI Creative Commons
Roland C. Wilhelm, Harold M. van Es, Daniel H. Buckley

et al.

Soil Biology and Biochemistry, Journal Year: 2021, Volume and Issue: 164, P. 108472 - 108472

Published: Oct. 29, 2021

Language: Английский

Citations

114

Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α DOI Creative Commons

Lijian Wu,

Yiteng Jin,

Xi Zhao

et al.

Cell Metabolism, Journal Year: 2023, Volume and Issue: 35(9), P. 1580 - 1596.e9

Published: July 27, 2023

Language: Английский

Citations

112

Lysine catabolism reprograms tumour immunity through histone crotonylation DOI
Huairui Yuan, Xujia Wu, Qiulian Wu

et al.

Nature, Journal Year: 2023, Volume and Issue: 617(7962), P. 818 - 826

Published: May 17, 2023

Language: Английский

Citations

100

WhichTF is functionally important in your open chromatin data? DOI Creative Commons
Yosuke Tanigawa,

Ethan S. Dyer,

Gill Bejerano

et al.

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(8), P. e1010378 - e1010378

Published: Aug. 30, 2022

We present WhichTF, a computational method to identify functionally important transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF applies an ontology-guided functional approach compute novel enrichment by integrating measurements, high-confidence pre-computed conservation-aware TF binding sites, and putative gene-regulatory models. Comparison with prior sheer abundance-based methods reveals the unique ability of context-specific TFs relevance, including NF-κB family members in lymphocytes GATA cardiac cells. distinguish transcriptional regulatory landscape closely related samples, we apply differential analysis demonstrate its utility lymphocyte, mesoderm developmental, disease find suggestive, under-characterized such as RUNX3 development GLI1 systemic lupus erythematosus. also known for stress response, suggesting routine experimental caveats that warrant careful consideration. yields biological insight into molecular mechanisms TF-mediated regulation diverse contexts, human mouse cell types, fate trajectories, disease-associated

Language: Английский

Citations

96