Development of miniBac-seq to profile bacterial transcriptome from ultra-low input RNA amount
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ABSTRACT:
ORGANISM(S): Escherichia coli BW25113
PROVIDER: GSE184365 | GEO | 2022/06/30
REPOSITORIES: GEO
Similar Datasets
Project description:We developed miniBac-seq, a strand-specific method for high-quality bacterial RNA-seq library construction from sub-nanogram of total RNA, equivalent to a few thousand bacterial cells; we also tested its potent to be adopted as a single cell RNA-seq method. Here we provide the supporting data to the paper introducing this technology.
2022-07-22 | GSE195463 | 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
Project description:Genome-wide profiling of transcription factors based on massive parallel sequencing of immunoprecipitated chromatin (ChIP-seq) requires nanogram amounts of DNA. Here we describe a high-fidelity, single-tube linear DNA amplification method (LinDA) for ChIP-seq and reChIP-seq with picogram DNA amounts obtained from a few thousand cells. This amplification technology will facilitate global analyses of transcription-factor binding and chromatin with very small cell populations, such as stem or cancer-initiating cells.
2011-10-11 | GSE32332 | GEO
Project description:Development of miniBac-seq to profile bacterial transcriptome from ultra-low input RNA amount
| PRJNA764223 | ENA
Project description:RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species which are often present in low abundance. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification. EMBR-seq results in greater than 80% of the sequenced RNA molecules deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq employs a single oligonucleotide per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. Thus, EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.
2020-10-22 | GSE149666 | GEO
Project description:Genome-wide profiling of transcription factors based on massive parallel sequencing of immunoprecipitated chromatin (ChIP-seq) requires nanogram amounts of DNA. Here we describe a high-fidelity, single-tube linear DNA amplification method (LinDA) for ChIP-seq and reChIP-seq with picogram DNA amounts obtained from a few thousand cells. This amplification technology will facilitate global analyses of transcription-factor binding and chromatin with very small cell populations, such as stem or cancer-initiating cells. In total 5 samples were generated from the F9 teratocarcinoma cell system: mRXRa-RARg-LinDA;mRXRa_100xfold-diluted_LinDA; mRXRa(1) and two others previously described in GSE30538 (mRXRa(2): !Series_sample_id=GSM757796; mRARg : !Series_sample_id=GSM757803). In addition, 14 samples were generated from the H3396 breast cancer cell system. Those samples containing the label "LinDA" has been amplified following the Linear DNA amplification method developed by Pattabhiraman et al. ((2011), Nature Methods 8, 565-67) prior library preparation for Solexa sequencing.
2011-10-11 | E-GEOD-32332 | biostudies-arrayexpress
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: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:our work established a powerful method for physically studying TF-RNA interaction in very few cells, and provide valuable recourse for decoding more precious regulatory network of Runx1 during embryonic hematopoiesis.
2022-08-26 | GSE211847 | 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