2C-ChIP: measuring chromatin immunoprecipitation signal from defined genomic regions with deep sequencing

A conserved HOTAIRM1-HOXA1 regulatory axis contributes early to neuronal differentiation

HOTAIRM1 is unlike most long non-coding RNAs in that its sequence is highly conserved across mammals. Such evolutionary conservation points to it having a role in key cellular processes. We previously reported that HOTAIRM1 is required to curb premature activation of downstream HOXA genes in a cell model recapitulating their sequential induction during development. We found that it regulates 3' HOXA gene expression by a mechanism involving epigenetic and three-dimensional chromatin changes. Here we show that HOTAIRM1 participates in proper progression through the early stages of neuronal differentiation. We found that it can associate with the HOXA1 transcription factor and contributes to its downstream transcriptional program. Particularly, HOTAIRM1 affects the NANOG/POU5F1/SOX2 core pluripotency network maintaining an undifferentiated cell state. HOXA1 depletion similarly perturbed expression of these pluripotent factors, suggesting that HOTAIRM1 is a modulator of this transcription factor pathway. Also, given that binding of HOTAIRM1 to HOXA1 was observed in different cell types and species, our results point to this ribonucleoprotein complex as an integral part of a conserved HOTAIRM1-HOXA1 regulatory axis modulating the transition from a pluripotent to a differentiated neuronal state.

Profiling Chromatin Landscape at High Resolution and Throughput with 2C-ChIP

Chromatin immunoprecipitation (ChIP) is used to probe the presence of proteins and/or their posttranslational modifications on genomic DNA. This method is often used alongside chromosome conformation capture approaches to obtain a better-rounded view of the functional relationship between chromatin architecture and its landscape. Since the inception of ChIP, its protocol has been modified to improve speed, sensitivity, and specificity. Combining ChIP with deep sequencing has recently improved its throughput and made genome-wide profiling possible. However, genome-wide analysis is not always the best option, particularly when many samples are required to study a given genomic region or when quantitative data is desired. We recently developed carbon copy-ChIP (2C-ChIP), a new form of the high-throughput ChIP analysis method ideally suited for these types of studies. 2C-ChIP applies ligation-mediated amplification (LMA) followed by deep sequencing to quantitatively detect specified genomic regions in ChIP samples. Here, we describe the generation of 2C-ChIP libraries and computational processing of the resulting sequencing data.

LAMPS: an analysis pipeline for sequence-specific ligation-mediated amplification reads

OBJECTIVE

Ligation-Mediated Amplification (LMA) is a versatile biochemical tool for amplifying selected DNA sequences. LMA has increased in popularity due to its integration within chromosome conformation capture (5C) and chromatin immunoprecipitation (2C-ChIP) methodologies. The output of either 5C or 2C-ChIP protocols is a single-read sequencing library of ligated primer pairs that may or may not be multiplexed. While many computational tools currently exist for read mapping and analysis, these tools neither fully support multiplexed libraries nor provide qualitative reporting on the LMA primers involved. Typically, the task of library demultiplexing or primer analysis is offloaded on to the user. Our aim was to develop an easy-to-use pipeline for processing (multiplexed) single-read sequencing data produced by sequence-specific LMA.

RESULTS

Here, we describe the Ligation-mediated Amplified, Multiplexed Primer-pair Sequence (LAMPS) analysis pipeline. LAMPS facilitates the analysis of multiplexed LMA sequencing data and provides a thorough assessment of a library's reads for a variety of experimental parameters (e.g., primer-pair efficiency). The standardized output of LAMPS allows for easy integration with downstream analyses, such as data track visualization on a genome browser. LAMPS is made publicly available on GitHub: https://github.com/BlanchetteLab/LAMPS.