{ "id": "ep218w3b50", "slug": "single-cell-chip-seq", "title": "Single-cell ChIP-seq", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology" }, { "id": "life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna" }, { "id": "life.biology.rna.transcription-and-gene-regulation", "label": "Transcription and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.transcription-and-gene-regulation" }, { "id": "life.biology.rna.chromatin-gene-regulation", "label": "Chromatin-linked gene regulation", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.rna.chromatin-gene-regulation" } ], "geo": [ { "id": "geo.nongeo.t2001.life.biology.rna", "label": "Non-geographic · 2001 to 2020: RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life" }, { "id": "geo.nongeo.t2001.life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology" }, { "id": "geo.nongeo.t2001.life.biology.rna", "label": "RNA and gene regulation", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.rna" } ] } ], "excerpt": "Single-cell ChIP-seq is a family of genomics methods that profiles histone modifications or transcription factor binding in individual cells, first reported as Drop-ChIP in 2015.", "snippet": "Single-cell ChIP-seq is a family of genomics methods that profiles histone modifications or transcription factor binding in individual cells, first reported as Drop-ChIP in 2015.", "node": "life.biology.rna.chromatin-gene-regulation", "markdown": "# Single-cell ChIP-seq\n\nSingle-cell ChIP-seq is a family of genomics methods that profiles histone modifications or transcription factor binding across the genome in individual cells, by coupling chromatin immunoprecipitation or antibody-guided chromatin cleavage to single-cell [DNA barcoding](https://www.edgepedia.org/dna-barcoding) and sequencing. Bulk ChIP-seq requires \\( 10^{5} \\) to \\( 10^{7} \\) cells because of material loss during immunoprecipitation.[1](https://link.springer.com/article/10.1186/s43556-020-00009-w) Single-cell versions answer whether a population contains subpopulations with distinct chromatin states, for example ES cell subpopulations defined by differentiation priming[2](https://doi.org/10.1038/nbt.3383) or cells in drug-sensitive tumors sharing a chromatin signature with resistant cells.[3](https://doi.org/10.1038/s41588-019-0424-9)\n\n| Key fact | Value |\n|---|---|\n| What one cell yields | On the order of 1,000 unique reads in early Drop-ChIP, capturing roughly 1,000 marked promoters or enhancers per cell[2](https://doi.org/10.1038/nbt.3383) |\n| Cell throughput, droplet scChIP-seq | About 2,000 to 4,000 cells per experiment at an average of 4,000 reads per cell[4](https://genome.cshlp.org/content/31/10/1831) |\n| Cell throughput, iscChIC-seq | More than 10,000 single cells per histone modification, with 11,000 (H3K4me3) to 45,000 (H3K27me3) nonredundant reads per cell[4](https://genome.cshlp.org/content/31/10/1831) |\n| Cell throughput, sciCUT&Tag | About 40,000 cells per chip at roughly 0.11 USD per cell, versus about 0.85 USD per cell for standard droplet kits[5](https://doi.org/10.1038/s41596-023-00905-9) |\n| Agreement with bulk ChIP-seq | Pooled scChIC-seq reads correlate with bulk at \\( r = 0.77 \\) (H3K4me3) and \\( r = 0.67 \\) (H3K27me3)[6](https://doi.org/10.1038/s41592-019-0361-7) |\n| Main limitation | Sparsity: about 1,000 unique reads per cell means thousands of cells are needed for reliable clustering[1](https://link.springer.com/article/10.1186/s43556-020-00009-w) • [7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0270043) |\n\n## How it works\n\nThree molecular routes convert antibody recognition of a chromatin mark into barcoded, sequenceable DNA from one cell.\n\n**Droplet ChIP with barcoding.** Drop-ChIP combines a microfluidic device with single-cell DNA barcoding: each cell is encapsulated in a droplet with lysate and micrococcal nuclease (MNase), nucleosomes are barcoded within droplets, barcoded nucleosomes from all droplets are pooled for immunoprecipitation, and libraries are constructed and sequenced.[1](https://link.springer.com/article/10.1186/s43556-020-00009-w)\n\n**Antibody-guided MNase cleavage.** In scChIC-seq, MNase is tethered to an antibody against a histone mark, either by direct covalent conjugation (Ab-MNase) or through protein A-antibody interaction (Ab+PA-MNase). The enzyme cleaves DNA at marked sites inside the nucleus, and target fragments are selectively amplified because they are shorter than non-target DNA.[6](https://doi.org/10.1038/s41592-019-0361-7)\n\n**Antibody-directed Tn5 tagmentation.** In CUT&Tag-based methods, an antibody directs a Tn5 transposase fusion to the marked site; the Tn5 remains bound to DNA after magnesium-catalyzed tagmentation, and fragments are retained within intact cells, which makes the chemistry compatible with single-cell barcoding.[5](https://doi.org/10.1038/s41596-023-00905-9) A related tagmentation route, itChIP-seq, combines chromatin opening, simultaneous cellular indexing, and chromatin tagmentation in a single tube.[8](https://doi.org/10.1038/s41556-019-0383-5)\n\n## How it is done\n\n**scChIC-seq.** Fixed cells are pre-treated with RIPA buffer containing 0.2% SDS to de-condense chromatin, Ab-MNase conjugates are added and allowed to bind, unbound conjugates are washed away, and MNase is activated by calcium addition; standard library preparation and sequencing follow.[6](https://doi.org/10.1038/s41592-019-0361-7)\n\n**iscChIC-seq.** After antibody-guided MNase digestion of cells cross-linked with formaldehyde and disuccinimidyl glutarate (DSG), terminal transferase (TdT) adds several dG nucleotides to DNA ends, and T4 DNA ligase ligates oligo-dC barcode adaptors in a 96-well plate. Samples are pooled, 30 cells per well are flow-sorted into new 96-well plates, and two rounds of PCR complete the libraries.[4](https://genome.cshlp.org/content/31/10/1831)\n\n**sciCUT&Tag.** Lightly cross-linked nuclei are bound to magnetic beads and incubated with primary and secondary antibodies in bulk, then arrayed in a 96-well plate for a first round of cellular indexing by antibody-directed Tn5 tagmentation. The sample is repooled and arrayed across 5,184 nanowells at 12 to 24 nuclei per well for a second indexing round during PCR. The protocol takes 1.5 days and uses SNPs to remove cell collisions.[5](https://doi.org/10.1038/s41596-023-00905-9)\n\n## Origin\n\nThe antibody-guided cleavage principle dates to ChIC, reported by [Manfred Schmid](https://www.edgepedia.org/manfred-schmid), Thérèse Durussel, and [Ulrich K. Laemmli](https://www.edgepedia.org/ulrich-k-laemmli) in Molecular Cell in 2004.[9](https://doi.org/10.1016/j.molcel.2004.09.007) Microfluidic ChIP from as few as 2,000 cells, described by Angela R. Wu and Stephen R. Quake, was a low-input precursor.[10](https://doi.org/10.1101/pdb.prot084996) Combinatorial cellular indexing, in which nuclei are tagged with barcoded transposase complexes in 96 wells, then pooled, diluted, and redistributed at 15 to 25 nuclei per well by a cell sorter, was reported by Darren A. Cusanovich and colleagues in Science in 2015 and later reused by sciCUT&Tag.[11](https://doi.org/10.1126/science.aab1601)\n\nSingle-cell ChIP-seq itself was reported by Assaf Rotem, Oren Ram, Noam Shoresh, and colleagues in [Nature Biotechnology](https://www.edgepedia.org/nature-biotechnology) in 2015 as Drop-ChIP.[2](https://doi.org/10.1038/nbt.3383) A 2019 wave followed: droplet high-throughput scChIP-seq from Kevin Grosselin and colleagues in [Nature Genetics](https://www.edgepedia.org/nature-genetics)[3](https://doi.org/10.1038/s41588-019-0424-9); itChIP-seq from Shanshan Ai and colleagues in Nature Cell Biology[8](https://doi.org/10.1038/s41556-019-0383-5); scChIC-seq from Wai Lim Ku and colleagues in [Nature Methods](https://www.edgepedia.org/nature-methods)[6](https://doi.org/10.1038/s41592-019-0361-7); and CoBATCH from Qianhao Wang and colleagues in Molecular Cell.[12](https://doi.org/10.1016/j.molcel.2019.07.015) Later variants include iscChIC-seq[13](https://doi.org/10.1101/gr.260893.120), scCUT&Tag[14](https://doi.org/10.1038/s41587-021-00865-z), the sciCUT&Tag protocol[5](https://doi.org/10.1038/s41596-023-00905-9), MAbID[15](https://doi.org/10.1038/s41592-023-02090-9), TACIT[16](https://doi.org/10.1038/s41586-025-08656-1), and IT-scC&T-seq.[17](https://doi.org/10.1186/s13059-025-03661-z)\n\n## Variants\n\nThroughput and depth trade off across the family. Droplet scChIP-seq detects about 2,000 to 4,000 cells per experiment at an average of 4,000 reads per cell, with a comparatively complicated workflow.[4](https://genome.cshlp.org/content/31/10/1831) Single-cell itChIP-seq yields about 9,000 unique reads per cell.[8](https://doi.org/10.1038/s41556-019-0383-5) iscChIC-seq analyzes more than 10,000 single cells per histone modification with 11,000 to 45,000 nonredundant reads per cell.[4](https://genome.cshlp.org/content/31/10/1831) sciCUT&Tag reaches about 40,000 cells per chip, at least a four-fold throughput increase over other single-cell CUT&Tag approaches.[5](https://doi.org/10.1038/s41596-023-00905-9) IT-scC&T-seq generates libraries for over 10,000 cells at less than 0.01 USD per cell in 1 to 2 days, using only standard laboratory equipment.[17](https://doi.org/10.1186/s13059-025-03661-z) TACIT reached up to half a million non-duplicated reads per cell for H3K4me1 at the two-cell stage, a 41-fold increase over prior approaches.[16](https://doi.org/10.1038/s41586-025-08656-1)\n\n## Applications\n\nIn published comparisons, pooled scChIC-seq reads for H3K4me3 correlated with bulk ChIP-seq at \\( r = 0.77 \\), and for [H3K27me3](https://www.edgepedia.org/h3k27me3) at \\( r = 0.67 \\).[6](https://doi.org/10.1038/s41592-019-0361-7)\n\nApplications track the heterogeneity question. Drop-ChIP deconvoluted a mixture of ES cells, fibroblasts, and hematopoietic progenitors into cell-type chromatin maps, and assaying thousands of ES cells revealed subpopulations defined by pluripotency and differentiation-priming signatures.[2](https://doi.org/10.1038/nbt.3383) In breast cancer patient-derived xenografts, high-throughput scChIP-seq found that a subset of cells in untreated drug-sensitive tumors shared a chromatin signature with resistant cells, undetectable by bulk approaches, and that these cells had lost the repressive H3K27me3 mark at resistance-promoting genes.[3](https://doi.org/10.1038/s41588-019-0424-9) iscChIC-seq clustering resolved monocytes, T cells, B cells, and NK cells from white blood cells.[4](https://genome.cshlp.org/content/31/10/1831) sc-itChIP-seq of H3K27ac captured the earliest epigenetic priming in the naive-to-primed pluripotency transition and cell-type-specific enhancer usage in cardiac progenitor differentiation.[8](https://doi.org/10.1038/s41556-019-0383-5) scCUT&Tag of H3K27me3 distinguished blood cell types, generated cell-type-specific Polycomb landscapes from heterogeneous tissues, and profiled a brain tumor before and after treatment.[14](https://doi.org/10.1038/s41587-021-00865-z) TACIT produced genome-wide histone maps from zygote to blastocyst for embryo lineage tracing.[16](https://doi.org/10.1038/s41586-025-08656-1)\n\n## Limitations and alternatives\n\n**Sparsity and noise.** Per-cell data can be as shallow as about 1,000 unique reads, producing sparse datasets that motivate machine-learning imputation[7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0270043); Drop-ChIP data are sparse enough that thousands of cells are required for good clustering.[1](https://link.springer.com/article/10.1186/s43556-020-00009-w) Low-level non-specific antibody binding pulls down off-target sites, and the problem worsens in small-input experiments where on-target epitope is scarce.[2](https://doi.org/10.1038/nbt.3383) In Tn5-based workflows, the stochastic orientation of Tn5-A/B tagmentation means only half of fragments are successfully amplified and sequenced, and even light formaldehyde fixation can degrade single-cell data quality.[17](https://doi.org/10.1186/s13059-025-03661-z) Tn5 activity is attenuated in non-accessible chromatin and intrinsically biased toward open regions, so scChIL-seq and CoBATCH performed well for active marks but were not optimal for repressive marks in fixed samples.[4](https://genome.cshlp.org/content/31/10/1831) Across the family, antibody affinity largely determines reaction efficacy[18](https://www.mdpi.com/1422-0067/22/16/8809), and many methods lack commercial kits, requiring custom-made enzymes.[19](https://pmc.ncbi.nlm.nih.gov/articles/PMC11668300/)\n\n**Alternatives.** scATAC-seq profiles accessibility rather than specific marks; its data are binary (mostly 0 or 1 reads per locus).[20](https://pmc.ncbi.nlm.nih.gov/articles/PMC9352558/) Bulk CUT&Tag is more sensitive than ChIP-seq, works from few cells, and avoids crosslinking-associated epitope masking.[18](https://www.mdpi.com/1422-0067/22/16/8809) Comparisons with scCUT&RUN and scCUT&Tag indicate that Tn5-based methods usually offer higher throughput at the expense of unique fragments per cell.[21](https://www.nature.com/articles/s41592-025-02847-4) Recent multimodal work jointly detects [DNA methylation](https://www.edgepedia.org/dna-methylation) and histone modifications (H3K36me3, H3K27me3, H3K9me3) in single cells[21](https://www.nature.com/articles/s41592-025-02847-4), and MAbID jointly measures six chromatin epitopes in single cells.[15](https://doi.org/10.1038/s41592-023-02090-9)\n\n## References\n\n1. [Profiling chromatin regulatory landscape: insights into the development of ChIP-seq and ATAC-seq](https://link.springer.com/article/10.1186/s43556-020-00009-w)\n2. [Assaf Rotem and colleagues (2015). Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnology.](https://doi.org/10.1038/nbt.3383)\n3. [Kevin Grosselin and colleagues (2019). High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics.](https://doi.org/10.1038/s41588-019-0424-9)\n4. [Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing (iscChIC-seq)](https://genome.cshlp.org/content/31/10/1831)\n5. [Derek H. Janssens and colleagues (2023). Scalable single-cell profiling of chromatin modifications with sciCUT&Tag. Nature Protocols.](https://doi.org/10.1038/s41596-023-00905-9)\n6. [Wai Lim Ku and colleagues (2019). Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nature Methods.](https://doi.org/10.1038/s41592-019-0361-7)\n7. [Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0270043)\n8. [Shanshan Ai and colleagues (2019). Profiling chromatin states using single-cell itChIP-seq. Nature Cell Biology.](https://doi.org/10.1038/s41556-019-0383-5)\n9. [Manfred Schmid, Thérèse Durussel, Ulrich K. Laemmli (2004). ChIC and ChEC. Molecular Cell.](https://doi.org/10.1016/j.molcel.2004.09.007)\n10. [Angela R. Wu, Stephen R. Quake (2015). Microfluidics Technologies for Low Cell Number Chromatin Immunoprecipitation. Cold Spring Harbor Protocols.](https://doi.org/10.1101/pdb.prot084996)\n11. [Darren A. Cusanovich and colleagues (2015). Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science.](https://doi.org/10.1126/science.aab1601)\n12. [Qianhao Wang and colleagues (2019). CoBATCH for High-Throughput Single-Cell Epigenomic Profiling. Molecular Cell.](https://doi.org/10.1016/j.molcel.2019.07.015)\n13. [Wai Lim Ku and colleagues (2021). Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing. Genome Research.](https://doi.org/10.1101/gr.260893.120)\n14. [Steven J. Wu and colleagues (2021). Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nature Biotechnology.](https://doi.org/10.1038/s41587-021-00865-z)\n15. [Silke J. A. Lochs and colleagues (2023). Combinatorial single-cell profiling of major chromatin types with MAbID. Nature Methods.](https://doi.org/10.1038/s41592-023-02090-9)\n16. [Min Liu and colleagues (2025). Genome-coverage single-cell histone modifications for embryo lineage tracing. Nature.](https://doi.org/10.1038/s41586-025-08656-1)\n17. [Jingchun Ma and colleagues (2025). IT-scC&T-seq streamlines scalable, parallel profiling of protein–DNA interactions in single cells. Genome biology.](https://doi.org/10.1186/s13059-025-03661-z)\n18. [Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease](https://www.mdpi.com/1422-0067/22/16/8809)\n19. [Progress in multifactorial single-cell chromatin profiling methods](https://pmc.ncbi.nlm.nih.gov/articles/PMC11668300/)\n20. [Single-Cell Analysis of the Transcriptome and Epigenome](https://pmc.ncbi.nlm.nih.gov/articles/PMC9352558/)\n21. [Single-cell multi-omic detection of DNA methylation and histone modifications reconstructs the dynamics of epigenomic maintenance](https://www.nature.com/articles/s41592-025-02847-4)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › RNA and gene regulation › Transcription and gene regulation › Chromatin-linked gene regulation*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/single-cell-chip-seq", "markdown_url": "https://www.edgepedia.org/single-cell-chip-seq.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Single-cell ChIP-seq\", Edgepedia (EdgeChat), https://www.edgepedia.org/single-cell-chip-seq. Edgepedia Community License 1.0.", "credit_md": "\"[Single-cell ChIP-seq](https://www.edgepedia.org/single-cell-chip-seq)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/single-cell-chip-seq](https://www.edgepedia.org/single-cell-chip-seq). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Single-cell ChIP-seq\", Edgepedia (EdgeChat), https://www.edgepedia.org/single-cell-chip-seq. Edgepedia Community License 1.0.", "speakable": "Single-cell ChIP-seq is a family of genomics methods that profiles histone modifications or transcription factor binding in individual cells, first reported as Drop-ChIP in 2015." }