TAF-ChIP: An ultra low input approach for genome wide chromatin immunoprecipitation assay
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA448587 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8478970_1.fastq.gz | Fastqsanger.gz | |||
| SRR8478970_2.fastq.gz | Fastqsanger.gz | |||
| SRR8478980.fastq.gz | Fastqsanger.gz | |||
| SRR8478990_1.fastq.gz | Fastqsanger.gz | |||
| SRR8478990_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 40 |
Similar Datasets
Project description:The transcriptional regulation is often controlled by the epigenetic modifications or by chromatin associated proteins. To understand this regulation, chromatin immunoprecipitation (ChIP) followed by next generation sequencing is an invaluable and powerful technique. However, the major limitation of this approach is often the requirement of large amount of starting material for generating high-quality datasets, and often the workflow is laborious. This limitation also results in application of this approach to study of rare cell populations even more challenging, if not impossible. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high quality dataset from as few as 100 human and 1000 Drosophila cells. The method itself is straightforward and is by far less labor-intensive than conventional library preparation, and other contemporary low amount ChIP-Seq methods. Furthermore, this approach can be applied directly on 100 cells rather than relying on de-multiplexing strategies to generate the profile from limited number of cells. This can be extremely useful when the access to the starting material is very restricted, for example clinically isolated cells from patients. Using this approach we generated the H3K4Me3 and H3K9Me3 profiles from 100 K562 cells and 1000 sorted neural stem cells (NSC) from Drosophila. We benchmarked our TAF-ChIP datasets from K562 cells against the Encode datasets. For validating the TAF-ChIP datasets obtained from Drosophila NSCs we took advantage of Notch induced over proliferation specifically in type II NSCs. The epigenetic profile obtained from conventional ChIP-Seq approach and TAF-ChIP approach shows high degree of agreement, thereby underlining the utility of this approach for generating ChIP-Seq profiles from very low cell numbers.
2018-04-11 | GSE112633 | GEO
Project description:Chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) is a powerful technique to study transcriptional regulation. However, the requirement of millions of cells to generate results with high signal-to-noise ratio precludes it in the study of small cell populations. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality histone profiles from low cell numbers. The data obtained from the TAF-ChIP approach are amenable to standard tools for ChIP-Seq analysis, owing to its high signal-to-noise ratio. The epigenetic profiles from TAF-ChIP approach showed high agreement with conventional ChIP-Seq datasets, thereby underlining the utility of this approach.
| S-EPMC6653780 | biostudies-literature
Project description:Haematopoetic RNA-seq from ultra low input
| PRJNA419186 | ENA
Project description:Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions of cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data showed that cfMBD-seq performs equally to the standard MBD-seq (>1000 ng input) even when using 1 ng DNA as the input. cfMBD-seq demonstrated equivalent sequencing data quality as well as similar methylation profile when compared to cfMeDIP-seq. We showed that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulfite-free ultra-low input methylation profiling technology has a great potential in non-invasive and cost-effective cancer detection and classification.
2021-01-01 | GSE161331 | GEO
Project description:We applied ultra-low-input native ChIP-seq and profiled genome-wide H3K9me3 distribution in four germ cell types during spermatogenesis.
2020-07-17 | GSE137742 | GEO
Project description:Next Generation Sequencing has proven to be an exceptionally powerful tool in the field of genomics and transcriptomics. With recent development it is nowadays possible to analyze ultra-low input sample material down to single cells. Nevertheless investigating such sample material still limits the analysis to either the genome or transcriptome, hence a combined analysis of both types of nucleic acids from the same sample material is still in demand.We developed a protocol which enables the combined analysis of the genome as well as the transcriptome by Next Generation Sequencing from ultra-low input samples. The protocol was evaluated by sequencing sub-colony structures from human embryonic stem cells containing 150 to 200 cells. The method can be adapted to any available sequencing system (e.g. Illumina, SOLiD, 454, etc.).To our knowledge, this is the first report where sub-colonies of human embryonic stem cells have been analyzed both at the genomic as well as transcriptome level. The method of this proof of concept study may find useful practical applications for cases where only a limited number of cells are available, e.g. for tissues samples from biopsies, circulating tumor cells and cells from early embryonic development. The results we present demonstrate that a combined analysis of genomic DNA and messenger RNA from ultra low input samples is feasible and can readily be applied to other cellular systems with limited material available.
2015-11-17 | GSE69471 | GEO
Project description:Deeper understanding of T cell biology is crucial for the development of new therapeutics. Human naïve T cells have low RNA content and their numbers can be limiting; therefore we set out to determine the parameters for robust ultra-low input RNA sequencing. We performed transcriptome profiling at different cell inputs and compared three protocols: Switching Mechanism at 5’ End of RNA Template technology (SMART) with two different library preparation methods (Nextera (SMART_Nxt) and Clontech (SMART_CC)), and AmpliSeq technology. As the cell input decreased the number of detected coding genes decreased with SMART, while stayed constant with AmpliSeq. However, SMART enables detection of non-coding genes, which is not feasible for AmpliSeq. The detection is dependent on gene abundance, but not transcript length. The consistency between technical replicates and cell inputs was comparable across methods above 1K but highly variable at 100 cell input. Sensitivity of detection for differentially expressed genes decreased dramatically with decreased cell inputs in all protocols, support that additional approaches, such as pathway enrichment, are important for data interpretation at ultra-low input. Finally, T cell activation signature was detected at 1K cell input and above in all protocols, with AmpliSeq showing better detection at 100 cells.
2019-05-24 | GSE130882 | GEO
Project description:RNA-seq is the standard method for profiling gene expression in many biological systems. Due to the wide dynamic range and complex nature of the transcriptome, RNA-seq provides an incomplete characterisation, especially of lowly expressed genes and transcripts. Targeted RNA sequencing (RNA CaptureSeq) focuses sequencing on genes of interest, providing exquisite sensitivity for transcript detection and quantification. However, uses of CaptureSeq have focused on bulk samples and its performance on very small populations of cells is unknown. Here we show CaptureSeq greatly enhances transcriptomic profiling of target genes in ultra-low-input samples and provides equivalent performance to that on bulk samples. We validate the performance of CaptureSeq using multiple probe sets on samples of iPSC-derived cortical neurons. We demonstrate up to 275-fold enrichment for target genes, the detection of 10% additional genes and a greater than 5-fold increase in identified gene isoforms. Analysis of spike-in controls demonstrated CaptureSeq improved both detection sensitivity and expression quantification. Comparison to the CORTECON database of cerebral cortex development revealed CaptureSeq enhanced the identification of sample differentiation stage. CaptureSeq provides sensitive, reliable and quantitative expression measurements on hundreds-to-thousands of target genes from ultra-low-input samples and has the potential to greatly enhance transcriptomic profiling when samples are limiting.
2019-11-16 | GSE140442 | GEO
Project description:As the most abundant and best-characterized internal mRNA modification, N6-methyladenosine (m6A) emerges to play a critical regulatory role in wide range of physiological and pathological processes, including gametogenesis, neuronal development, obesity and tumorigenesis. Methylated RNA immunoprecipitation coupled with next-generation sequencing (MeRIP-seq) facilitates transcriptome-wide m6A profiling, also is the most widely used technique to understand the biological significance of m6A. However, it typically requires over 100 μg of total RNA or 107 cells as input materials, hampering its application in limited samples. Here, we develop tMeRIP-seq, a transposase assisted MeRIP-seq method to achieve m6A profiling using ultra-low amount of input RNA. By marrying Tn5 tagmentation to m6A-specific immunoprecipitation, tMeRIP-seq largely improves the efficiency of library construction and reduces the input materials to as little as 60 ng total RNA or 103 cells. We apply this method on a small droplet of human blood and recapitulate the m6A profile previously reported using conventional protocol. We find tMeRIP-seq is a convenient and powerful method to examine m6A in ultra-low input material, potentially providing m6A as a new layer of bio-marker for liquid biopsy.
2021-09-07 | GSE167307 | GEO
Project description:The development of combined chromatin immunoprecipitation and next generation sequencing (ChIP-seq) technologies has enabled genome-wide epigenetic profiling of numerous cell lines and tissue types. A major limitation of ChIP-seq, however, is the large number of cells required to generate high quality datasets, precluding the study of rare cell populations. Here, we present an ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) and sequencing method, to generate genome-wide histone mark profiles with high resolution and reproducibility from as few as one thousand cells. Using ULI-NChIP, we generated high quality maps of several covalent histone marks from 10^3-10^6 embryonic stem cells. To further validate our procedure we demonstrate that genome-wide H3K27me3 profiles generated from 10^3 E13.5 primordial germ cells (PGCs) using ULI-NChIP-seq show high similarity to recently generated datasets using 50-180× more material, illustrating the utility of this method for generating high quality libraries with improved complexity from rare cell populations.
2014-12-31 | GSE63523 | GEO