ChIP-seq case studies

EMBNET course

Bioinformatics of transcriptional regulation

Jan 31 2008

Christoph Schmid

Applications of ChIP-seq

• precise mapping of DNA-binding proteins /complexes

epigenetic code (histone modifications)

nucleosomes

transcription factor binding sites

3C (chromatin conformation capture)

Antibodies used in Barski et al. and numbers of tags sequenced

> 20 Million sequence tags per run

192 Millionsof tags mapped

=> 8Gb of processed results!

RNA Plymerase II

Insulator binding multi-zinc finger

ChIP-seq to determine epigenetic modifications

Figure 4 in

High-resolution profiling of histone

methylations in the human genome.

Barski A, Cuddapah S, Cui K, Roh TY,

Schones DE, Wang Z, Wei G, Chepelev I,

Zhao K: Cell 2007, 129:823-837.

© Elsevier Inc. with permissions

Reproducibility of ChIP-seq

GMAT: Genome-wide MApping Technique, a combination of chromatin immunoprecipitationand SAGE technique

frequency of tag sequence

(?)

0.3Mb

chromatin from human resting CD4+ T cells

Data analysis

• ~12’000 genes from genome annotations

• Ranking of genes based on microarray expression data from GNF -> pools of 1’000 genes

error rate of mapped sequence tags: ~2%

Normalized Counts: number of tags per base pair in 5bp windows

Epigenetic code at

TSS (Fig. 2)

Summary of ‘signatures’ (Fig. 5EF)

Prediction of transcription units? (Fig. 7)

Summary

• Novel method of large-scale sequencing to catalogue epigenetic modifications

• Histone modifications correlate with– expression levels

– loci of transcription start sites and enhancers

– chromosomal compaction and breakpoints, sequence repeats

• Specific ‘signatures’ as markers of functional loci

Conclusions

• So far only correlations, no causal relation!

• there is evidence that– transcriptional activity modifies epigenetic code

– Interactions among various methylases and demethylases

• Multi-million $$$ question:

=> Regulation of epigenetic modifications?

Cell differentiation and epigenetics

Genome-wide maps of chromatin state in pluripotent and lineage-committed cells.

Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, KocheRP, Lee W, Mendenhall E, O'Donovan A, Presser A, Russ C, Xie X, Meissner A, Wernig M, Jaenisch R, Nusbaum C, Lander ES and Bernstein BE.

Nature, 448, 553-560. (2007)

Experimental setup

Antibodies used in ChIP:

• K4 H3K4me3 (Abcam 8580)

• K9 H3K9me3 (Abcam 8898)

• K27 H3K27me3 (Upstate 07-449)

• K36 H3K36me3 (Abcam 9050)

• K20 H4K20me3 (Upstate 07-463)

• H3 pan-H3 (Abcam 1791)

• Rpol RNA polymerase II (Covance MMS-126R)

• WCE unenriched whole-cell extract

NPES(Hyb)

MEF

Embryonic Stem cellsHybrid ES cells

Neural Progenitor cellsMouse Embryonic Fibroblasts

Histone modifications at pecific loci

ChIP-Seq Data Reanalyzed

ChIP-Seq Data Reveal Nucleosome Architecture of Human Promoters. Schmid CD and Bucher P.

Cell, 131, 831-832. (2007)

Orientation maintained in ChIP-seq

Genomic seq

(+) (-)

Distances of mappings (+)- vs (-)-tags

Distances of tags in opposite orientation Distances of tags in opposite orientation

Distances of tags in opposite orientationPolII

Distances of tags in opposite orientationH3K4me3

CTCF H2AZ

abso

lute

cou

nts

abso

lute

cou

nts

abso

lute

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abso

lute

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nts

Striking periodicities in ChIP-seq data

Barski et al., Cell 2007, 129:823-837. Figure 2 © Elsevier Inc. with permissions

Orientation-dependent analysis

• set of 4290 highly expressed genes in CD4+ cells (based on microarray expression data from GNF)

• average number of ChIP-seq tags per genomic position

+

-

+

+

Resolution of nucleosomes

−600 −400 −200 0 200 400 600

0.00

0.04

0.08

0.12

Ab aginst PolII

position rel. to TSSaver

age

Ch

IP−s

eq c

ou

nts

(b

in s

ize

15 b

p)

raw_data5’end3’end

−600 −400 −200 0 200 400 600

0.1

0.2

0.3

0.4

0.5

Ab aginst H3K4me3

position rel. to TSS

raw_data5’end3’end

Transcriptional direction

Average of set of 4290 highly expressed genes in CD4+ cellsSchmid and Bucher, Cell (2007) 131: 831-832. © Elsevier Inc.

Nucleosomes in S. cerevisiae

Translational and rotational settings of H2A.Z nucleosomes across the Saccharomycescerevisiae genome.Albert I, Mavrich TN, Tomsho LP, Qi J, Zanton SJ, Schuster SC and Pugh BF.

Nature, 446, 572-576. (2007)

454 sequencing of H2A.Z attached DNA

Alberts et al., suppl. Methods:“Each read was replaced by a probability function that a nucleosome is located withina certain distance of the actual read coordinate.”

Divergence human vs. yeast?

−600 −400 −200 0 200 400 600

0.01

0.03

0.05

Ab aginst H2AZ

position rel. to TSS

raw_data5’end3’end

H2AZ nucleosome centers at:

+110; +300; +450,... +60; +260,...

Albert et al.

(2007)

Nature 446:

572-576.© Nature

PublishingGroup

Ave

rag

e C

hIP

-seq

cou

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(b

in s

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15b

p)

ChIP-seq with transcription factors

Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A, Thiessen N, Griffith OL, He A, Marra M, Snyder M and Jones S.

Nat Methods, 4, 651-657. (2007)

Mapping binding sites of STAT1

stim unstimuniquely mapped sequence reads: 15.1 12.9 (millions)

putative STAT1-binding regions: 41,582 11,004

Characteristic fragment length

0 200 400 600 800 1000

050

000

1000

0015

0000

2000

00

distances of tags in opposite orientation

distances in bp

abso

lute

cou

nts

HeLa cells■ IFNγ stim□ unstim

Reanalysis of data might improve resolution(Robertson et al. 2007)

−500 −400 −300 −200 −100 0 100 200 300 400 500

0.0

0.5

1.0

1.5

2.0

Ab aginst STAT1

position rel. to centers of 37 STAT1 binding sites derived from the literature

aver

age

ChIP

−seq

cou

nts

(bin

siz

e 25

bp)

centered5’ end3’ end