Enabling Epigenetics Research

CUT&RUN Spike-In Control
Catalog No. 53183(Version A1)

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ContentsOverview  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Kit Components and Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2Workflow  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  3CUT&Tag Spike-In Reaction Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4CUT&Tag Normalization Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7Technical Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  8

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Overview
CUT&RUN (Cleavage Under Targets & Release Using Nuclease) has emerged as a powerful
method for investigating the genome-wide distribution of various chromatin-associatedproteins and their modifications. However, the identification of differences between data
sets can be challenging when global modification changes occur. Additionally inaccuratequantification of starting material or technical variation during processing results in variation
across sample data. Currently available bioinformatic-based normalization methods are notapplicable in these instances, and the only reliable way to overcome bias and variation is toadd a known standard (Spike-In) into all samples. Active Motif offers Spike-In reagents for
ChIP-Seq and CUT&Tag, and has now introduced a similar approach for CUT&RUN.
Active Motif’s strategy for CUT&RUN normalization is to Spike-In cryopreserved Drosophilacell nuclei into samples prior to CUT&RUN. Then, during the antibody incubation step, aDrosophila H2Av antibody is added in addition to the antibody to the target of interest.
This Drosophila H2Av antibody provides a mechainsm to reliably tag Drosophila histones
in a consistent way across all samples. A normalization factor is then created based on theDrosophila signal and applied to the test genome. This CUT&RUN Spike-In strategy enables
normalization of CUT&RUN data independent of the experimental antibody and without bias.
The CUT&RUN Spike-In Control works with the ChIC/CUT&RUN Assay Kit (CatalogNo. 53180).
ProductFormat

Catalog No.

CUT&RUN Spike-In Control

24 rxns

53183

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Kit Components and Storage
All components are guaranteed for 6 months after receipt when stored properly.
Kit Component

Quantity

Storage

CUT&RUN Spike-In Antibody

24 µL

-20°C

CUT&RUN Spike-In Nuclei

6 x 440 µL-80°C

CUT&RUN Spike-in Nuclei are provided as six 440 µL vials of nuclei at a concentration of500 nuclei/µL.
We recommend using a ratio of 1 to 10 nuclei to experimental cells in CUT&RUN reactions.
Note:

If more than 1 vial of nuclei are used across an experiment, pool the neededvials of nuclei together to ensure consistency in the amount of nuclei added
per reaction for spike-in.

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Workflow

Cells

CUT&RUN
Spike-In Nuclei

Target-Specific
Antibody

CUT&RUN
Spike-In Antibody

CUT&RUN

Sequencing

Map to experimental
genome

Map to Drosophila
genomeNormalize Drosophila
tag counts across samples

Normalize experimental
sample tag counts
by same ratio

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CUT&RUN Spike-In Reaction Guidelines
Active Motif’s CUT&RUN normalization strategy1 may be applied to any mammalian CUT&RUN
assay reaction due to the lack of cross-reactivity of the Spike-in antibody with mammaliansamples. The amount of cryopreserved Drosophila nuclei and antibody used per CUT&RUN
reaction may need to be optimized with the goal of having Drosophila reads make up only
2-10% of the total sequencing reads. However, when using robust antibodies against tightly
localized histone modifications, such as H3K4me3, we recommend a spike-in:test sample
ratio of 1:20. For antibodies against spreading marks such as H3K27me3, we recommend aspike-in:test sample ratio of 1:10. For antibodies against transcription factors such as YY1, werecommend a spike-in:test sample ratio of 1:100.

Conditions

Human Cells

CUT&Tag-IT
Spike-In Nuclei

CUT&Tag-IT Spike-In
Nuclei/HumanCells Ratio%DM/Total

Abundant HistoneMark (H3K27me3)

500K

50K

1:10

5.98%

Medium Abundance
Histone Mark (H3K4me3)

500K

20K

1:20

4.57%

Rare Target, Transcription
Factor (YY1)

500K

5K

1:100

7.49%

To demonstrate the utility of this approach, histone modification level differences weremimicked by setting up CUT&RUN reactions with different amounts of starting cell numbers.
Various numbers of cryopreserved human K562 cells (500,000, 400,000, 300,000, 200,000,100,000, and 50,000) were combined with 20,000 (for H3K4me3) (Figure 1) or 10,000 (forYY1) (Figure 2) of cryopreserved Drosophila nuclei for each experiment.
H3K4me3 and YY1, were evaluated in the CUT&RUN spike-in assay, with biological duplicates
included in each experiment. Libraries were quantified and sequenced to a depth of 20-30
million reads per sample. However, sequencing to equal read depth for each sample masks
the differences in starting amounts for each sample (Figures 3 and 4). Therefore spike-in
normalization is required to reveal the differences in starting material2. For normalization,
the sample with the lowest number of Drosophila reads was used to generate normalization
factors across samples, which were then applied to down-sample the human read counts foreach sample accordingly. Read counts in CUT&RUN samples were adjusted using a scale
factor for peak calling and bigWig files were generated from the normalized read coverage tocompensate for sample-to-sample variability. The normalized results correlate with expected
results for the ratios of input material (Figures 5 and 6).

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Figure 1: K562 Starting Cell Numbers for CUT&RUN Targeting H3K4me3 with and without
Spike-In Normalization
20,000 CUT&RUN Spike-In Nuclei were added to 500,000, 400,000, 300,000, 200,000, 100,000
and 50,000 K562 cells and assayed in CUT&RUN. Results with and without normalization
are shown.

Figure 2: K562 Starting Cell Numbers for CUT&RUN targeting YY1 with and without
Spike-In Normalization
5,000 CUT&RUN Spike-In Nuclei were added to 500,000, 400,000, 300,000, 200,000, 100,000 and50,000 K562 cells and assayed in CUT&RUN. Results with and without normalization are shown.

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Fig. 3Fig. 4

Figure 3: K562 Paired Alignments with and without Normalization Targeting H3K4me3
The number of alignments is not reflective of the number of cells added to the experiment, thus
normalization is needed.
Figure 4: K562 Paired Alignments with and without Normalization Targeting YY1
The number of alignments is not reflective of the number of cells added to the experiment, thus
normalization is needed.
Fig. 5

Fig. 6

Figure 5: K562 Paired Alignments After Normalization to Drosophila Paired Alignments
K562 paired alignments were normalized to the Drosophila alignment number from 500,000 K562
cells. Now the expected ratios based on amount of starting cells has been restored in H3K4me3
Figure 6: K562 Paired Alignments After Normalization to Drosophila Paired Alignments
K562 paired alignments were normalized to the Drosophila alignment number from 500,000 K562
cells. Now the expected ratios based on amount of starting cells has been restored in YY1

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CUT&RUN Normalization Guidelines
1.

Perform CUT&RUN by combining the CUT&RUN Spike-In Nuclei with the experimental
sample cells to be used in CUT&RUN. Perform CUT&RUN as per the instructions
provide in Active Motif’s ChIC/CUT&RUN Assay Kit (Catalog No. 53180) and add 1 µLof CUT&RUN Spike-In Antibody, in combination with your test antibody, during theprimary antibody incubation step. Use the guidelines provided for nuclei and antibody
quantities based on the antibody target.

2.

Sequence the CUT&RUN libraries using an NGS platform.

3.

Map the CUT&RUN data to the test reference genome (e.g. human, mouse, or other).

4.

Map CUT&RUN data to the Drosophila reference genome.

5.

Count uniquely aligning Drosophila sequence tags and identify the sample containing
the least number of tags.

6.

Divide the aligned Drosophila tag value from the sample with the lowest Drosophila
tag count by the Drosophila tag count value from all other samples to and generatea normalization factor for each sample. (Sample 1 with lowest tag count / Sample 2)= Normalization factor. The sample with the lowest drosophila tag count will have anormalization factor of 1.

7.

Generate the normalization factors for all samples using the strategy from step 6.

8.

Use the normalization factors to down-sample the read counts for each sample.

9.

After obtaining normalized human read counts, use a standard CUT&RUN pipeline
starting with the downsampled tag counts for each sample for peak calling andgeneration of bigWigs.

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References
1.

Egan, B. et al. (2016) PLoS ONE. 11(11): e0166438.

2.

Taruttis et al. (2017) Biotechniques 62:53-61

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