TAF-ChIP: An ultra low input approach for genome wide chromatin immunoprecipitation assay
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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 |
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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.
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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.
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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.
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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.
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