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aChIP is an efficient and sensitive ChIP-seq technique for economically important plant organs

Nature Plants volume 10, pages 1317–1329 (2024)Cite this article

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Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is crucial for profiling histone modifications and transcription factor binding throughout the genome. However, its application in economically important plant organs (EIPOs) such as seeds, fruits and flowers is challenging due to their sturdy cell walls and complex constituents. Here we present advanced ChIP (aChIP), an optimized method that efficiently isolates chromatin from plant tissues while simultaneously removing cell walls and cellular constituents. aChIP precisely profiles histone modifications in all 14 tested EIPOs and identifies transcription factor and chromatin-modifying enzyme binding sites. In addition, aChIP enhances ChIP efficiency, revealing numerous novel modified sites compared with previous methods in vegetative tissues. aChIP reveals the histone modification landscape for rapeseed dry seeds, highlighting the intricate roles of chromatin dynamics during seed dormancy and germination. Altogether, aChIP is a powerful, efficient and sensitive approach for comprehensive chromatin profiling in virtually all plant tissues, especially in EIPOs.

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Fig. 1: aChIP for histone modification profiling in maturing rapeseed seeds.
Fig. 2: Universal application of aChIP across diverse EIPOs.
Fig. 3: aChIP for transcription factors and chromatin enzymes.
Fig. 4: Efficiency and sensitivity of aChIP in plant vegetative tissues.
Fig. 5: Dynamics of histone modifications and gene expression in 50 DPA seeds, dry seeds, germinated seeds and young leaves of rapeseed.

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Data availability

All sequence data generated in this study have been deposited in NCBI GEO under the accession number GSE243806. All accessions of published ChIP-seq and RNA-seq data used in this study are provided in Supplementary Data 3 and 9. All data supporting the findings of this study are available within the manuscript and its supporting information or are available from the corresponding author upon request.

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Acknowledgements

We thank Dijun Chen (Nanjing University) for providing ChIP-Hub data; Fei Zhang, Ning Yang, Jihua Ding, and Xiaopeng Fu (Huazhong Agricultural University) for providing some EIPOs used in this study; and the bioinformatics computing platform at National Key Laboratory of Crop Genetic Improvement in Huazhong Agricultural University. Funding: this work was supported by the National Key Research and Development Program of China (2021YFF1000100 to L.Z.), the National Natural Science Foundation of China (32222063 and 32370636 to L.Z., 32200471 to Q.Z., 31930032 to J.S. and 32188102 to J.-K.Z.), the China Postdoctoral Science Foundation (2022M721281 to Q.Z.), the National Key Research and Development Program of China (2021YFA1300404 to J.-K.Z.), the Fundamental Research Funds for the Central Universities (2662023PY004 to L.Z.), the First Class Discipline Construction Funds of College of Plant Science and Technology of Huazhong Agricultural University (2022ZKPY004 to L.Z.) and the National Key Laboratory of Crop Genetic Improvement Research Program (ZW22B0101 and ZW22B0204).

Author information

Author notes

  1. These authors contributed equally: Qing Zhang, Wenying Zhong.

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement, National Engineering Research Center of Rapeseed, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China

    Qing Zhang, Wenying Zhong, Guangfeng Zhu, Lulu Cheng, Caijun Yin, Li Deng, Yang Yang, Jinxiong Shen, Tingdong Fu & Lun Zhao

  2. Shanghai Center for Plant Stress Biology, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China

    Zhengjing Zhang

  3. Institute of Advanced Biotechnology and School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Jian-Kang Zhu

  4. Center for Advanced Bioindustry Technologies, Chinese Academy of Agricultural Sciences, Beijing, China

    Jian-Kang Zhu

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  1. Qing Zhang
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Contributions

Conception and design: L.Z. and Q.Z.; data generation: Q.Z. with the assistance of G.Z., L.C., Z.Z. and C.Y.; data analysis: W.Z., Q.Z., YY. and L.D.; data interpretation and paper writing: Q.Z. and L.Z. with the assistance of W.Z., J.S., T.F. and J.-K.Z.

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Correspondence to Lun Zhao.

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Nature Plants thanks Bo Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evaluation of the published ChIP-seq data in plants.

a, Pie charts showing the distribution of plant ChIP-seq datasets according to tissue specificity. EIPOs, economically important plant organs. b, Bar chart showing the quality of ChIP-seq data based on FRiP (fraction of reads in peaks). The published data were collected by ChIP-Hub, while aChIP data were generated in this study. c, Genome browser screenshots showing the published ChIP-seq data for histone modifications in cantaloupe and transcription factor ONAC127 in rice.

Extended Data Fig. 2 Schematic diagrams for eChIP method, along with quality and efficiency analysis for aChIP data in rapeseed seeds.

a, Schematic overview of eChIP. The eChIP method starts by fixing tissue with formaldehyde, followed by grinding tissues into fine powder, and subsequent homogenate lysis using buffer S. Then, the chromatin is fragmented and retained in the supernatant with buffer F, followed with IP (immunoprecipitation) with antibodies, ChIP-DNA purification and library sequencing. b, c, Observation and quantification of differences in cell nucleus count before the sonication step in the aChIP and rChIP methods. Cell nuclei were stained with DAPI. Each value represents the mean ± standard error of mean (n = 3 biological replicates). d, Comparison of signal-to-noise ratio based on the FRiP for H3K4me3 and H3K27me3 ChIP-seq datasets in 50 DPA (days post anthesis) rapeseed seeds. The dashed line indicated an FRiP of 30%. e, Pearson correlation for two replicates of H3K4me3 aChIP. Each point represents the log2 of mapped reads within the combined peaks of the two replicates. The R value calculated by Pearson correlation coefficient at the combined peaks is shown. f, Correlations of log2-fold-change in H3K4me3 and H3K27me3 (x-axis) and gene expression (y-axis) between 50 DPA seeds and young leaves. g, Genome browser screenshot showing H3K27me3 and H3K4me3 aChIP data for 50 DPA rapeseed seeds.

Extended Data Fig. 3 Comparison of aChIP and published data in tomato fruits.

a, Genome browser screenshots showing H3K4me3 and H3K27me3 landscape profiled by aChIP and published data (Lü et al., 2018) in tomato fruits. b, Comparison of signal-to-noise ratio based on FRiP for H3K4me3 and H3K27me3 ChIP-seq datasets between aChIP and the published data in tomato fruits. c, Comparison of FRiP values for H3K4me3 and H3K27me3 ChIP-seq datasets under different sequencing depths among aChIP and published data in tomato fruits, the “M” on the x-axis represents the million of reads. d, GO enrichment analysis of aChIP unique H3K4me3 or H3K27me3-related genes in the tomato fruits.

Extended Data Fig. 4 Comparison of aChIP and published data in potato tubers.

a, Genome browser screenshots showing H3K4me3 and H3K27me3 landscape profiled by aChIP and published data (Zeng et al., 2019) in potato tubers. b, Comparison of signal-to-noise ratio based on FRiP for H3K4me3 and H3K27me3 ChIP-seq datasets between aChIP and the published data in potato tubers. c, Comparison of H3K4me3 and H3K27me3 peak intensities between aChIP and published data in potato tubers. Flanking regions are 2 kb upstream and downstream of the peak regions.

Extended Data Fig. 5 aChIP data quality analysis for transcription factors and chromatin enzymes.

a, Pearson correlation for two replicates of aChIP for RNAPII from 50 DPA rapeseed seeds, CAMTA3-FLAG, MBD7-MYC and DME-FLAG from Arabidopsis seedlings. Each point represents the log2 of mapped reads within the combined peaks of the two replicates. The R value calculated by Pearson correlation coefficient at the combined peaks is shown. b, Motif prediction of the indicated CAMTA3-binding regions based on aChIP data. The identified motif was found in 15.2% of the target sequences, compared to 6.5% in the background sequences. The top 10 predicted motifs were shown. c, Bubble chart showing the top 7 enriched GO terms for genes associated with common CAMTA3-binding peaks and aChIP unique peaks, in comparison to rChIP data. The x-axis represents the ratio of the number of genes in the candidate set enriched in this GO term to the total number of genes in the background set involved in the same GO term. The y-axis lists the GO terms. Each bubble corresponds to a specific GO term. The color of each bubble signifies its statistical significance (p.adjust), calculated using the Benjamini & Hochberg method. The size of each bubble is proportional to the number of genes it includes, with larger bubbles indicating a higher gene count. d, Peak profiles displaying the distribution of CAMTA3-binding signals across aChIP unique peaks (± 2 kb) in the indicated categories. Wild-type (Col-0) FLAG ChIP-seq served as control. The peak region is converted into percentiles to standardize peaks of different lengths. e, Venn diagrams showing the overlap of MBD7-MYC peaks identified by aChIP and published data (Lang et al., 2015). f, Peak profiles displaying the distribution of MBD7-binding signals across aChIP unique peaks (± 2 kb) in the indicated categories. Wild-type (Col-0) MYC ChIP-seq served as control. The peak region is converted into percentiles to standardize peaks of different lengths. g, Barplots showing the genome distribution and proportion of DME binding regions.

Extended Data Fig. 6 Comparison of H3K4me3 differences between aChIP and eChIP in rapeseed young leaves and rice seedlings.

a, Pearson correlation for two replicates of H3K4me3 aChIP in rapeseed young leaves and rice seedlings. Each point represents the log2 of mapped reads within the combined peaks of the two replicates. The R value calculated by Pearson correlation coefficient at the combined peaks is shown. b, Comparison of signal-to-noise ratio based on the FRiP for H3K4me3 ChIP-seq datasets in rapeseed young leaves and rice seedlings generated by the indicated methods. c, Comparison of H3K4me3 ChIP-DNA content between aChIP and eChIP methods which maintained uniformity in materials and antibody. Each group contained three replicates, labeled with points in the bar. The value represents the mean ± standard error of mean. d, e, Heatmap of aChIP unique peak signals and expression levels of peak-related genes. The scale regions are 2 kb upstream of the TSS and 2 kb downstream of the TES. f, Bubble chart showing the top 10 enriched GO terms for genes marked by aChIP H3K4me3 unique peaks which comparison with eChIP in rapeseed young leaves and rice seedling, respectively.

Extended Data Fig. 7 Comparative analysis of aChIP and CUT&Tag data.

a, Comparison of signal-to-noise ratio based on the FRiP for H3K4me3 ChIP-seq datasets in rapeseed young leaves and rice seedlings generated by aChIP and CUT&Tag. b, Venn diagram showing the overlap of H3K4me3 peaks between aChIP and CUT&Tag in rapeseed young leaves and rice seedlings. c, d, Genome browser screenshots showing H3K4me3 landscape profiled by aChIP and CUT&Tag in rapeseed young leaves and rice seedlings. NarrowPeak file generated from MACS2 is used to display the called peaks. The blue bars indicate statistically significant peaks (q-value < 1e-5). Two categories, aChIP unique peaks and overlapped peak, are shown. The light green shadows show aChIP identifies more histone modification sites compared to CUT&Tag. e, f, Peak intensities of H3K4me3 for the indicated categories. Profiles displaying the distribution of H3K4me3 signals across the body (± 2 kb) of protein‑coding genes marked by the indicated categories. g, Bubble chart showing the top 10 enriched GO terms for genes marked by aChIP H3K4me3 unique peaks which comparison with CUT&Tag in rapeseed young leaves and rice seedling, respectively.

Extended Data Fig. 8 Quality analysis of aChIP data for dryd seeds of rapeseed and Arabidopsis.

a, e, Physical image of rapeseed and Arabidopsis dry seeds stored in a drying cabinet for over a year. b, f, Genome browser screenshot showing H3K4me3 and H3K27me3 aChIP data in dry rapeseed seeds and Arabidopsis dry seeds. c, g, Barplot displaying the FRiP values of H3K4me3 and H3K27me3 in dry rapeseed seeds and Arabidopsis dry seeds. The dashed line indicated an FRiP of 30%. d, h, Pearson correlation for two replicates of H3K4me3 and H3K27me3 aChIP in dry rapeseed seeds and Arabidopsis dry seeds. Each point represents the log2 of mapped reads within the combined peaks of the two replicates. The R value calculated by Pearson correlation coefficient at the combined peaks is shown. The venn diagrams showed the percentage and number of the overlapping peaks between the two biological replicates.

Extended Data Fig. 9

Representative genome browser screenshots and signal-to-noise ratios (FRiP) for five representative histone modifications in the indicated rapeseed tissues.

Extended Data Fig. 10 Comparison of histone modification differences between the indicated tissues.

a, Profiles displaying the distribution of histone modification signals across the gene body (± 2 kb) of related protein-coding genes and H3K9me2 signals across the peak regeion (± 2 kb). The gene body or peak regeion is converted into percentiles to standardize genes or peaks of different lengths. ***p < 2.22e − 16 from two-tailed Student’s t-test. b, Correlations of log2-fold-change in H3K4me3 (x-axis) and gene expression (y-axis) between dry seeds and germinated seeds (left panel) and between young leaves and 50 DPA seeds (right panel). Representative examples are shown. c, Dynamics of H3K4me3 and transcription between dry seeds and germinated seeds. Heatmaps of differential H3K4me3 signals and the expression levels of the corresponding genes are shown. Boxes show median values and the interquartile range of gene expression. Whiskers show minimum and maximum values, excluding outliers. **p < 0.01 and ***p < 1e-15 from two-tailed Wilcoxon test. (PDry vs Germinated H3K4me3 down < 2.22e − 16; PDry vs Germinated H3K4me3 non-significant < 2.22e − 16).

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Supplementary Data 1

The comparison of chromatin extraction efficiency in different steps of rChIP, eChIP and aChIP methods.

Supplementary Data 2

Statistics for the generated datasets.

Supplementary Data 3

Preliminary quality analysis of aChIP-seq and published ChIP-seq datasets.

Supplementary Data 4

The GO enrichment results for CMATA3-marked genes.

Supplementary Data 5

The GO enrichment results for CAMTA3 common peaks (aChIP versus published) marked genes.

Supplementary Data 6

The GO enrichment results for CAMTA3 aChIP unique peaks (aChIP versus published) marked genes.

Supplementary Data 7

The GO enrichment results for genes that changed from the bivalent states in dry seeds to H3K4me3 in germinated seeds.

Supplementary Data 8

The reference genome sequences and gene annotations of different plants used in this study.

Supplementary Data 9

Preliminary quality analysis of RNA-seq data.

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Zhang, Q., Zhong, W., Zhu, G. et al. aChIP is an efficient and sensitive ChIP-seq technique for economically important plant organs. Nat. Plants 10, 1317–1329 (2024). https://doi.org/10.1038/s41477-024-01743-7

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