chemistry

PLAC-Seq

PLAC-Seq is a chemistry topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand PLAC-Seq rather than just read about it. In short: Proximity ligation-assisted chromatin immunoprecipitation sequencing (PLAC-seq) is a chromatin conformation capture(3C)-based technique to detect and quantify genomic chromatin structure from a protein-centric approach. PLAC-seq combines in situ Hi-C and chromatin immunoprecipitation (ChIP), which allows for the identification of long-range chromatin interactions at a high resolution with low sequencing costs.

Key takeaways

PLAC-Seq belongs to chemistry; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect PLAC-Seq to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of PLAC-Seq from memory before moving on to harder problems.

Reference excerpt

Proximity ligation-assisted chromatin immunoprecipitation sequencing (PLAC-seq) is a chromatin conformation capture(3C)-based technique to detect and quantify genomic chromatin structure from a protein-centric approach. PLAC-seq combines in situ Hi-C and chromatin immunoprecipitation (ChIP), which allows for the identification of long-range chromatin interactions at a high resolution with low sequencing costs. Mapping long-range 3-dimensional(3D) chromatin interactions is important in identifying transcription enhancers and non-coding variants that can be linked to human diseases. Different 3C-based techniques have been used to study the higher-order 3D chromatin structure, and it has been combined with high-throughput sequencing to determine the chromatin structure on a genome-wide level. Hi-C is one of the most widely used 3C-based techniques because it allows for high-resolution (kilobase-scale) genome-topology identification. However, it requires billions of sequencing reads which has limited its application. Another commonly used 3C-based technique is chromatin interaction analysis by paired-end tag sequencing (ChiA-PET). ChiA-PET can identify long-range interactions of transcription promoters and enhancers at a high resolution but requires millions of cells. PLAC-seq alleviates these issues by using in situ Hi-C, which creates long-range DNA contacts in situ in the nucleus before lysis. Unlike ChiA-PET which performs ChIP and proximity ligation after chromatin shearing, performing proximity ligation in the nuclei first prevents large disruptions of protein/DNA complexes. This decreases false-positive interactions and improves DNA contact capture efficiency, meaning that PLAC-seq is more accurate and requires fewer cells.

History PLAC-seq was developed in 2016 and an almost identical technique called HiChIP was also developed in the same year. Both methods combine in situ Hi-C and ChIP but have different library preparation methods. While PLAC-seq uses biotin pull-down followed by end-repair, adapter ligation, and PCR, HiChIP usesTn5 tagmentation, biotin pull-down, and PCR. However, both techniques can use the same quality control and data analysis techniques. Different computation software tools can be used to analyze the data from PLAC-seq, for example, Fit-Hi-C, HiCCUPS, Mango, Hichipper, MAPS, and FitHiChIP. Many of the earlier software tools were developed for other 3C-based technologies and were not optimized for PLAC-seq/HiChIP data. Fit-Hi-C and HiCCUPS, both developed in 2014, were mainly developed for Hi-C data, and utilize a matrix-balancing-based normalization approach. Mango was developed in 2015, and is mainly used for ChIA-PET data, but has high false-positive rates in analyzing PLAC-seq/HiChIP data due to the different biases. Hichipper was developed in 2018 to alleviate this issue and introduced a bias-correcting algorithm, but it still has difficulties identifying protein interactions between protein binding and non-protein binding regions on the chromosome. MAPS and FitHiChIP were developed in 2019 as a PLAC-seq/HiChIP-specific analysis pipeline, and are generally thought to be more effective than the existing models to analyze PLAC-seq/HiChIp data.

Procedure The general workflow of PLAC-seq involves cell harvesting and crosslinking, in situ digestion and proximity ligation, ChIP, library construction, sequencing, and data analysis. The first step of PLAC-seq includes the preparation and crosslinking of cell and tissue samples, which typically begins with cell collection through centrifugation. The next step involves the use of a DNA crosslinking agent such as formaldehyde (HCOH) followed by the addition of glycine to stop the crosslinking reaction. The cross-linked cells can then be pelleted by centrifugation and either stored at -80 or used in the next step of the procedure. In situ digestion involves cell lysis with the use of a lysis buffer followed by digestion with a restriction enzyme MboI. This step allows for uniform digestion of genetic material while keeping the crosslinked regions of the chromosome intact. After inactivation of the digestion reaction, dNTPs and biotin are added in order to repair overhangs and mark the DNA for pull down respectively. In situ proximity ligation occurs when the biotinylated ends of the crosslinked DNA are ligated with each other. Chromatin fragmentation by sonication allows for the shearing of non-crosslinked fragments of DNA. This is followed by immunoprecipitation of biotinylated DNA through the use of antibody-coated beads. The DNA is then reverse-crosslinked and purified using column-based DNA purification or phenol-chloroform extraction. The library construction step first involves the pull-down of biotinylated DNA and the addition of sequencing adapters. The cycle number for amplification needs to be determined prior to the final amplification and library purification. Data analysis of PLAC-seq sequencing data can be carried out in multiple ways, however, the common methods involve the use of Fit-Hi-C, FitHiChIP, and MAPS. Data analysis involves mapping to a reference genome, using software tools such as Hichipper to identify peaks, and downstream analysis involving peak comparison and functional enrichment analysis. The resulting data can also be integrated with other genomic data such as Hi-C or RNA-seq in order to identify potential regulatory networks.

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with PLAC-Seq

Start with the simplest possible case. Write down what PLAC-Seq claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In chemistry, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to PLAC-Seq before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about PLAC-Seq ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of PLAC-Seq

In research: PLAC-Seq appears in chemistry research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses PLAC-Seq in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: PLAC-Seq is common in secondary-school and first-year university syllabi. It links to neighbouring topics DNA sequencing methods, Molecular biology techniques, so understanding it makes those chapters shorter.
In everyday life: Look for PLAC-Seq outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study PLAC-Seq in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what PLAC-Seq means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain PLAC-Seq out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is PLAC-Seq in simple terms?

Why does PLAC-Seq matter?

Because it connects several chemistry ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study PLAC-Seq?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on PLAC-Seq.