CUT&Tag seq of Spi-B in small intestinal tuft cells
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1055504 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The binding peaks of Spi-B in tuft cells were predominantly located in intergenic regions and introns, mirroring the distribution of PU.1 binding peaks in the genome.
2025-07-03 | GSE251823 | GEO
Project description:To map the chromatin binding sites for SMC1 in OE19 cells, we performed CUT&TAG.
2024-09-25 | E-MTAB-14090 | biostudies-arrayexpress
Project description:To map the chromatin binding sites for various factors in OE19 cells, we performed CUT&TAG.
2023-02-24 | E-MTAB-11356 | biostudies-arrayexpress
Project description:HIF2A(EPAS1) geenome_wide localization by Cut & Tag in MRC5 human fibroblasts ectopically expressing HIF2A in the pBabe vector, and in control MRC5 human fibroblasts containing only the pBabe vector. 2 biological replicates were done for the MRC5-HIF2A cells. Cut & Tag with an anti-H3-K27me3 Ab was done as a positive control for the Cut & Tag experiments.
2025-01-17 | E-MTAB-13732 | biostudies-arrayexpress
Project description:To investigate the transcriptional regulation of Xbp1s in ILC2s, sorted large intestinal ILC2s from mice were used for CUT&Tag sequencing to analyze the target of Xbp1s.
2024-05-28 | GSE268234 | GEO
Project description:To investigate GLI3 TF targets during organoid fate decisions, we performed Cut&Tag of GLI3 binding
2022-08-28 | E-MTAB-12006 | biostudies-arrayexpress
Project description:We developed scNanoSeq-CUT&Tag, a streamlined method by adapting a modified CUT&Tag protocol to Oxford Nanopore sequencing platform for efficient chromatin modification profiling at single-cell resolution. We firstly tested the performance of scNanoSeq-CUT&Tag on six human cell lines: K562, 293T, GM12878, HG002, H9, HFF1 and adult mouse blood cells, it showed that scNanoSeq-CUT&Tag can accurately distinguish different cell types in vitro and in vivo. Moreover, scNanoSeq-CUT&Tag enables to effectively map the allele-specific epigenomic modifications in the human genome andallows to analyze co-occupancy of histone modifications. Taking advantage of long-read sequencing,scNanoSeq-CUT&Tag can sensitively detect epigenomic state of repetitive elements. In addition, by applying scNanoSeq-CUT&Tag to testicular cells of adult mouse B6D2F1, we demonstrated that scNanoSeq-CUT&Tag maps dynamic epigenetic state changes during mouse spermatogenesis. Finally, we exploited the epigenetic changes of human leukemia cell line K562 during DNA demethylation, it showed that NanoSeq-CUT&Tag can capture H3K27ac signals changes along DNA demethylation. Overall, we prove that scNanoSeq-CUT&Tag is a valuable tool for efficiently probing chromatin state changes within individual cells.
2024-09-06 | GSE249799 | GEO
Project description:Precise profiling of epigenomes, including histone modifications and transcription factor binding sites, is essential for better understanding gene regulatory mechanisms. Cleavage Under Targets & Tagmentation (CUT&Tag) is an easy and low-cost epigenomic profiling method that can be performed on a low number of cells and at the single-cell level. A large number of CUT&Tag datasets have been generated in various biological systems, providing a valuable resource. CUT&Tag experiments use the hyperactive transposase Tn5 for tagmentation. We found that the preference of Tn5 captured reads toward accessible chromatin regions can influence the distribution of CUT&Tag reads and cause open chromatin biases, further confounding the analysis of CUT&Tag data. The high sparsity of single-cell sequencing data makes the open chromatin biases more substantial than in bulk sequencing data. Here, we present a comprehensive computational method, PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), to mitigate the open chromatin bias inherent in CUT&Tag data at both bulk and single-cell levels. By integrating existing transcriptome and epigenome data using machine learning and comprehensive modeling, we demonstrate that PATTY yields more accurate and robust detection of occupancy sites for both active and repressive histone marks than existing methods, with experimental validation. We further designed a single-cell CUT&Tag analysis framework by utilizing this model and showing improved cell clustering from bias-corrected single-cell CUT&Tag data compared to using raw data. This model paved the way for further development of computational tools for improving bulk and single-cell CUT&Tag data analysis.
2026-02-06 | GSE298565 | GEO