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Measuring methylation: from arrays to sequencing

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Вопросы

Что такое CpG (что означает буква p?)
Что такое CpG островки? Есть ли четкие критерии их поиска?
Почему CpG динуклеотидов в геноме меньше, чем можно ожидать?
Какой эффект оказывает метилирование на экспрессию генов и почему?
Какой белок производит метилирование цитозинов?
Какой белок производить де-метилирование?

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Часто говорят не только про островки

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DNA methylation and evolution

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Methylation can regulate �gene expression

Plot from Peter Hickey

http://meeting.dxy.cn/oemethylation2012/article/i18782.html

Methylation at a single CpG vs. gene expression

Each point is one sample

Methylation is important because it can regulate the expression of genes.

So, sometimes we see a relationship between methylation and gene expression that looks like what you see here in this plot on the right.

Each point in this plot is from one sample and DNA methylation is shown on the x axis and gene expression is on the y

And you can see that as DNA methylation increases gene expression decreases and vice versa.

Now the reason that this can happen is shown here on the left.

You can see that in a normal cell, the CpG lollipops in the body of the gene are methylated, while the ones in the promoter are unmethylated and so this gene is expressed in a normal cell.

However, in cancer for example, we can lose the methylation in the gene body and gain methylation in the promoter, which can lead to this gene being supressed.

This is when you see the sort of relationship show in this plot.

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DNA methylation and cancer

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Methylation changes coat colour of Agouti mice

Dolinoy 2008, Nutr Rev.

This gene controls coat colour in Agouti mice

These CpG sites in the promoter change PS1A expression depending on methylation

These mice are genetically identical

Hypomethylated

Hypermethylated

Coat colour different due to different maternal diet i.e. environment!

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Methylation makes worker bees!

Cridge et al. 2015, Nutrients

These larvae are genetically identical

Hypomethylated

Hypermethylated

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TET proteins demethylate cytosines

https://doi.org/10.1038/nrg.2017.33

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Sensitivity of transcription factors to DNA methylation

https://doi.org/10.1042/EBC20190033

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166133/

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Methylation is cool

What do we usually want to know about it?

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Finding methylation differences can tell us a lot

Methylation is critical in determining cell type

Regulatory T-cell vs. Naïve T-cell

Methylation can be disrupted in disease

Cancer vs. Normal

Methylation is affected by the environment

Smokers vs. Non-smokers

Collect appropriate samples

Extract DNA and measure methylation

Statistical analysis

Normal

Cancer

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How do we measure methylation?

Bisulphite conversion

Create “SNPs”

Single nucleotide resolution

Array
Sequencing

Enrichment of methylated DNA

Restriction enzymes
Affinity

Regional resolution

Array
Sequencing

Ok, so now we know that methylation is cool and interesting and that we often want to know about differences between things,

But how do we actually measure it?

Well, there are two main types of approaches:

The first is using bisulfite conversion, which lets us create “SNPs” which can tell us if a CpG was methylated or not.

This technique provides us with single nucleotide resolution and we can use it with both arrays and sequencing.

The other approach is to do some sort of enrichment of methylated DNA.

This typically uses a restriction enzyme or some sort of affinity pulldown to enrich for the methylted DNA.

In contract to bisulfite conversion, this really only gives us a regional resolution, so we can only tell if chunks of DNA were methylated or not.

We can also combine this with both arrays and sequencing.

For the remainder of this talk, I’m only going to focus on bisulfite based approaches.

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What is bisulphite conversion?

Chemical process
Unmethylated Cs get converted to Ts
Methylated Cs are protected
Creates “SNP”

Used to call methylation

PCR

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What is bisulphite conversion?

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Methylation sequencing

AKA bisulphite sequencing: the good, the bad and the ugly

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Two main types of bisulphite sequencing

Whole-genome bisulphite sequencing (BS-seq)

Gold standard
Genome-wide (~30,000,000 CpGs in human)
Expensive but covers almost everything

Need high (10-30x) coverage to reliably call methylation

Targeted BS-seq

Only sequence regions of interest

Reduced representation BS-seq (restriction enzyme)
Capture BS-seq (similar principal to exome)

Cheaper but can miss a lot of stuff

Can usually do higher (20-60x) coverage

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WGBS

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What was bisulphite conversion again?

DNA fragment

OT – original top

OB – original bottom

CTOT – complementary to OT

CTOB – complementary to OB

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What was bisulphite conversion again?

DNA fragment

OT – original top

OB – original bottom

CTOT – complementary to OT

CTOB – complementary to OB

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What was bisulphite conversion again?

DNA fragment

All four of these can be sequenced!

OT – original top

OB – original bottom

CTOT – complementary to OT

CTOB – complementary to OB

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What are the challenges?

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What are the challenges?

Like calling SNPs, methylation in BS-seq inferred by comparison to unconverted reference sequence

Correct alignment is critical

More challenging than usual!

Aligned sequences do not exactly match reference
Complexity of libraries is reduced

Many Cs become Ts, so less info for mapping!

Methylation is not symmetrical

Two strands of DNA in the reference genome must be considered separately

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Mapping (Bismark)

DNA fragment

BS conversion & PCR

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Mapping (Bismark)

TCGGTATGTTTAAACGTT

DNA fragment

BS conversion & PCR

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Mapping (Bismark)

TCGGTATGTTTAAACGTT

TTGGTATGTTTAAATGTT

TCAATATATTTAAACATT

In silico read conversion

C-to-T

G-to-A

DNA fragment

BS conversion & PCR

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Mapping (Bismark)

TCGGTATGTTTAAACGTT

TTGGTATGTTTAAATGTT

TCAATATATTTAAACATT

In silico read conversion

C-to-T

G-to-A

…TTGGTATGTTTAAATGTT…

…AACCATACAAATTTACAA…

…CCAACATATTTAAACACT…

…GGTTGTATAAATTTGTGA…

Align to in silico bisulphite converted genome

Fwd strand C-to-T converted genome

Fwd strand G-to-A converted genome

Reverse complement

DNA fragment

BS conversion & PCR

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Mapping (Bismark)

TCAATATATTTAAACATT

TCGGTATGTTTAAACGTT

TTGGTATGTTTAAATGTT

TCAATATATTTAAACATT

In silico read conversion

C-to-T

G-to-A

…TTGGTATGTTTAAATGTT…

…AACCATACAAATTTACAA…

…CCAACATATTTAAACACT…

…GGTTGTATAAATTTGTGA…

Align to in silico bisulphite converted genome

Fwd strand C-to-T converted genome

Fwd strand G-to-A converted genome

Reverse complement

…TTGGTATGTTTAAATGTT…

…AACCATACAAATTTACAA…

…CCAACATATTTAAACACT…

…GGTTGTATAAATTTGTGA…

x

Read all alignment outputs simultaneously to determine if sequence can be mapped uniquely

DNA fragment

BS conversion & PCR

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Mapping (Bismark)

TCAATATATTTAAACATT

TCGGTATGTTTAAACGTT

TTGGTATGTTTAAATGTT

TCAATATATTTAAACATT

In silico read conversion

C-to-T

G-to-A

…TTGGTATGTTTAAATGTT…

…AACCATACAAATTTACAA…

…CCAACATATTTAAACACT…

…GGTTGTATAAATTTGTGA…

Align to in silico bisulphite converted genome

Fwd strand C-to-T converted genome

Fwd strand G-to-A converted genome

Reverse complement

…TTGGTATGTTTAAATGTT…

…AACCATACAAATTTACAA…

…CCAACATATTTAAACACT…

…GGTTGTATAAATTTGTGA…

x

TTGGTATGTTTAAATGTT

x

Read all alignment outputs simultaneously to determine if sequence can be mapped uniquely

DNA fragment

BS conversion & PCR

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Aligners performance

https://github.com/brentp/bwa-meth

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Calling methylation

TCGGTATGTTTAAATGTT

TATGTTTAAATGTT

…TCGGTATGTTTAAAT

…TCGGTATGTT

AAACGTT…

…TCGGTATGTTTAAATGTT

GTT…

…TTG

…CCGGCATGTTTAAACGCT…

…TCGGTATGTTT

…TCGGTATGT

TTAAATGTT…

ATGTT…

…TCGGTATGTTTAAAT

TT…

…TTGGTATGTTTA

ATGTT…

…TCGGTATGTTTAAACGT

Genome reference

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Calling methylation

TCGGTATGTTTAAATGTT

TATGTTTAAATGTT

…TCGGTATGTTTAAAT

GTT…

…TTG

…CCGGCATGTTTAAACGCT…

Genome reference

Good coverage is very important for reliable�methylation calls!

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https://pmc.ncbi.nlm.nih.gov/articles/PMC4729449/

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Some real BS-seq mapping results

https://software.broadinstitute.org/software/igv/interpreting_bisulfite_mode

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Methylation calling output

chr1 753479 753479 50 1 1

chr1 753492 753492 66.67 2 1

chr1 753540 753540 100 1 0

chr1 753541 753541 50 1 1

chr1 753667 753667 25 1 3

chr1 753724 753724 66.67 2 1

chr1 753763 753763 0 0 2

chr1 753785 753785 0 0 1

chr1 759932 759932 100 1 0

chr1 760913 760913 0 0 1

chr1 761299 761299 100 2 0

chr1 761371 761371 80 8 2

chr1 761377 761377 100 10 0

chr1 761446 761446 92.86 13 1

chr1 761460 761460 53.85 7 6

chr1 762005 762005 100 1 0

chr1 762114 762114 0 0 5

chr1 762176 762176 0 0 7

chr1 762180 762180 0 0 8

No. unmethylated reads

No. methylated reads

% methylation

Sum for total coverage

Position of C in genome

This is what we work with!

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RRBS

Similar to ribosomal RNA depletion approaches

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Reduced-representation bisulfite sequencing (RRBS-Seq)

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Analysis pipeline

Krueger et al. 2012, Nature Methods

Thorough QC is VERY important for BS-seq

Need to be brutal with trimming off poor quality bases…

…and adapters

As with SNP calling, removing PCR duplicates is a good idea for better methylation calling

Other stuff to find cool biology!

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Methylation arrays

What are they and how do they work?

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What are microarrays?

https://en.wikipedia.org/wiki/Transcriptomics_technologies

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Microarrays for gene expression

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Microarrays for gene expression

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Microarrays for gene expression

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Different types of chips

https://en.wikipedia.org/wiki/Transcriptomics_technologies

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SNP chips

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Illumina Infinium HumanMethylation BeadChips

Human only
Gene biased; selected to be relevant to human development & disease

eg. TSS, promoters, CpG islands, enhancers, ...

1 chip = 12 samples

>27,000 unique CpG sites measured in each sample

1 chip = 8 samples

1 chip = 12 samples

27k array (2009)

450k array (2011)

850k array (2015)

>450,000 unique CpG sites measured in each sample

>850,000 unique CpG sites measured in each sample

Modified slide from Belinda Phipson

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Methylation arrays are based on SNP array technology

Methylation array “SNPs” (C/T) are created by bisulphite conversion
Comparing the intensity of C/T gives the proportion of methylation at single CpG

What is this base?

Measure fluorescence

intensity

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What methylation values can �we get?

On an array, we measure methylation in a population of cells
Individual cell can be either 0, 0.5 or 1 at one CpG
Across a population we get a continuous measurement between [0-1]

CH₃

0

0.5

1

A sample

Many cells in single sample

Since were feading fluorescence intensities from the array, you may be wondering what values we can actually get for methylation?

Let’s just think about these 3 cell here that have a pair of chromosomes – one for mum and one from dad.

And we’re interested in methylation at a single CpG.

In this first cell, there is no methylation on either chromosome.

This cell in the middle here, is methylated at this CpG on one chromosome but not the other.

And finally, this third cell is methylated at the CpG on both chromosomes.

Can anyone tell me what methylation values they think we will get for each of these cells?

Yes, these cell will have methylation values of 0, 0.5 and 1.

However, on an array are not measuring single cells, we are measuring methylation in a population of cells – a whole smoosh of cells.

So, even though and individual cell can only be either 0, 0.5 or 1, across a population we can get any continuous meaure between 0 and 1.

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Measures of methylation

Arrays measure both methylated (C) and unmethylated (T) signal to get proportion of methylation at a CpG

Intuitive, easy to interpret, great for visualisation

M value

Beta value

Du et al. 2011, BMC Bioinformatics

Can convert between them via a logit transformation

Better statistical properties, recommended for statistical testing

Due to bisulfite conversion we measure both methylated C and unmethylated Ts to calculate the proportion of methylation at a CpG

This is called the beta value and is the proportion of the methylated signal over the total signal.

Looking at the density distribution of the CpGs measured on the array we can see that most are either unmethylated or methylated with a smattering of signal in between.

This is a very intuitive measure that is easy to interpret and is great for visualisation, but it’s not great for statistics.

For statistical analysis we tend to use M-values, which also have a bimodal distribution but aren’t constricted between 0 and 1, the distribution extends from –inf to +inf

M-values have been shown to have better statistical properties and are recommended for statistical testing.

These two measures are also related through a logit transformation, which is quite useful.

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What does the data look like?

Sample A1	Sample A2	Sample A3	Sample B1	Sample B2	Sample B3
0.213	0.221	0.311	0.123	0.216	0.198
-0.011	0.001	-0.016	2.011	2.002	2.702
2.213	2.256	2.698	0.052	0.101	0.238
4.567	5.231	4.982	4.152	6.216	4.698
-4.723	-3.459	-5.36	-5.763	-5.122	-4.998
-5.567	-4.666	-4.845	-4.522	-4.111	-3.245
3.421	5.467	5.554	5.445	5.298	4.514
2.981	3.345	3.512	-3.534	-4.311	-3.889
3.792	2.987	3.324	-0.231	-0.066	-0.001

… ... ...

CpG sites

Table of M-values

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Array analysis pipeline

QC: β density plots, control probes, MDS/clustering plots, …

Normalization: within and between arrays

Statistical testing for differential methylation, CpGs & regions

Annotation to genes, gene set testing, visualization, …

Combine with other data types

Transform data to remove unwanted variation

minfi, missMethyl, wateRmelon

Estimate means and variances and borrow information across probes

limma, bumphunter, DMRcate

Think about biological interpretation

missMethyl, Gviz

e.g. gene expression

GenomicRanges

Remove bad samples and poor performing probes (CpGs)

minfi, methylumi, limma

Software

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Epigenome-wide association studies (EWAS)

Similar to GWAS
Compare lots of cases to lots of controls

Often looking for small effects e.g. complex disease or environmental effects

Need lots of samples

100s or 1000s of cases & controls

https://en.wikipedia.org/wiki/Epigenome-wide_association_study_(EWAS)

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Summary

Methylation arrays very popular

Only for human
Great for EWAS
Analysis very mature

Bioconductor is the place to go!

BS-seq best option for genome-wide single nucleotide resolution

Only option for species other than human
Pre-processing, mapping, etc. pretty good
Statistical analysis still developing

Bioconductor is a valuable resource

Downstream analysis dependent on biological question

Methylation is interesting & we know how to measure it

Best technology for the job depends on what you want to know!

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Other detection methods

https://link.springer.com/article/10.1186/s12935-024-03405-2

MSP (methylation-specific PCR)
Pyrosequencing
MS-SSCA (methylation-specific single-strand conformation analysis)
MS-HRM (methylation-specific high-resolution melting)
SMART-MSP (sensitive melting analysis after real-time MSP)
MS-SNuPE (methylation-specific single nucleotide primer extension)
COLD-MS-PCR (co-amplification at lower denaturation temperature MS-PCR)
MS-FLAG (methylation-specific fluorescent amplicon generation)
HeavyMethyl
MB-MSP (MutS-based methylation-specific PCR)

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Analysis is the same but performance is better

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https://pmc.ncbi.nlm.nih.gov/articles/PMC7542392/

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oxBS-seq

https://pmc.ncbi.nlm.nih.gov/articles/PMC3919000/

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https://www.mdpi.com/2218-273X/11/7/1013

Single cell and multi omics

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Single cell methylation pipeline

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Single cell methylation pipeline

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Single cell methylation pipeline

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Low resolution methods

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Methylation database

https://ngdc.cncb.ac.cn/methbank/

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Acknowledgments

Jovana Maksimovic, PhD

Murdoch Childrens Research Institute

Alicia Oshlack
Belinda Phipson
MCRI Bioinformatics group!

Johns Hopkins University

Peter Hickey

https://f1000research.com/articles/5-1281/v3

jovana.maksimovic@mcri.edu.au

Based on SLIDES DOWNLOADED FROM:

https://f1000research.com/assets/download/1114351

missMethyl

https://www.bioconductor.org/packages/release/bioc/html/missMethyl.html