Long read sequencing technologies now allow high‐quality sequencing of RNAs (or their cDNAs) that are hundreds to thousands of nucleotides long. Long read sequences of nascent RNA provide single‐nucleotide‐resolution information about co‐transcriptional RNA processing events—e.g., splicing, folding, and base modifications. Here, we describe how to isolate nascent RNA from mammalian cells through subcellular fractionation of chromatin‐associated RNA, as well as how to deplete poly(A)+RNA and rRNA, and, finally, how to generate a full‐length cDNA library for use on long read sequencing platforms. This approach allows for an understanding of coordinated splicing status across multi‐intron transcripts by revealing patterns of splicing or other RNA processing events that cannot be gained from traditional short read RNA sequencing. © 2020 Wiley Periodicals LLC.
Cleavage under targets and release using nuclease (CUT&RUN) is a recently developed chromatin profiling technique that uses a targeted micrococcal nuclease cleavage strategy to obtain high‐resolution binding profiles of protein factors or to map histones with specific post‐translational modifications. Due to its high sensitivity, CUT&RUN allows quality binding profiles to be obtained with only a fraction of the starting material and sequencing depth typically required for other chromatin profiling techniques such as chromatin immunoprecipitation. Although CUT&RUN has been widely adopted in multiple model systems, it has rarely been utilized in
- NSF-PAR ID:
- 10446383
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Current Protocols
- Volume:
- 2
- Issue:
- 6
- ISSN:
- 2691-1299
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Basic Protocol 1 : Subcellular fractionationBasic Protocol 2 : Nascent RNA isolation and adapter ligationBasic Protocol 3 : cDNA amplicon preparation -
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Abstract Histone post‐translational modifications (PTMs) play important roles in many biological processes, including gene regulation and chromatin dynamics, and are thus of high interest across many fields of biological research. Chromatin immunoprecipitation coupled with sequencing (ChIP‐seq) is a powerful tool to profile histone PTMs
in vivo . This method, however, is largely dependent on the specificity and availability of suitable commercial antibodies. While mass spectrometry (MS)–based proteomic approaches to quantitatively measure histone PTMs have been developed in mammals and several other model organisms, such methods are currently not readily available in plants. One major challenge for the implementation of such methods in plants has been the difficulty in isolating sufficient amounts of pure, high‐quality histones, a step rendered difficult by the presence of the cell wall. Here, we developed a high‐yielding histone extraction and purification method optimized forArabidopsis thaliana that can be used to obtain high‐quality histones for MS. In contrast to other methods used in plants, this approach is relatively simple, and does not require membranes or additional specialized steps, such as gel excision or chromatography, to extract highly purified histones. We also describe methods for producing MS‐ready histone peptides through chemical labeling and digestion. Finally, we describe an optimized method to quantify and analyze the resulting histone PTM data using a modified version of EpiProfile 2.0 for Arabidopsis. In all, the workflow described here can be used to measure changes to histone PTMs resulting from various treatments, stresses, and time courses, as well as in different mutant lines. © 2022 Wiley Periodicals LLC.Basic Protocol 1 : Nuclear isolation and histone acid extractionBasic Protocol 2 : Peptide labeling, digestion, and desaltingBasic Protocol 3 : Histone HPLC‐MS/MS and data analysis -
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Abstract Background Epigenomic profiling assays such as ChIP-seq have been widely used to map the genome-wide enrichment profiles of chromatin-associated proteins and posttranslational histone modifications. Sequencing depth is a key parameter in experimental design and quality control. However, due to variable sequencing depth requirements across experimental conditions, it can be challenging to determine optimal sequencing depth, particularly for projects involving multiple targets or cell types.
Results We developed the
peaksat R package to provide target read depth estimates for epigenomic experiments based on the analysis of peak saturation curves. We appliedpeaksat to establish the distinctive read depth requirements for ChIP-seq studies of histone modifications in different cell lines. Usingpeaksat, we were able to estimate the target read depth required per library to obtain high-quality peak calls for downstream analysis. In addition,peaksat was applied to other sequence-enrichment methods including CUT&RUN and ATAC-seq.Conclusion peaksat addresses a need for researchers to make informed decisions about whether their sequencing data has been generated to an adequate depth and subsequently sufficient meaningful peaks, and failing that, how many more reads would be required per library.peaksat is applicable to other sequence-based methods that include calling peaks in their analysis. -
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Abstract Glycosaminoglycans (GAGs) are linear polysaccharides found in a variety of organisms. GAGs contribute to biochemical pathway regulation, cell signaling, and disease progression. GAG sequence information is imperative for determining structure‐function relationships. Recent advances in electron‐activation techniques paired with high‐resolution mass spectrometry allow for full sequencing of GAG structures. Electron detachment dissociation (EDD) and negative electron transfer dissociation (NETD) are two electron‐activation methods that have been utilized for GAG characterization. Both methods produce an abundance of informative glycosidic and cross‐ring fragment ions without producing a high degree of sulfate decomposition. Here, we provide detailed protocols for using EDD and NETD to sequence GAG chains. In addition to protocols directly involving performing these MS/MS methods, protocols include sample preparation, method development, internal calibration, and data analysis. © 2021 Wiley Periodicals LLC.
This article was corrected on 27 July 2022. See the end of the full text for details.
Basic Protocol 1 : Preparation of glycosaminoglycan samplesBasic Protocol 2 : FTICR method developmentBasic Protocol 3 : Internal calibration with NaTFABasic Protocol 4 : Electron Detachment Dissociation (EDD) of GAG samplesBasic Protocol 5 : Negative electron transfer dissociation (NETD) of GAG samplesBasic Protocol 6 : Analysis of MS/MS data -
Abstract Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to identify factor binding to genomic DNA and chromatin modifications. ChIP-seq data analysis is affected by genomic regions that generate ultra-high artifactual signals. To remove these signals from ChIP-seq data, the Encyclopedia of DNA Elements (ENCODE) project developed comprehensive sets of regions defined by low mappability and ultra-high signals called blacklists for human, mouse (Mus musculus), nematode (Caenorhabditis elegans), and fruit fly (Drosophila melanogaster). However, blacklists are not currently available for many model and nonmodel species. Here, we describe an alternative approach for removing false-positive peaks called greenscreen. Greenscreen is easy to implement, requires few input samples, and uses analysis tools frequently employed for ChIP-seq. Greenscreen removes artifactual signals as effectively as blacklists in Arabidopsis thaliana and human ChIP-seq dataset while covering less of the genome and dramatically improves ChIP-seq peak calling and downstream analyses. Greenscreen filtering reveals true factor binding overlap and occupancy changes in different genetic backgrounds or tissues. Because it is effective with as few as two inputs, greenscreen is readily adaptable for use in any species or genome build. Although developed for ChIP-seq, greenscreen also identifies artifactual signals from other genomic datasets including Cleavage Under Targets and Release Using Nuclease. We present an improved ChIP-seq pipeline incorporating greenscreen that detects more true peaks than other methods.more » « less
